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Successful Digital Vision Starts at the Top

Business change is tough.   Just try it at Cloud speed, and you’ll know what I mean.

That said, digital business transformation is reshaping companies and industries around the world, at a rapid rate.

If you don’t cross the Cloud chasm, and learn how to play in the new digital economy,  you might just get left behind.

Sadly, not every executive has a digital vision.

That’s a big deal because the pattern here is that successful digital business transformation starts at the top of the company.  And it starts with digital vision.

But just having a digital vision is not enough.

It has to be a shared transformative digital vision.   Not a mandate, but a shared digital vision from the top, that’s led and made real by the people in the middle and lower levels.

In the book, Leading Digital: Turning Technology into Business Transformation, George Westerman, Didier Bonnet, and Andrew McAfee, share how successful companies and executives drive digital business transformation through shared transformative digital visions.

Employees Don’t Always Get the WHY, WHAT, or HOW of Digital Business Transformation

You need a digital vision at the top.   Otherwise, it’s like pushing rocks uphill.  Worse, not everybody will be in the game, or know what position they play, or even how to play the game.

Via Leading Digital: Turning Technology into Business Transformation:

“The changes being wrought through digital transformation are real.  Yet, even when leaders see the digital threats and opportunity, employees may need to be convinced.  Many employees feel they are paid to do a job, not to change that job.  And they have lived through big initiatives in the past that failed to turn into reality.  To many, digital transformations is either irrelevant or just another passing fad.  Still other people may not understand how the change affects their jobs or how they might make the transition.”

Only Senior Executives Can Create a Compelling Vision of the Future

Digital business transformation must be led.   Senior executives are in the right position to create a compelling future all up, and communicate it across the board.

Via Leading Digital: Turning Technology into Business Transformation:

“Our research shows that successful digital transformation starts at the top of the company.  Only the senior-most executives can create a compelling vision of the future and communicate it throughout the organization.  Then people in the middle and lower levels can make the vision a reality.  Managers can redesign process, workers can start to work differently, and everyone can identify new ways to meet the vision.  This kind of change doesn't happen through simple mandate.  It must be led.

Among the companies we studied, none have created true digital transformation through a bottom-up approach.  Some executives have changed their parts of the business--for example, product design and supply chain at Nike--but the executives stopped at the boundaries of their business units.  Changing part of your business is not enough.  Often, the real benefits of transformation come from seeing potential synergies across silos and then creating conditions through which everyone can unlock that value.  Only senior executives are positioned to drive this kind of boundary-spanning change.”

Digital Masters Have a Shared Digital Vision (While Others Do Not)

As the business landscape is reshaping, you are either a disruptor or the disrupted.  The Digital Masters that are creating the disruption in their business and in their industries have shared digital visions, and re-imagine their business for a mobile-first, Cloud-first world, and a new digital economy.

Via Leading Digital: Turning Technology into Business Transformation:

“So how prevalent is digital vision? In our global survey of 431 executives in 391 companies, only 42 percent said that their senior executive had a digital vision.  Only 35 percent said the vision was shared among senior and middle managers.  These numbers are surprisingly low, given the rapid rate at which digital transformation is reshaping companies and industries.  But the low overall numbers mask an important distinction.  Digital Masters have a shared digital vision, while others do not. 

Among the Digital Masters that we surveyed, 82 percent agreed that their senior leaders shared a common vision of digital transformation, and 71 percent said it was shared between senior and middle managers.  The picture is quite different for firms outside our Digital Masters category, where less than 30 percent said their senior leaders had a shared digital vision and only 17 percent said the shared vision extended to middle management.”

Digital Vision is Not Enough (You Need a Transformative Digital Vision)

It’s bad enough that many executives don’t have a shared digital vision.   But what makes it worse, is that even fewer have a transformative digital vision, which is the key to success in the digital frontier.

Via Leading Digital: Turning Technology into Business Transformation:

“But having a shared digital vision is not quite enough.  Many organizations fail to capture the full potential of digital technologies because their leaders lack a truly transformative vision of the digital future.  On average, only 31 percent of our respondents said that they had a vision which represented radical change, and 41 percent said their vision crossed internal organizational units.  Digital Masters were far more transformative in their vision, with two-thirds agreeing they had a radical vision, and 82 percent agreeing their vision crossed organizational silos.  Meanwhile, nonmasters were far less transformative in their visions.”

Where there is no vision, the businesses perish.

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Categories: Architecture, Programming

Should I Focus On Getting Certifications?

Making the Complex Simple - John Sonmez - Thu, 03/05/2015 - 17:00

In this video I respond to an email asking about whether or not certifications are something to focus on. I explain that some certifications are meaningless, but if you think it will help you get a job then you should go for it!

The post Should I Focus On Getting Certifications? appeared first on Simple Programmer.

Categories: Programming

Python: scikit-learn/lda: Extracting topics from QCon talk abstracts

Mark Needham - Thu, 03/05/2015 - 09:52

Following on from Rik van Bruggen’s blog post on a QCon graph he’s created ahead of this week’s conference, I was curious whether we could extract any interesting relationships between talks based on their abstracts.

Talks are already grouped by their hosting track but there’s likely to be some overlap in topics even for talks on different tracks.
I therefore wanted to extract topics and connect each talk to the topic that describes it best.

My first attempt was following an example which uses Non-Negative Matrix factorization which worked very well for extracting topics but didn’t seem to provide an obvious way to work out how to link those topics to individual talks.

Instead I ended up looking at the lda library which uses Latent Dirichlet Allocation and allowed me to achieve both goals.

I already had some code to run TF/IDF over each of the talks so I thought I’d be able to feed the matrix output from that into the LDA function. This is what I started with:

import csv
 
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import NMF
from collections import defaultdict
from bs4 import BeautifulSoup, NavigableString
from soupselect import select
 
def uri_to_file_name(uri):
    return uri.replace("/", "-")
 
sessions = {}
with open("data/sessions.csv", "r") as sessions_file:
    reader = csv.reader(sessions_file, delimiter = ",")
    reader.next() # header
    for row in reader:
        session_id = int(row[0])
        filename = "data/sessions/" + uri_to_file_name(row[4])
        page = open(filename).read()
        soup = BeautifulSoup(page)
        abstract = select(soup, "div.brenham-main-content p")
        if abstract:
            sessions[session_id] = {"abstract" : abstract[0].text, "title": row[3] }
        else:
            abstract = select(soup, "div.pane-content p")
            sessions[session_id] = {"abstract" : abstract[0].text, "title": row[3] }
 
corpus = []
titles = []
for id, session in sorted(sessions.iteritems(), key=lambda t: int(t[0])):
    corpus.append(session["abstract"])
    titles.append(session["title"])
 
n_topics = 15
n_top_words = 50
n_features = 6000
 
vectorizer = TfidfVectorizer(analyzer='word', ngram_range=(1,1), min_df = 0, stop_words = 'english')
matrix =  vectorizer.fit_transform(corpus)
feature_names = vectorizer.get_feature_names()
 
import lda
import numpy as np
 
vocab = feature_names
 
model = lda.LDA(n_topics=20, n_iter=500, random_state=1)
model.fit(matrix)
topic_word = model.topic_word_
n_top_words = 20
 
for i, topic_dist in enumerate(topic_word):
    topic_words = np.array(vocab)[np.argsort(topic_dist)][:-n_top_words:-1]
    print('Topic {}: {}'.format(i, ' '.join(topic_words)))

And if we run it?

