“Organizational agility is about learning about the market and changing to provide what it wants”.
This is the opening statement by Neil Killick at the recently concluded Dev Day 2015.
“Agile is ordering tapas till you’re full and not ordering a 10-course meal”
Neil opens with the quote which refers to ordering small chunks of food called “tapas” and sharing it with everyone else. So essentially, we would keep eating with tapas until we are full, rather than ordering a 10-course meal. This he says, is agile. In developing software, we come up with grand plans on how to achieve and build something and then commit to it. However, we should be agile in doing smaller things so that in the event there is a change of plans, we are open to mixing things up a bit as we go along. It’s important we work in small batches so we can throw away and prioritize.
He then shares an image of a bullet train. After learning about how bad our train service is, he shares 4 qualities of the bullet train service
How did they achieve this? They built dedicated lines for high-speed rail so they aren’t slowed down by slower trains. There are virtually no road crossings and the tracks are specially designed with dedicated drivers and support staff. So basically, the moral of the story was not to make a train faster or more reliable, but rather, to create a network for high-speed trains.
Most organizations and environments will ask their employees to simply work faster and more predictable rather than building a network to support people from delivering software quickly. The key here would be to let one team focus on one thing without worrying about multiple things.
The big question: Is agile estimation really helping us evolve into more agile developers? It’s better than the tools we used before but it’s still predictive, which means we are still trying to figure out how expensive software is which result in the project and its members going over-budget. He refers to the train again where we know that in developed countries the trains always show up in 3-5 minutes, in a software world we don’t know things for certain so we are predictive. He gives poker planning as another example which is predictive and organized for speed so we quickly try to get it out of the way and any points used in it is becomes abstract. Planning poker is also focused only on cost, not on when the customer will receive the benefit of the product. It’s also develop-centric, we don’t concern ourselves with marketing and legal issues. It also encourages a false security because again it doesn’t give us an accurate ETA, rather it only helps us make predictions which may not be accurate.
These, Neil states, are just some of the problems with agile planning.
Enter Slicing heuristics. Rather than estimating how long things take, one simply breaks down goals into small chunks and measures how long each chunk will take. You can then learn from that and improve the entire process going forward. It’s aimed at improving predictability and reduce time to release products. Now the keyword is heuristic, is that it’s not an approach that’s not perfect.
The 5 steps are:
What do we learn from all this?
The work might take a longer time, it may be straight up unpredictable, or simply unpredictable within a work type, or a certain work type may become retired. If we reduce asking people for too many things then we can get more things done. In a software company the queues are invisible. Unlike in a coffee shop, adding another person to the task simply makes the queue longer, thus delaying your coffee.
If we have a variable cycle time then we can try being more consistent in the way work is defined, keeping work-in-progress consistent and minimizing distractions. This also results in reduced stress.
So slicing heuristics lead to:
However, this will only work if you try it. Neil went on to explain that you shouldn’t go ahead and implement this in all in one go, rather, you can take a few bits and pieces from this theory and try it out.
“If you’re not experimenting, then you’re committing to something that may not work in your context.”
Neil ends his session with a few chosen words of wisdom.
“Always focus on individuals and interactions over processes and tools”
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What kicked off in 2011 as a friendly gaming event has now developed into a fully-fledged gaming tournament. With the goal of promoting team building, leadership, and planning, the Virtusa
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Module 1: Introduction to Machine Learning
This module introduces machine learning and discussed how algorithms and languages are used.
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· Introduction to machine learning algorithms
· Introduction to machine learning languages
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Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.
· Azure machine learning overview
· Introduction to Azure machine learning studio
· Developing and hosting Azure machine learning applications
Module 3: Managing Datasets
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Module 4: Building Azure Machine Learning Models
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Module 5: Using Azure Machine Learning Models
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· Deploying and publishing models
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Module 6: Using Cognitive Services
This module introduces the cognitive services APIs for text and image processing to create a recommendation application, and describes the use of neural networks with Azure machine learning.
· Cognitive services overview
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· Recommending products
Feel free to contact us for any inquiries
uditha bandara – 0716092918
All Day (Wednesday)
ANC education ,310 R A De Mel Mw, Colombo 03 00300
Blue Chip Training0716092918
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The real value comes from taking an idea from concept through to execution using Lean tactics and working under high pressure with the best startups.
26 (Friday) 5:00 pm - 28 (Sunday) 8:00 pm
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