7 Best Machine Learning Workflow and Pipeline Orchestration Tools

Author(s): Eryk Lewinson

Originally published on Towards AI.

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Building impactful machine learning projects relies on much more than selecting the best algorithm for the job. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines. These pipelines cover the entire lifecycle of an ML project, from data ingestion and preprocessing, to model training, evaluation, and deployment.

Adopted from https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/well-architected-machine-learning-lifecycle.html

In this article, we will first briefly explain what ML workflows and pipelines are. Then, we will provide an overview of the best tools currently available on the market. Some of these were developed by big tech companies such as Google, Netflix, and Airbnb. Each of these tools is used by hundreds of companies (tech and non-tech!) around the world to streamline their data and ML pipelines.

By the end of this article, you will be able to identify the key characteristics of each of the selected orchestration tools and pick the one that should suit your use case!

Before exploring the tools, we first need to explain the difference between ML workflows and pipelines.

A machine learning workflow refers to the sequence of steps or tasks involved in the entire process of building an ML model. A typical… Read the full blog for free on Medium.

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Published via Towards AI