Building Successful Communities of Practice · Emily Webber · 2016
A good overview for those interested in starting or improving a practice. Using skill benchmarks as a way to pair individuals with a learning parter or buddy is a great concept.
Key Insights
A Community of Practice is a group of people who share a concern or passion for something they do and learn how to do it better through regular interaction. These communities support organisational learning, accelerate professional development, and enable effective knowledge sharing and management. They also improve communication, help build better practices, and break down silos. Perhaps most importantly, they make people happier: which in turn supports hiring and retention through a more motivated workforce. There are three major ways people learn: from reading and training, from doing, and from others. Knowledge itself is created through the transformation of learning experience, following a cycle that moves from concrete experience to reflective observation, then to abstract conceptualisation, and finally to active experimentation where learners try out what they've discovered. Communities of Practice support a range of learning activities that create a more rounded curriculum. They create connections that enable shadowing and peer learning, can build their own training curriculum, and provide a natural venue for self-initiated learning through networking, reading, writing and speaking. They offer a place to share ideas and receive support, encourage small experiments and short projects, and foster questions, retrospectives and feedback loops. By bringing together a diverse group of people who share the same challenges but have different experiences, they create a wider pool of knowledge for problem-solving. As a community matures, it naturally moves from simply sharing knowledge to actively solving shared problems together. Happier people do better work and are more motivated; research suggests happiness makes people around 12% more productive. Daniel Pink identifies three key drivers of motivation: autonomy (the desire to direct our own lives), mastery (the urge to get better at something that matters), and purpose (the yearning to serve something larger than ourselves). Communities of Practice can support all three. Community leadership involves building, sustaining and developing the community; managing people and dynamics; providing support and facilitation; informing, advising or coaching members; defining professional direction and standards; and representing community members within and outside the organisation. Leaders should create an aspirational, achievable and easy-to-understand vision, using SMART goals where helpful and involving the community in creating them. A practical example might be increasing knowledge within permanent staff to reduce reliance on contractors. When considering membership, look for people who share similar roles, environments, work goals, challenges, or learning goals: particularly learning relevant to day-to-day work. Members should be aligned on purpose, learning, challenges and teaching. The community should collectively decide how to spend its time, allowing for building social connections, learning as a group, talking through problems and building better practices, sharing outside the community, and creating community improvements. Skills confidence mapping helps communities understand their collective capabilities. The Dreyfus model of adult skill acquisition identifies five stages: novice (follows rules), advanced beginner (recognises patterns), competent (chooses a perspective), proficient (responds to situations), and expert (writes their own rules). A complementary model is ShuHaRi, which describes protection (following the master), breakaway (learning principles and integrating them into practice), and creating (developing personal approaches adapted to individual circumstances). When you know everyone's skills, you can match people with learning buddies; if everyone is weak in one area, you might bring in an external trainer. Members typically fall into categories: core, active, occasional, peripheral, and outside. The key elements of a self-sustaining practice are leadership (where members are comfortable taking the lead), engaged membership focused on the vision, continuous learning and practice improvement, member-led skills development, and visibility and support from advocates outside the community.
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Operationalising Machine Learning: An Interview Study
Shankar, Garcia, Hellerstein, Parameswaran 2022. (View Paper → )
Organisations rely on machine learning engineers (MLEs) to operationalise ML, i.e., deploy and maintain ML pipelines in production. The process of operationalising ML, or MLOps, consists of a continual loop of i) data collection and labelling, ii) experimentation to improve ML performance, iii) evaluation throughout a multi-staged deployment process, and (iv) monitoring of performance drops in production. When considered together, these responsibilities seem staggering-how does anyone do MLOps, what are the unaddressed challenges, and what are the implications for tool builders?
Successful machine learning operations centre around three critical variables:
- Velocity (rapid prototyping and iteration)
- Validation (early testing and error detection)
- Versioning (maintaining multiple model versions to minimise downtime).
Rather than viewing the oft-cited 90% failure rate of ML models as problematic, the authors reframe it as a natural consequence of experimentation … most attempts shouldn't reach production, and effective teams excel at quickly identifying promising approaches.
In practice, successful ML teams focus on data quality over model complexity, collaborating closely with domain experts to validate ideas early. They implement multi-stage deployment processes (test, dev, canary, shadow) and tie ML metrics directly to business outcomes. Small, incremental changes using configuration files rather than code changes help maintain stability while enabling experimentation.
For production reliability, organisations establish regular retraining cadences, maintain fallback model versions, add rule-based guardrails to prevent obvious errors, and implement automated validation checks. Organisational practices like on-call rotations, centralised bug tracking, and defined service level objectives further support sustainable MLOps. Throughout all stages, the focus remains on high-value experiments rather than running many in parallel for the sake of keeping resources busy.
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Book Highlights
Unfortunately, most software products never have a description. Instead, all they have is a shopping list of features. A shopping bag filled with flour, sugar, milk, and eggs is not the same thing as a cake. It's only a cake when all the steps of the recipe have been followed, and the result looks, smells, and tastes substantially like the known characteristics of a cake. Alan Cooper · The Inmates Are Running the Asylum
The ancient Egyptians were among those to maintain a dual-calendar system, with their civic calendar comprising the three 120-day seasons based on the flooding of the Nile. This only gives a total of 360 days for a year, so an additional five ‘epagomenal’ days were added after the twelfth month to bring the total up to 36529 – a practice copied in a number of cultures. James Vincent · Beyond Measure
Overall, JTBD is not a single method: it’s a lens, a way of seeing. Kim Kalbach et al · The Jobs to Be Done Playbook
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Quotes & Tweets
When your primary goal is to be liked, you can't take risks. You can't disagree. You can't push boundaries. You become a prisoner of other people's expectations. Shane Parrish
If you write down a problem clearly and specifically, you have already solved half of it. Kidlin’s Law