Topic 0: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 1: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 2: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 3: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 4: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 5: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 6: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 7: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 8: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 9: 10 faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 10: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 11: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 12: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 13: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 14: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 15: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 16: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 17: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 18: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure
Topic 19: zoosk faced exposing expression external externally extra extraordinary extreme extremes face facebook facilitates faster factor factors fail failed failure

As you can see, every topic has the same set of words which isn’t what we want. Let’s switch out our TF/IDF vectorizer for a simpler count based one:

vectorizer = CountVectorizer(analyzer='word', ngram_range=(1,1), min_df = 0, stop_words = 'english')

The rest of the code stays the same and these are the topics that get extracted:

Topic 0: time people company did writing real way used let cassandra soundcloud successful know web organization audio lives swift stuck
Topic 1: process development delivery platform developer continuous testing rapidly deployment implementing release demonstrate paas advice hard light predictable radically introduce
Topic 2: way open space kind people change meetings ll lead powerful practice times everyday simple qconlondon organization unconference track extraordinary
Topic 3: apache apis processing open spark distributed leading making environments solr cases brooklyn components existing ingestion contributing data target evolved
Topic 4: management million effective cost halo gameplay player billion ad catastrophic store microsoft final music influence information launch research purchased
Topic 5: product look like use talk problems working analysis projects challenges 2011 functionality useful spread business deep inside happens sensemaker
Topic 6: ll computers started principles free focus face smaller atlas control uses products avoid computing ground billions mean volume consistently
Topic 7: code end users developers just application way line apps mobile features sites hours issues applications write faster game better
Topic 8: ve development teams use things world like time learned lessons think methods multiple story say customer developer experiences organisations
Topic 9: software building docker built challenges monitoring gilt application discuss solution decision talk download source center critical decisions bintray customers
Topic 10: years infrastructure tools language different service lot devops talk adoption scala popular clojure advantages introduced effectively looking wasn includes
Topic 11: high does latency session requirements functional performance real world questions problem second engineering patterns gravity explain discuss expected time
Topic 12: business make build technology technologies help trying developers parts want interfaces small best centres implementations critical moo databases going
Topic 13: need design systems large driven scale software applications slow protocol change needs approach gets new contracts solutions complicated distributed
Topic 14: architecture service micro architectures increasing talk microservices order market value values new present presents services scalable trading practices today
Topic 15: java using fast robovm lmax ios presentation really jvm native best exchange azul hardware started project slowdowns goal bring
Topic 16: data services using traditional create ways support uk large user person complex systems production impact art organizations accessing mirage
Topic 17: agile team experience don work doing processes based key reach extra defined pressure machines nightmare practices learn goals guidance
Topic 18: internet new devices programming things iot big number deliver day connected performing growing got state thing provided times automated
Topic 19: cloud including deploy session api government security culture software type attack techniques environment digital secure microservice better creation interaction

Some of the groupings seem to make sense e.g. Topic 11 contains words related to high performance code with low latency; Topic 15 covers Java, the JVM and other related words; but others are more difficult to decipher

e.g. both Topic 14 and Topic 19 talk about micro services but the latter mentions ‘government’ and ‘security’ so perhaps the talks linked to that topic come at micro services from a different angle altogether.

Next let’s see which topics a talk is most likely to be about. We’ll look at the first ten:

doc_topic = model.doc_topic_
for i in range(0, 10):
    print("{} (top topic: {})".format(titles[i], doc_topic[i].argmax()))
    print(doc_topic[i].argsort()[::-1][:3])
 
To the Moon (top topic: 8)
[ 8  0 11]
Evolutionary Architecture and Micro-Services - A Match Enabled by Continuous Delivery (top topic: 14)
[14 19 16]
How SoundCloud uses Cassandra (top topic: 0)
[0 6 5]
DevOps and the Need for Speed (top topic: 18)
[18  5 16]
Neuro-diversity and agile (top topic: 7)
[17  7  2]
Java 8 in Anger (top topic: 7)
[ 7 15 12]
APIs that Change Lifestyles (top topic: 9)
[ 9  6 19]
Elasticsearch powers the Citizen Advice Bureau (CAB) to monitor trends in society before they become issues (top topic: 16)
[16 12 19]
Architecture Open Space (top topic: 2)
[ 2 19 18]
Don’t let Data Gravity crush your infrastructure (top topic: 11)
[11 16  3]

So our third talk on the list ‘How SoundCloud uses Cassandra’ does end up being tagged with topic 0 which mentions SoundCloud so that’s good!

Topic 0: time people company did writing real way used let cassandra soundcloud successful know web organization audio lives swift stuck

It’s next two topics are 5 & 6 which contain the following words…

Topic 5: product look like use talk problems working analysis projects challenges 2011 functionality useful spread business deep inside happens sensemaker
Topic 6: ll computers started principles free focus face smaller atlas control uses products avoid computing ground billions mean volume consistently

…which are not as intuitive. What about Java 8 in Anger? It’s been tagged with topics 7, 15 and 12:

Topic 7: code end users developers just application way line apps mobile features sites hours issues applications write faster game better
Topic 15: java using fast robovm lmax ios presentation really jvm native best exchange azul hardware started project slowdowns goal bring
Topic 12: business make build technology technologies help trying developers parts want interfaces small best centres implementations critical moo databases going

15 makes sense since that mentions Java and perhaps 12 and 7 do as well as they both mention developers.

So while the topics pulled out are not horrendous I don’t think they’re particularly useful yet either. These are some of the areas I need to do some more research around:

  • How do you measure the success of topic modelling? I’ve been eyeballing the output of the algorithm but I imagine there’s an automated way to do that.
  • How do you determine the right number of topics? I found an article written by Christophe Grainger which explains a way of doing that which I need to look at in more detail.
  • It feels like I would be able to pull out better topics if I had an ontology of computer science/software words and then ran the words through that to derive topics.
  • Another approach suggested by Michael is to find the most popular words using the CountVectorizer and tag talks with those instead.

If you have any suggestions let me know. The full code is on github if you want to play around with it.

Categories: Programming

Lightweight software architecture - an interview with Fog Creek

Coding the Architecture - Simon Brown - Thu, 03/05/2015 - 08:48

I recently did a short interview with the folks from Fog Creek (creators of Stack Exchange, Trello, FogBugz, etc) about lightweight approaches to software architecture, my book and so on. The entire interview is only about 8 minutes in length and you can watch/listen/read it on the Fog Creek blog.

Read more...

