There’s no single data science project documentation recipe. Rather, your documentation needs will vary by project, team, organization, and industry. And it’s not just about producing data science model documentation. Instead, think broader and ask – What do I need to document and why? Once you’ve thought this through and […]Read more
A data science strategic plan isn’t just about data. Rather, it is a comprehensive plan that defines how you build and maintain an ecosystem that delivers sustainable value from your data science investments.
Developing this plan is not easy. So why bother?
Well, without one, you could be steering down a path without knowing where it is going…Read more
What do flight attendants, surgical teams, and successful data science project managers have in common? They all use a checklist. Why? Although every surgery, project, and flight is different, each has a repeatable pattern. And checklists can help remind us of each step and major consideration. Don’t reinvent the wheel. […]Read more
Machine Learning Model Operations is a multi-disciplinary field that is gaining traction as organizations are realizing that there’s a lot more work even after model deployment. Rather, the model maintenance work often requires more effort than the development and deployment of a model. Hence, the world of Machine Learning Operations […]Read more
The importance of AI project management, or more specifically, managing AI projects, is highlighted by a survey showing that 85% of AI projects do not deliver on their promises to business. In other words, as noted by Forbes, most AI projects fail. An AI project is different than a typical […]Read more
This post will explore managing analytics projects by focusing on three questions: What is the difference between a data scientist and a data analyst? What is the difference between analytics projects and data science projects? What project management / project coordination framework might be helpful to manage analytics projects? Project […]Read more
The Boston Dynamics humanoid robot tripping over a curtain and tumbling off stage? A prediction model that tips a teen’s parents off to her pregnancy? A music recommendation engine that suggests Coldplay? Yup. There are just too many big data, data science, and data analytics failure examples to cover in […]Read more
Machine Learning Project Management is of growing importance If you are leading machine learning projects, you have probably noticed (or soon will notice) that machine learning project management is of growing importance and that it would be extremely helpful to have a repeatable agile process framework that helps to ensure […]Read more
So are there new emerging approaches that are data science native? Microsoft’s Team Data Science Process (TDSP), Domino Data Lab’s Data Science Life Cycle, and the Data Science Process Alliance’s Data Driven Scrum (DDS) are approaches that are both data science native and agile. There are pros and cons specific to each approach but they share some fundamental principles.Read more
Can you mix and match elements of multiple project management approaches? Of course! This post explores two such general hybrid approaches for data science:agile-waterfall and research and development.Read more