Streamlining a machine learning project team

Description

Artificial intelligence is already helping many businesses become more responsive and competitive, but how do you move machine learning models efficiently from research to deployment at enterprise scale? It’s imperative to plan for deployment from day one, both in tool selection and in the feedback and development process.

As recently as a few years ago, data scientists were the people who played in a sandbox—when they came up with a useful model, it was thrown over the wall to another team that would reimplement it to put it into production. Those days are over now: there’s only one Git repo in the entire company, and everything you commit is essentially in production. But teams are still run as if data science is mainly about experimentation.

Sourav Day and Alex Ng share best practices for working in this new reality. Data scientists can still play in a sandbox but must do so in a way such that offers a turnkey solution to take models into production. Just as in DevOps, where people work at the intersection of development and operations, today people are working at the intersection of data science and software engineering and need to be integrated into the team with tools and support. Manifold developed the Lean AI process and the open source Orbyter package for Docker-first data science to help do just that.

Sourav and Alex explain how to streamline a machine learning project and help your engineers work as an an integrated part of your development and production teams.

Topics include:

• Understanding both the business problem and the data
• Containerized data science for cleaner workflows
• Data engineering as a core competency
• Building iterative data models to deliver value early
• Best practices for bookkeeping ML experiments
• Developing user trust in the data models
• Seamless deployment at production scale
• Observing and validating on-the-ground model use