Categories: Architecture

Dogma Driven Development

Actively Lazy - Wed, 03/04/2015 - 21:24

We really are an arrogant, opinionated bunch, aren’t we? We work in an industry where there aren’t any right answers. We pretend what we do is computer “science”. When in reality, its more art than science. It certainly isn’t engineering. Engineering suggests an underlying physics, mathematical models of how the world works. Is there a mathematical model of how to build software at scale? No. Do we understand the difference between what makes good software and bad software? No. Are there papers with published proofs of whether this idea or that idea has any observable difference on written software, as practised by companies the world over? No. It turns out this is a difficult field: software is weird stuff. And yet we work in an industry full of close-minded people, convinced that their way is The One True Way. It’s not science, its basically art. Our industry is dominated by fashion.

Which language we work in is fashion: should we use Ruby, or Node.js or maybe Clojure. Hey Go seems pretty cool. By which I mean “I read about it on the internet, and I’d quite like to put it on my CV so can I please f*** up your million pound project in a big experiment of whether I can figure out all the nuances of the language faster than the project can de-rail?”

If it’s not the language we’re using, its architectural patterns. The dogma attached to REST. Jesus H Christ. It’s just a bunch of HTTP requests, no need to get so picky! For a while it was SOA. Then that became the old legacy thing, so now it’s all micro-services, which are totally different. Definitely. I read it on the internet, it must be true.

Everyone has their opinions. Christ, we’ve got our opinions. Thousands of blogs and wankers on twitter telling you what they think about the world (exactly like this one) As if one person’s observations are useful for anything more than being able to replicate their past success, should you ever by mistake find yourself on their timeline from about six weeks ago.

For example: I wrote a post recently about pairing, and some fine specimen of internet based humanity felt the need to tell me that people who need to pair are an embarrassment to the profession, that we should find another line of work. Hahaha I know, don’t read the comments. Especially when it’s in reply to something you wrote. But seriously now, is it necessary to share your close minded ignorance with the world?

I shouldn’t get worked up about some asshat on the internet. But it’s not just some asshat on the internet. There are hundreds of thousands of these asshats with their closed minds and dogmatic views on the world. And not just asshats spouting off on the internet, but getting paid to build the software that increasingly runs all our lives. When will we admit that we have no idea what we’re doing. The only way to get better is to learn as many tools and techniques as we can and hopefully, along the way, we’ll learn when to apply which techniques and when not to.

For example, I’ve worked with some people that don’t get TDD. Ok, fine – some people just aren’t “test infected”. And a couple of guys that really would rather gut me and fry my liver for dinner than pair with me. Do I feel the need to evangelise to them as though I’ve just found God? No. Does it offend me that they don’t follow my religion? No. Do I feel the need to suicide bomb their project? No. Its your call. Its your funeral. When I have proof that my way is The One True Way and yours is a sham, you can damn well bet I’ll be force feeding it to you. But given that ain’t gonna happen: I think we’re all pretty safe. If you don’t wanna pair, you put your headphones on and disappear into your silent reverie. Those of us that like pairing will pair, those of us that don’t, won’t. I’m fine with that.

The trouble is, in this farcical echo chamber of an industry, where the lessons of 40 years ago still haven’t been learnt properly. Where we keep repeating the mistakes of 20 years ago. Of 10 years ago. Of 5 years ago. Of 2 years ago. Of last week. For Christ’s sake people, can we not just learn a little of what’s gone before? All we have is mindless opinion, presented as fact. Everyone’s out to flog you their new shiny products, or whatever bullshit service they’re offering this week. No, sorry, it’s all utter bollocks. We know less about building decent software now than we did 40 years ago. It’s just now we build a massive amount more of it. And it’s even more shit than it ever was. Only now, now we have those crazy bastards that otherwise would stand on street corners telling me that Jesus would save me if only I would let him; but now they’re selling me scrum master training or some other snake oil.

All of this is unfortunately entirely indistinguishable from reasoned debate, so for youngsters entering the industry they have no way to know that its just a bunch of wankers arguing which colour to paint this new square wheel they invented. Until after a few years they become as jaded and cynical as the rest of us and decide to take advantage of all the other dumb fools out there. They find their little niche, their little way of making the world a little bit worse but themselves a little bit richer. And so the cycle repeats. Fashion begets fashion. Opinion begets opinion.

There aren’t any right answers in creating software. I know what I’ve found works some of the time. I get paid to put into practice what I know. I hope you do, too. But we’ve all had a different set of experiences which means we often don’t agree on what works and what doesn’t. But this is all we have. The plural of anecdote is not data.

All we have is individual judgement, borne out of individual experience. There is no grand unified theory of Correct Software Development. The best we can hope to do is learn from each other and try as many different approaches as possible. Try and fail safely and often. The more techniques you’ve tried the better the chance you can find the right technique at the right time.

Call it craftsmanship if you like. Call it art if you like. But it certainly isn’t science. And I don’t know about you, but it’s a very long time since I saw any engineering round these parts.


Categories: Programming, Testing & QA

10 Reasons to Consider a Multi-Model Database

This is a guest post by Nikhil Palekar, Solutions Architect, FoundationDB.

The proliferation of NoSQL databases is a response to the needs of modern applications. Still, not all data can be shoehorned into a particular NoSQL model, which is why so many different database options exist in the market. As a result, organizations are now facing serious database bloat within their infrastructure.

But a new class of database engine recently has emerged that can address the business needs of each of those applications and use cases without also requiring the enterprise to maintain separate systems, software licenses, developers, and administrators.

These multi-model databases can provide a single back end that exposes multiple data models to the applications it supports. In that way, multi-model databases eliminate fragmentation and provide a consistent, well-understood backend that supports many different products and applications. The benefits to the organization are extensive, but some of the most significant benefits include:

1. Consolidation
Categories: Architecture

The Use, Misuse, and Abuse of Complexity and Complex

Herding Cats - Glen Alleman - Wed, 03/04/2015 - 16:54

Our world is complex and becoming more complex all the time. We are connected and in turn driven by a complex web of interacting technology and processes. These interacting technologies and processes are implemented by information and communication technologies that are themselves complex. It is difficult to apply such a broad topic as complexity to the equally broad topic of developing software systems or even broader topic of engineered systems.

Measuring complexity in engineered systems is a highly varying concept.1

The result of these complex systems many times creates complexity. But care is needed is tossing around words like complex and complexity. If these systems are in fact Engineered, rather than simply left to emerge on their own, we can apply some principles to control the unwanted complexity of these complex systems.

First some definitions. These are not the touchy feely definitions found in places like Cynefin where the units of measure of complex, complexity, and chaos, are no where to be found. Cynefin was developed in the context of management and organizational strategy by David Snowden. We're interested in the system complexity of things, the people who build them, and the environments where they are deployed. But this also means measuring complex and the complexity in units meaningful to the decision makers. These units must somehow be connected to the cost, schedule, and probability of success for those paying for the work.

