Great posting by Pete Johnson on a serverless application, “30K Page Views for $0.21: A Serverless Story”. Its interesting to read a read world application that leverages AWS Lambda functions coupled with S3 storage, providing real data on the extremely low charges for the number of page views. If nothing else, read the “What I’ve Found is Cool (and Not) About Lambda” section🙂
- The team members started following processes well.
- There was more interaction and collaboration between SD, QA, Product Owners and Product Managers.
Software available here
It may sound either an odd question, or a sensible question depending on your kanban board today, but a lot of people seem to ignore the swim lane benefits, and just go with the column standard kanban board.
Given kanban is lean, if your using an electronic board as a lot of us are these days, its probably worth playing with a few of the options in your chosen kanban product to see if you can gain further agility though surfacing of cards. Swim lanes can clearly call out the progress within work stream or features within the application delivery that previously were hidden within the column order.
Further, options for surfacing further data on the story card itself may aid in clarity of the micro/macro picture e.g usage of tags on the card. More interestingly tracking staleness per column, which could aid in understanding flow and WIP.
- Ensure you have an ordered backlog of business value stories that have A/C, adhere to a sensible story format.
- Drop estimation
- Engineers get 20? points for completing a story (Done).
- A Done stories that has defects raised before moving to production, loose a point per defect
- Whether you have an automated Continuous Deployment process or manual process, the team member who performed the release to production gets 5 points.
- Testers get 10? points per defect
- Put in place a team wide scoreboard
- Each month, the greatest scorer get some small prize e.g. voucher etc
- Retrospect on the above and improve e.g. maybe the business assign business value points to stories to enhance the ordered backlog?
Net out, you’d hope that velocity has improved, and defects have dropped. Which at the end of the day is what we all want, whether we are management or software engineers, we want to know we are making a difference, and delivering value.
O’Reilly’s article, “The dynamic forces shaping AI”, provide an interesting read on the historical landscape of data within AI, and the possible future paths AI may influence Data, Talent, Compute and Algo.
fact crucial in creating the most valuable and impactful AI systems is usually credited to Google
Stating the obvious, clearly there is opportunity for talent🙂
it employs smart contracts and machine learning to enable banks to offer a vast array of financial products – or roll out new ones in days. It is 100% future-proof.
Further, and equally of interest is the compliance angle that should facilitate uptake due to the MiFid and Basel regulations:
When it comes to compliance, every transaction is reported in real time so banks can determine their exact financial position at any moment. These tools are in the core of Vault OS, making implementation of capital-adequacy standards such as Basel III automatic.
Its also interesting to see the Machine Learning angle, not surprising given the Google staffers influence on the solution:
Our machine learning team builds systems that analyse banking behaviour to better guide people with their financial lives.
Of no surprise, VaultOS sounds like its already being tested by banks:
The start-up has been working with about ten banks, Taylor said, at least one of which would be starting a trial using the new system in August. He expects the system to be up-and-running within about a year.
Mining of Massive DataSets, although a few years old, offers an interesting read on stream-clustering algorithm. PDF of the book is available here, coupled with a web site from the authors, and slides on many chapters.
Following on from the previous blog posting, if you venture down the Bag-Of-Words road of feature engineering, you may then want to begin looking at clustering.