PyData Global 2022

One of the key questions in modern data science and machine learning, for businesses and practitioners alike, is how do you move machine learning projects from prototype and experiment to production as a repeatable process. In this workshop, we present a hands-on introduction to the landscape of production-grade tools, techniques, and workflows that bridge the gap between laptop data science and production ML workflows. Participants will learn how to take common machine learning models, such as those from scikit-learn, XGBoost, and Keras, and productionize them using Metaflow.

We’ll present a high-level overview of the 8 layers of the ML stack: data, compute, versioning, orchestration, software architecture, model operations, feature engineering, and model development. We’ll present a schematic as to which layers data scientists need to be thinking about and working with, and then introduce attendees to the tooling and workflow landscape. In doing so, we’ll present a widely applicable stack that provides the best possible user experience for data scientists, allowing them to focus on parts they like (modeling using their favorite off-the-shelf libraries) while providing robust built-in solutions for the foundational infrastructure.

You can find the companion repository for the workshop here: https://github.com/outerbounds/full-stack-ML-metaflow-tutorial

Session Outline

Lesson 1: Machine learning workflows and DAGs

This lesson will focus on building local machine learning workflows using Metaflow, although the high-level concepts taught will be applicable to any workflow orchestrator. Attendees will get a feel for writing flows and DAGs to define the steps in their workflows. We’ll also use DAG cards to visualize our ML workflows. This lesson will be local computation and in the next lesson, we’ll burst to the cloud.

Lesson 2: Bursting to the Cloud

In this lesson, we’ll see how we can move ML steps or entire workflows to the cloud from the comfort of our own IDE. In this case, we’ll be using AWS Batch compute resources, but the techniques are generalizable.

Lesson 3 (optional and time permitting): Integrating other tools into your ML pipelines

We’ll also see how to begin integrating other tools into our pipelines, such as dbt for data transformation, great expectations for data validation, Weights & Biases for experiment tracking, and Amazon Sagemaker for model deployment. Once again, the intention is not to tie us to any of these tools, but to use them to illustrate various aspects of the ML stack and to develop a workflow in which they can easily be switched out for other tools, depending on where you work and who you’re collaborating with.