Complexity has turned out to be very difficult to define. The dozens of definitions that have been offered all fall short in one respect or another, classifying something as complex which we intuitively would see as simple, or denying an obviously complex phenomenon the label of complexity. Moreover, these definitions are either only applicable to a very restricted domain, such as computer algorithms or genomes, or so vague as to be almost meaningless. (From Principia Cybernetica) 

Some more background about complex systems and their complexity4

  • A System is a set of interacting components - whether human-made, naturally-occurring, or a combination of both.
  • By "interact", it means the exchange of physical force, energy, mass flow, or information, such that one component can change the state of another component. Or for software systems, the exchange of information, state knowledge, or impact an outcome of other component.
  • The technologies or natures of these systems may be mechanical, chemical, electronic, biological, informational (software), or combinations of these or others.
  • The behavior of a system can include "emergent" aspects that are not a characteristic of any individual component, but arise from their combination.
  • Emergent properties can be valuable (e.g., delivery of new services) or undesired (e.g., dangerous or unstable).
  • The behavior of a system is often not easily predicted from the behavior of its individual components, and may also be more complex.
  • The complexity of human-engineered systems is growing, in response to demands for increased sophistication, in response to market or government competition, and enabled by technologies.
  • It has become relatively easy to construct systems which cannot be so readily understood. 

What is complexity then?

The words chaos and complexity have been used to mean disorder and complications for centuries. Only in the last thirty years have they been used to refer to mathematical and scientific bodies of knowledge.6

Often something is called complex when we can't fully understand its structure or behavior. It is uncertain, unpredictable, complicated, or just difficult to understand.  Complexity is often described as the inability of a human mind to grasp the whole of a complex problem and predict the outcome as Subjective Complexity.5

The complex and complexity I'm speaking about are for Engineered Systems, products and services used by organizations, but engineered for their use.  Their use can be considered complex, even create complexity and many times emergent. But the Cynefin approach to complex is ethereal, without the principled basis found in engineering and more importantly Systems Engineering.3 This appears to be why agilest toss around the terms found in Cynefin, since engineering of the software is not a core principle of agile development, rather emergent design and architecture is the basis of the Agile Manifesto.

Here's an example of the engineering side of complex systems, from Dr. Sheard's presentation "Twelve Roles and Three Types of Systems Engineering," NASA Goddard Space Flight Center, Systems Engineering Seminar, Complexity and Systems Engineering, April 5, 2011,

Screen Shot 2015-03-01 at 9.02.58 AM

Before applying these definitions to problems for developing software, there is more work to do.

In complex systems there are entities that participate in the system2

  • The technical system being designed and built.
  • The socio-technical systems that are building the systems - the project team or production team.
  • The technological Environment into which the system will be inserted when the system is complete and deployed. The socio-political system related to the technological environment. This is generally the interaction of the system stakeholders with the resulting system.
  • The subjective human experience when thinking about, designing, or using the system, called Cognition.

Cynefin does not make these distinctions, instead separates the system into Complex, Complicated, Chaotic, and Obvious, without distinguishing to which engineered portion of the system these are applicable.

So when we hear about Complex Adaptive Systems in the absence of a domain and the mathematics of such a system, care is needed. It is likely no actionable information will be available in units of measure meaningful to the decision makers to help them make decisions. Just a set words.

References

1 "Complexity Types: from Science to Systems Engineering," Sarah Sheard, ; Mostashari, Ali, Proceedings of the 21th Annual International Symposium of the International Council of Systems Engineering

2 "Systems Engineering in Complexity Context," Sarah Sheard, Proceedings of the 23nd Annual International Symposium of the International Council of Systems Engineering

3 Systems Engineering Principles and Practices, 2nd Edition, Alexander Kossiakoff William N. Sweet Samuel J. Seymour Steven M. Biemer, John Wiley & Sons.

4 The Challenge of Complex Systems, INCOSE Crossroads of America Chapter.

5 “On Systems architects and systems architecting: some thoughts on explaining the art and science of system architecting” H. G. Sillitto, Proceedings of INCOSE IS 2009, Singapore, 20-23 July. 

6 Practical Applications of Complexity Theory for Systems Engineers, Sarah Sheard, Proceedings of the 15th Annual International Symposium of the International Council on Systems Engineering

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Categories: Project Management

Failure is not an Option

Herding Cats - Glen Alleman - Wed, 03/04/2015 - 04:30

LastTitan_vandenberg_fThere is a popular noton in the agile world, and some business guru's that failure is encouraged as part of the learning process. What is not stated is when and where this failure can take place.

The picture to the left is the flight of the last Titan IV launch vehicle. I was outside the SCIF, but got to see everything up the 2nd stage separation.

The Martin Company’s launch vehicle built a five-decade legacy goes back to the earliest rockets designed and built in the United States. The Intercontinental Ballistic Missile (ICBM) program; Project Gemini, NASA’s 2nd human spaceflight program; Mars Viking landers; Voyager deep space probes; communications and reconnaissance satellites—all of these programs and more relied on the Titan for a safe and dependable launch. 

The final version flew when the program was retired after delivering National Reconnaissance Office payload to orbit on October 19, 2005. A total 368 Titans were flown, with capabilities ranging from Earth reconnaissance and military and civil communications to human and robotic exploration.

In this domain, failure is not an option. Many would correctly say, failures were found before use. And that is correct, Design, Development, Test, and Evaluation (DDT&E) is the basis of must work when commanded to do so when commanded to do so.

In domains without the needed capability that must perform on demand - fail fast and fail often may be applicable. 

Choose domain before suggesting a process idea is applicable

Related articles Software Engineering is a Verb Self-Organization Quote of the Day
Categories: Project Management

The House of Lean, or Is That The House of Quality?

House of Lean

Lean is the systematic removal of waste within a process, known as muda in Japanese. Much of our understanding of lean as a process improvement tool is a reflection of the Toyota Production System (TPS). In the parlance of TPS, the container for lean ideas and concepts is the House of Quality. Larman and Leffingwell and others have modified the metaphor to the House of Quality that Toyota leveraged to the House of Lean (HoL) to focus on the flow of work. The focus on flow makes lean concepts an important component for scaling Agile in frameworks like SAFe. Even without the rebranding, the core of lean provides a focus how work is done, which improves the flow or smoothness of work. The focus on flow reduces variance (know as muri in Japanese). Lean identifies variance from by comparing the outcome of work to development standards to expose existing problems so waste can be reduced. I am assuming that once waste is exposed that you do something about it. The concept of the House of Lean or the House of Quality has many variants. All that I have studied are valuable, however we will use the Larman/Leffingwell version of the HoL as this version seems to have found fertile ground in the software development field. The House of Lean we will explore consists of six components. They are:

