A data science process can make or break a team. Indeed, we see time and time again that many of the reasons behind data science project failures are not technical in nature but rather stem from process-related issues. Simply throwing compute power and PhDs at the problem doesn’t work. Rather, […]Read more
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
A data science life cycle is an iterative set of data science steps you take to deliver a project or analysis. Because every data science project and team are different, every specific data science life cycle is different. However, most data science projects tend to flow through the same general […]Read more
It’s an understatement that great leadership is challenging and rare. And leading data science teams has unique challenges: While this topic might seem most relevant to management and executives, remember that leadership is not a title. Rather, I encourage everyone reading this post to assess these points and to upskill […]Read more
Ironically, data science teams that are so intensely focused on model measurement often don’t measure their own project performance which is problematic because… …But wait! Data scientists measure all sorts of metrics. Of course, they will closely monitor data science metrics and KPIs such as RMSE, F1 scores, or correlation […]Read more
The Domino Data Science Life Cycle is a modern life cycle approach. Domino Data Lab, a Silicon Valley vendor that provides a data science platform, crafted its data science project life cycle framework in a 2017 whitepaper. The paper wraps its life cycle around goals, challenges, diagnoses, system recommendations, and role […]Read more