  1. A ceiling or roof, which represents the goal. The goal is typically defined as delivering the maximum value in the shortest sustainable lead-time while providing the highest value and quality to customers, people and society.
  2. Pillar one (typically shown on the left side of the hours or lean) represents respect for people. Work of any sort is built around people. People need to be empowered to assess and evolve how they work within the standards of the organization. Agile reflects this respect for people in the principles of self-organization.
  3. Pillar two (typically shown on the right side of the hours or lean) represents continuous improvement. Continuous improvement, often termed Kiazen or good change, is the relentless removal of inefficiencies. Inefficiencies (waste) detract or keep an organization from attaining the goal.
  4. Between the two pillars are housed:
    1. Delivery practices that reflect the techniques used to deploy lean, such as great engineers, cadence, cross-functional teams, team rooms and process visualization. In the SAFe version of the HoL, the 14 Lean Principles often subsume a discussion of delivery practices. The inclusion in the HoL of specific types of lean and Agile delivery practices helps practitionersto clearly see the linkage between theory in the 14 Lean Principles and the two pillars of lean and the practice of developing software.
    2. 14 Lean Principles (See my interview with Don Reinertsen on SPaMCAST and my review of his book, The Principles of Product Development Flow: Second Generation Lean Product Development). The 14 Lean Principles espoused by Reinertsen are a mechanism to remove waste from the flow of work. In the original TPS, versions of the HoQ this was reflect by an element called Reduction of Mudas (reduction of wastes). Reinertsen provides a set of principles that are more easily translated to software development and maintenance.
  5. A base which represents lean/Agile leadership. Many of the representations of the HoL/HoQ call the base management support. Leadership is far stronger than support. Leadership reflects a management that is trained in lean and Agile AND believes in lean and Agile. Belief is reflected in decisions that are philosophically in sync with the 12 Agile Principles and the 14 Principles of Product Development.

The House of Lean is a convenient container to hold the concepts and ideas that began as the Toyota Production System and have evolved as tools to be less manufacturing-oriented. The evolution of the HoL to include concepts and techniques familiar to Agile practitioners have not only helped to reduce muda and muri, but also is a useful tool to help reduce overhead when scaling Agile using frameworks like SAFe.


Categories: Process Management

Off-Time: It’s OK to Do Nothing

NOOP.NL - Jurgen Appelo - Tue, 03/03/2015 - 18:37

On my final trip last year, I had been looking forward to run in Rio de Janeiro, along Ipanema Beach and Copacabana. But British Airways lost my luggage. I had no running gear and no time to purchase alternative shoes and clothes. I felt a bit sad and disappointed.

The post Off-Time: It’s OK to Do Nothing appeared first on NOOP.NL.

Categories: Project Management

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Categories: Architecture

The Cost Estimating Problem

Herding Cats - Glen Alleman - Tue, 03/03/2015 - 17:32

Screen Shot 2015-03-02 at 12.32.11 PM

In probability theory, de Finetti's theorem† explains why exchangeable observations are conditionally independent given some latent variable to which an epistemic probability distribution would then be assigned. It is named in honor of Bruno de Finetti.

It states that an exchangeable sequence of Bernoulli random variables is a "mixture" of independent and identically distributed (i.i.d.) Bernoulli random variables – while the individual variables of the exchangeable sequence are not themselves i.i.d., only exchangeable, there is an underlying family of i.i.d. random variables.

Thus, while observations need not be i.i.d. for a sequence to be exchangeable, there are underlying, generally unobservable, quantities which are i.i.d. – exchangeable sequences are (not necessarily i.i.d.) mixtures of i.i.d. sequences.

All of this actually has importance. When we start to assess risks using probabilistic process based on statistical processes, we need to be very careful to understand the underlying mathematics.

There are four approach to saying what we mean when we say “probability”

  1. Logical – weak implications
  2. Propensity – physical properties
  3. Frequency – attributed to sequences of observations
  4. Subjective – personal opinion

† Finetti’s Theorem is at the heart of estimating random variables. Cost is a random variable, like schedule durations and the technical outcomes from the effort based on cost and schedule. In statistical assessment of cost and schedule, Frequentist (counting) statistics is one approach. The second is Bayesian inference (used in most science). The exchangeability of the random variables is critical to building times series of sampled data from the project to forecast future performance.

The Bigger Problem in Estimating

Screen Shot 2015-03-02 at 12.38.40 PM

Exchangability and de Finetti's Theorem 

Finite Exchangeable Sequences

Categories: Project Management

101 Proven Practices for Focus

“Lack of direction, not lack of time, is the problem. We all have twenty-four hour days.” -- Zig Ziglar

Here is my collection of 101 Proven Practices for Focus.   It still needs work to improve it, but I wanted to shared it, as is, because focus is one of the most important skills we can develop for work and life.

Focus is the backbone of personal effectiveness, personal development, productivity, time management, leadership skills, and just about anything that matters.   Focus is a key ingredient to helping us achieve the things we set out to do, and to learn the things we need to learn.

Without focus, we can’t achieve great results.

I have a very healthy respect for the power of focus to amplify impact, to create amazing breakthroughs, and to make things happen.

The Power of Focus

Long ago one of my most impactful mentors said that focus is what separates the best from the rest.  In all of his experience, what exceptional people had, that others did not, was focus.

Here are a few relevant definitions of focus:
A main purpose or interest.
A center of interest or activity.
Close or narrow attention; concentration.

I think of focus simply as  the skill or ability to direct and hold our attention.

Focus is a Skill

Too many people think of focus as something either you are good at, or you are not.  It’s just like delayed gratification.

Focus is a skill you can build.

Focus is actually a skill and you can develop it.   In fact, you can develop it quite a bit.  For example, I helped a colleague get themselves off of their ADD medication by learning some new ways to retrain their brain.   It turned out that the medication only helped so much, the side effects sucked, and in the end, what they really needed was coping mechanisms for their mind, to better direct and hold their attention.

Here’s the surprise, though.  You can actually learn how to direct your attention very quickly.  Simply ask new questions.  You can direct your attention by asking questions.   If you want to change your focus, change the question.

101 Proven Practices at a Glance

Here is a list of the 101 Proven Practices for Focus:

  1. Align  your focus and your values
  2. Ask new questions to change your focus
  3. Ask yourself, “What are you rushing through for?”
  4. Beware of random, intermittent rewards
  5. Bite off what you can chew
  6. Breathe
  7. Capture all of your ideas in one place
  8. Capture all of your To-Dos all in one place
  9. Carry the good forward
  10. Change your environment
  11. Change your physiology
  12. Choose one project or one thing to focus on
  13. Choose to do it
  14. Clear away all distractions
  15. Clear away external distractions
  16. Clear away internal distractions
  17. Close your distractions
  18. Consolidate and batch your tasks
  19. Create routines to help you focus
  20. Decide to finish it
  21. Delay gratification
  22. Develop a routine
  23. Develop an effective startup routine
  24. Develop an effective shutdown routine
  25. Develop effective email routines
  26. Develop effective renewal activities
  27. Develop effective social media routines
  28. Direct your attention with skill
  29. Do less, focus more
  30. Do now what you could put off until later
  31. Do things you enjoy focusing on
  32. Do worst things first
  33. Don’t chase every interesting idea
  34. Edit later
  35. Exercise your body
  36. Exercise your mind
  37. Expand your attention span
  38. Find a way to refocus
  39. Find the best time to do your routine tasks
  40. Find your flow
  41. Finish what you started
  42. Focus on what you control
  43. Force yourself to focus
  44. Get clear on what you want
  45. Give it the time and attention it deserves
  46. Have a time and place for things
  47. Hold a clear picture in your mind of what you want to accomplish
  48. Keep it simple
  49. Keep your energy up
  50. Know the tests for success
  51. Know what’s on your plate
  52. Know your limits
  53. Know your personal patterns
  54. Know your priorities
  55. Learn to say no – to yourself and others
  56. Limit your starts and stops
  57. Limit your task switching
  58. Link it to good feelings
  59. Make it easy to pick back up where you left off
  60. Make it relentless
  61. Make it work, then make it right
  62. Master your mindset
  63. Multi-Task with skill
  64. Music everywhere
  65. Narrow your focus
  66. Pair up
  67. Pick up where you left off
  68. Practice meditation
  69. Put the focus on something bigger than yourself
  70. Rate your focus each day
  71. Reduce friction
  72. Reduce open work
  73. Reward yourself along the way
  74. See it, do it
  75. Set a time frame for focus 
  76. Set goals
  77. Set goals with hard deadlines
  78. Set mini-goals
  79. Set quantity limits
  80. Set time limits
  81. Shelve things you aren’t actively working on
  82. Single Task
  83. Spend your attention with skill
  84. Start with WHY
  85. Stop starting new projects
  86. Take breaks
  87. Take care of the basics
  88. Use lists to avoid getting overwhelmed or overloaded
  89. Use metaphors
  90. Use Sprints to scope your focus
  91. Use the Rule of Three
  92. Use verbal cues
  93. Use visual cues
  94. Visualize your performance
  95. Wake up at the same time each day
  96. Wiggle your toes – it’s a fast way to bring yourself back to the present
  97. Write down your goals
  98. Write down your steps
  99. Write down your tasks
  100. Write down your thoughts
  101. Work when you are most comfortable

When you go through the 101 Proven Practices for Focus, don’t expect it to be perfect.  It’s a work in progress.   Some of the practices for focus need to be fleshed out better.   There is also some duplication and overlap, as I re-organize the list and find better ways to group and label ideas.

In the future, I’m going to revamp this collection to have some more precision, better naming, and some links to relevant quotes, and some science where possible.   There is a lot more relevant science that explains why some of these techniques work, and why some work so well.

What’s important is that you find the practices that resonate for you, and the things that you can actually practice.

Getting Started

You might find that from all the practices, only one or two really resonate, or help you change your game.   And, that’s great.   The idea of having a large list to select from is that it’s more to choose from.  The bigger your toolbox, the more you can choose the right tool for the job.  If you only have a hammer, then everything looks like a nail.

If you don’t consider yourself an expert in focus, that’s fine.  Everybody has to start somewhere.  In fact, you might even use one of the practices to help you get better:  Rate your focus each day.

Simply rate yourself, on a scale of 1-10, where 10 is awesome and 1 means you’re a squirrel with a sugar high, dazed and confused, and chasing all the shiny objects that come into site.   And then see if your focus improves over the course of a week.

If you adopt just one practice, try either Align  your focus and your values or Ask new questions to change your focus.  

Feel Free to Share It With Friends

At the bottom of the 101 Proven Practices for Focus, you’ll find the standard sharing buttons for social media to make it easier to share.

Share it with friends, family, your world, the world.

The ability to focus is really a challenge for a lot of people.   The answer to improve your attention and focus is through proven practices, techniques, and skill building.  Too many people hope the answer lies in a pill, but pills don’t teach you skills.

Even if you struggle a bit in the beginning, remind yourself that growth feels awkward.   You' will get better with practice.  Practice deliberately.  In fact, the side benefit of focusing on improving your focus, is, well, you guessed it 
 you’ll improve your focus.

What we focus on expands, and the more we focus our attention, and apply deliberate practice, the deeper our ability to focus will grow.

Grow your focus with skill.

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Categories: Architecture, Programming

A product manager's perfection....

Xebia Blog - Tue, 03/03/2015 - 15:59

is achieved not there are no more features to add, but when there are no more features to take away. -- Antoine de Saint Exupéry

Not only was Antoine a brilliant writer, philosopher and pilot (well arguably since he crashed in the Mediterranean) but most of all he had a sharp mind about engineering, and I frequent quote him when I train product owners, product managers or in general product companies, about what makes a good product great. I also tell them their most important word in their vocabulary is "no". But the question then becomes, what is the criteria to say "yes"?

Typically we will look at the value of a feature and use different ways to prioritise and rank different features, break them down to their minimal proposition and get the team going. But what if you already have a product? and it’s rate of development is slowing. Features have been stacked on each other for years or even decades, and it’s become more and more difficult for the teams to wade through the proverbial swamp the code has become?

Too many features

Too many features

Turns out there are a number of criteria that you can follow:

1.) Working software, means it’s actually being used.

Though it may sound obvious, it’s not that easy to figure out. I was once part of a team that had to rebuild a rather large piece (read huge) of software for an air traffic control system. The managers ensured us that every functionality was a must keep, but the cost would have been prohibitory high.

One of the functions of the system was a record and replay mode for legal purposes. It basically registers all events throughout the system to serve as evidence that picture compilers would be accountable, or at least verifiable. One of our engineers had the bright insight that we could catalogue this data anonymously to figure out which functions were used and which were not.

Turned out the Standish Group was pretty right in their claims that 80% of the software is never used. Carving that out was met with fierce resistance, but it was easier to convince management (and users) with data, than with gut.

Another upside? we also knew what functions they were using a lot, and figured out how to improve those substantially.

2.) The cost of platforms

Yippee we got it running on a gazillion platforms! and boy do we have a reach, the marketing guys are going frenzy. Even if is the right choice at the time, you need to revisit this assumption all the time, and be prepared to clean up! This is often looked upon as a disinvestment: “we spent so much money on finally getting Blackberry working” or “It’s so cost effective that we can also offer it on platform XYZ”.

In the web world it’s often the number of browsers we support, but for larger software systems it is more often operating systems, database versions or even hardware. For one customer we would refurbish hardware systems, simply because it was cheaper than moving on to a more modern machine.

Key take away: If the platform is deprecated, remove it entirely from the codebase, it will bog the team down and you need their speed to respond to an ever increasing pace of new platforms.

3.) Old strategies

Every market and every product company pivots at least every few years (or dies). Focus shifts from consumer groups, to type of clients, type of delivery, shift to service or something else which is novel, hip and most of all profitable. Code bases tend to have a certain inertia. The larger the product, the bigger the inertia, and before you know it there a are tons of features in their that are far less valuable in the new situation. Cutting away perfectly good features is always painful but at some point you end up with the toolbars of Microsoft Word. Nice features, but complete overkill for the average user.

4.) The cause and effect trap

When people are faced with an issue they tend to focus on fixing the issue as it manifests itself. It's hard for our brain to think in problems, it tries to think in solutions. There is an excellent blog post here that provides a powerful method to overcome this phenomena by asking five times "why".

  • "We need the system to automatically export account details at the end of the day."
  • "Why?"
  • "So we can enter the records into the finance system"
  • "So it sounds like the real problem is getting the data into the finance system, not exporting it. Exporting just complicates the issue. Let's implement a data feed that automatically feeds the data to the finance system"

The hard job is to continuously keep evaluating your features, and remove those who are no longer valuable. It may seem like your throwing away good code, but ultimately it is not the largest product that survives, but the one that is able to adapt fast enough to the changing market. (Freely after Darwin)

 

The Virtue of Purgatory in Software Development

From the Editor of Methods & Tools - Tue, 03/03/2015 - 14:51
Having some decade of experience in software development behind me, I had the time to accumulate a lot of mistakes. One of the recurring patterns in these failures was the ambition to solve code issues too quickly. This was especially the case when the problem was related to code that I wrote, which made me feel responsible for the situation. Naturally, I had also often think that my code couldn’t be bad and somebody must have changed it after I deliver it, but this is another story ;O) When you detect ...

Four Tips for Managing Performance in Agile Teams

I’ve been talking with clients recently about their managers’ and HR’s transition to agile. I hear this common question: “How do we manage performance of the people on our agile teams?”

  1. Reframe “manage performance” to “career development.” People on agile teams don’t need a manager to manage their performance. If they are retrospecting at reasonable intervals, they will inspect-and-adapt to work better together. Well, they will if managers don’t interfere with their work by creating experts or moving people off project teams.
  2. The manager creates a trusting relationship with each person on the team. That means having a one-on-one weekly or bi-weekly with each person. At the one-on-one, the manager provides tips for feedback and offers coaching.  (If the person needs it or wants it from the manager.) The person might want to know where else he or she can receive coaching. The manager removes obstacles if the person has them. They discuss career development.
  3. When managers discuss career development, each person needs to see an accurate view of the value they bring to the organization. That means each person has to know how to give and receive feedback. They each have to know how to ask for and accept coaching. The manager provides meta-feedback and meta-coaching.
  4. If you, as a manager, meet with each person at least once every two weeks, no problem is a problem for too long. The people in the team have another person to discuss issues with. The manager sees the system and can change it to help the people on the team.

Now, what does this mean for raises?

I like to separate the raise from the feedback. People need feedback all the time, not just once a year. That’s why I like weekly or biweekly one-on-ones. Feedback isn’t just from the manager to the employee; it’s two-way feedback. If people have trouble working in the current environment, the managers might have a better chance to change it than an employee who is not a manager.

What about merit raises? This is tricky. So many managers and HR people continue to think one person is a star. No, on well-functioning agile teams, the team is the star—not individuals. You have options:

  • Make sure you pay each person at parity. This might not be trivial. You need expertise criteria for each job level.
  • When it comes to merit raises, provide a pot of money for the team and ask them to distribute it.
  • Distribute the merit money to each person equally. Explain that you are doing this, so people provide feedback to each other.
  • Here’s something radical: When people think they are ready for a raise or another level, have a discussion with the team. Let the team vote on it.

Managers have to not get in the way when it comes to “performance management.” The people on the team are adult humans. They somehow muddle through the rest of their lives, successfully providing and receiving feedback. They know the worth of things outside work. It’s just inside work that we keep salary secret.

It might not fit for you to have open-book salaries. On the other hand, how much do your managers and HR do that interferes with a team? You have to be careful about this.

If you reward individuals and ask people to work together as a team, how long do you think they will work together as a team? I don’t know the answer to that question.

Long ago, my managers asked me to be a “team player.”  One guy got a huge raise—and I didn’t, although I had saved his tush several times—I stopped working as a “team” member. I got my big raise the following year. (Year!) This incongruent approach is why people leave organizations—when the stated way “we work here” is not congruent with the stated goals: agile and self-organizing teams.

What do you want? Managers and HR to manage people? Or, to lead people using servant leadership, and let the teams solve their problems and manage their own performance?

If teams don’t know how to improve, that’s one thing. But, I bet your teams do know how to improve. You don’t have to manage their performance. You need to create an environment in which people can do their best work—that’s the manager’s job and the secret to “managing performance.”

Related posts:

 

Categories: Project Management

Quote of the Day

Herding Cats - Glen Alleman - Mon, 03/02/2015 - 20:09

GalileoChristina_edGalileo Galilei, Letter to the Grand Duchess Christina of Tuscany (1615)

.... Considering the force exerted by logical deductions, they may ascertain that it is not in the power of the professors of demonstrative sciences to change their opinions at will and apply themselves first to one side and then to the other.

There is a great difference between commanding a mathematician or a philosopher and influencing a lawyer or a merchant, for demonstrated conclusions about things in nature or in the heavens cannot be changed with the same facility as opinions about what is or is not lawful in a contract, bargain, or bill of exchange.

If those suggesting we abandon the principles of Microeconomics of Software Development (decision making in the presence of scarcity, abundance, and economic value)†, requiring that decisions made today with their impacts on future outcomes, do so without probabilistic knowledge of those impacts can be done in the absence of estimating those impacts - think again.

It Just Ain't So

† Software Project Effort Estimation: Foundations and Best Practice Guidelines for Success, May 7, 2014 by Adam Trendowicz and Ross Jeffery

Categories: Project Management

Change Your Life With this Free Blogging Course

Making the Complex Simple - John Sonmez - Mon, 03/02/2015 - 17:00

Around the mid December 2014, I decided to launch a completely free blogging course that would be delivered via email over 3 weeks. I had no idea how popular and successful that blogging course would turn out to be. (Here’s a good book to check out) At the time of writing this post, almost 3,000 […]

The post Change Your Life With this Free Blogging Course appeared first on Simple Programmer.

Categories: Programming

Tutum, first impressions

Xebia Blog - Mon, 03/02/2015 - 16:40

Tutum is a platform to build, run and manage your docker containers. After shortly playing with it some time ago, I decided to take a bit more serious look at it this time. This article describes first impressions of using this platform, more specifically looking at it from a continuous delivery perspective.

The web interface

First thing to notice is the clean and simple web interface. Basically there are two main sections, which are services and nodes. The services view lists the services or containers you have deployed with status information and two buttons, one to stop (or start) and one to terminate the container, which means to throw it away.

You can drill down to a specific service, which provides you with more detailed information per service. The detail page provides you information about the containers, a slider to scale up and down, endpoints, logging, some metrics for monitoring and more .

Screen Shot 2015-02-23 at 22.49.33

The second view is a list of nodes. The list contains the VM's on which containers can be deployed. Again with two simple buttons to start/stop and to terminate the node. For each node it displays useful information about the current status, where it runs, and how many containers are deployed on it.

The node page also allows you to drill down to get more information on a specific node.  The screenshot below shows some metrics in fancy graphs for a node, which can potentially be used to impress your boss.

Screen Shot 2015-02-23 at 23.07.30

 

Creating a new node

You’ll need a node to deploy containers on it. In the node view you see two big green buttons. One states: “Launch new node cluster”. This will bring up a form with currently four popular providers Amazon, Digital Ocean, Microsoft Azure and Softlayer. If you have linked your account(s) in the settings you can select that provider from a dropdown box. It only takes a few clicks to get a node up and running. In fact you create a node cluster, which allows you to easily scale up or down by adding or removing nodes from the cluster.

You also have an option to ‘Bring you own node’. This allows you to add your own Ubuntu Linux systems as nodes to Tutum. You need to install an agent onto your system and open up a firewall port to make your node available to Tutum. Again very easy and straight forward.

Creating a new service

Once you have created a node, you maybe want to do something with it. Tumtum provides jumpstart images with popular types of services for storage, cacheing, queueing and more, providing for example MongoDB, Elasticsearch or Tomcat. Using a wizard it takes only four steps to get a particular service up and running.

Besides the jumpstart images that Tutum provides, you can also search public repositories for your image of choice. Eventually you would like to have your own images running your homegrown software. You can upload your image to a Tutum private registry. You can either pull it from Docker Hub or upload your local images directly to Tutum.

Automating

We all know real (wo)men (and automated processes) don’t use GUI’s. Tutum provides a nice and extensive command line interface for both Linux and Mac. I installed it using brew on my MBP and seconds later I was logged in and doing all kinds of cool stuff with the command line.

Screen Shot 2015-02-24 at 22.23.30

The cli is actually doing rest calls, so you can skip the cli all together and talk HTTP directly to a REST API, or if it pleases you, you can use the python API to create scripts that are actually maintainable. You can pretty much automate all management of your nodes, containers, and services using the API, which is a must have in this era of continuous everything.

A simple deployment example

So let's say we've build a new version of our software on our build server. Now we want to get this software deployed to do some integration testing, or if you feeling lucky just drop it straight into production.

build the docker image

tutum build -t test/myimage .

upload the image to Tutum registry

tutum image push <image_id>

create the service

tutum service create <image_id>

run it on a node

tutum service run -p <port> -n <name> <image_id>

That's it. Of course there are lots of options to play with, for example deployment strategy, set memory, auto starting etc. But the above steps are enough to get your image build, deployed and run. Most time I had to spend was waiting while uploading my image using the flaky-but-expensive hotel wifi.

Conclusion for now

Tutum is clean, simple and just works. I’m impressed with ease and speed you can get your containers up and running. It takes only minutes to get from zero to running using the jumpstart services, or even your own containers. Although they still call it beta, everything I did just worked, and without the need to read through lots of complex documentation. The web interface is self explanatory and the REST API or cli provides everything you need to integrate Tutum in your build pipeline, so you can get your new features in production with automation speed.

I'm wondering how challenging managing would be at a scale of hundreds of nodes and even more containers, when using the web interface. You'd need a meta-overview or aggregate view or something. But then again, you have a very nice API to

Python: scikit-learn – Training a classifier with non numeric features

Mark Needham - Mon, 03/02/2015 - 08:48

Following on from my previous posts on training a classifier to pick out the speaker in sentences of HIMYM transcripts the next thing to do was train a random forest of decision trees to see how that fared.

I’ve used scikit-learn for this before so I decided to use that. However, before building a random forest I wanted to check that I could build an equivalent decision tree.

I initially thought that scikit-learn’s DecisionTree classifier would take in data in the same format as nltk’s so I started out with the following code:

import json
import nltk
import collections
 
from himymutil.ml import pos_features
from sklearn import tree
from sklearn.cross_validation import train_test_split
 
with open("data/import/trained_sentences.json", "r") as json_file:
    json_data = json.load(json_file)
 
tagged_sents = []
for sentence in json_data:
    tagged_sents.append([(word["word"], word["speaker"]) for word in sentence["words"]])
 
featuresets = []
for tagged_sent in tagged_sents:
    untagged_sent = nltk.tag.untag(tagged_sent)
    sentence_pos = nltk.pos_tag(untagged_sent)
    for i, (word, tag) in enumerate(tagged_sent):
        featuresets.append((pos_features(untagged_sent, sentence_pos, i), tag) )
 
clf = tree.DecisionTreeClassifier()
 
train_data, test_data = train_test_split(featuresets, test_size=0.20, train_size=0.80)
 
>>> train_data[1]
({'word': u'your', 'word-pos': 'PRP$', 'next-word-pos': 'NN', 'prev-word-pos': 'VB', 'prev-word': u'throw', 'next-word': u'body'}, False)
 
>>> clf.fit([item[0] for item in train_data], [item[1] for item in train_data])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/sklearn/tree/tree.py", line 137, in fit
    X, = check_arrays(X, dtype=DTYPE, sparse_format="dense")
  File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/sklearn/utils/validation.py", line 281, in check_arrays
    array = np.asarray(array, dtype=dtype)
  File "/Users/markneedham/projects/neo4j-himym/himym/lib/python2.7/site-packages/numpy/core/numeric.py", line 460, in asarray
    return array(a, dtype, copy=False, order=order)
TypeError: float() argument must be a string or a number

In fact, the classifier can only deal with numeric features so we need to translate our features into that format using DictVectorizer.

from sklearn.feature_extraction import DictVectorizer
 
vec = DictVectorizer()
X = vec.fit_transform([item[0] for item in featuresets]).toarray()
 
>>> len(X)
13016
 
>>> len(X[0])
7302
 
>>> vec.get_feature_names()[10:15]
['next-word-pos=EX', 'next-word-pos=IN', 'next-word-pos=JJ', 'next-word-pos=JJR', 'next-word-pos=JJS']

We end up with one feature for every key/value combination that exists in featuresets.

I was initially confused about how to split up training and test data sets but it’s actually fairly easy – train_test_split allows us to pass in multiple lists which it splits along the same seam:

vec = DictVectorizer()
X = vec.fit_transform([item[0] for item in featuresets]).toarray()
Y = [item[1] for item in featuresets]
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.20, train_size=0.80)

Next we want to train the classifier which is a couple of lines of code:

clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, Y_train)

I wrote the following function to assess the classifier:

import collections
import nltk
 
def assess(text, predictions_actual):
    refsets = collections.defaultdict(set)
    testsets = collections.defaultdict(set)
    for i, (prediction, actual) in enumerate(predictions_actual):
        refsets[actual].add(i)
        testsets[prediction].add(i)
    speaker_precision = nltk.metrics.precision(refsets[True], testsets[True])
    speaker_recall = nltk.metrics.recall(refsets[True], testsets[True])
    non_speaker_precision = nltk.metrics.precision(refsets[False], testsets[False])
    non_speaker_recall = nltk.metrics.recall(refsets[False], testsets[False])
    return [text, speaker_precision, speaker_recall, non_speaker_precision, non_speaker_recall]

We can call it like so:

predictions = clf.predict(X_test)
assessment = assess("Decision Tree", zip(predictions, Y_test))
 
>>> assessment
['Decision Tree', 0.9459459459459459, 0.9210526315789473, 0.9970134395221503, 0.9980069755854509]

Those values are in the same ball park as we’ve seen with the nltk classifier so I’m happy it’s all wired up correctly.

The last thing I wanted to do was visualise the decision tree that had been created and the easiest way to do that is export the classifier to DOT format and then use graphviz to create an image:

with open("/tmp/decisionTree.dot", 'w') as file:
    tree.export_graphviz(clf, out_file = file, feature_names = vec.get_feature_names())
dot -Tpng /tmp/decisionTree.dot -o /tmp/decisionTree.png


The decision tree is quite a few levels deep so here’s part of it:

DecisionTreeSection

The full script is on github if you want to play around with it.

Categories: Programming