Tools 67. Deepchecks Assess As machine learning finds its way into the mainstream, practices are maturing around automatically testing models, validating training data and observing model performance in production. Increasingly, these automated checks are being incorporated into continuous delivery pipelines or run against production models to detect drift and model performance. A number of tools with similar or overlapping capabilities have emerged to handle various steps in this process (Giskard and Evidently are also covered in this volume). Deepchecks is another of these tools that’s available as an open- source Python library and can be invoked from pipeline code through an extensive set of APIs. One unique feature is its ability to handle either tabular or image data with a module for language data currently in alpha release. At the moment, no single tool can handle the variety of tests and guardrails across the entire ML pipeline. We recommend assessing Deepchecks for your particular application niche. 68. Design token translation tools Assess Design tokens are a useful mechanism for defining standard elements in design systems. But, keeping those design elements consistent across media such as mobile apps or web frameworks is an increasingly formidable task. Design token translation tools simplify this problem by organizing and automating transformation from the token description (in YAML or JSON) into the code that actually controls rendering in a given medium such as CSS, React components or HTML. Style Dictionary is an open-source example that is widely used and integrates well into automated build pipelines, but there are also commercial alternatives such as Specify. 69. Devbox Assess Devbox provides an approachable interface for creating reproducible, per-project development environments leveraging the Nix package manager. Our teams use it to eliminate version and configuration mismatches in their development environments, and they like it for its ease of use. Devbox supports shell hooks, custom scripts and devcontainer.json generation for integration with VSCode. 70. Evidently Assess Evidently is an open-source Python tool designed to help build monitoring for machine learning models to guarantee their quality and stable production operations. It can be used at various stages of a model lifecycle: as a dashboard to review the model in a notebook, as part of a pipeline or as a monitoring service after deployment. With a particular focus on model drift detection, Evidently also offers features such as model quality, data quality inspection and target drift detection. In addition, it has many built-in metrics, associated visualizations and tests which are easily combined into a report, dashboard or a test-driven pipeline. © Thoughtworks, Inc. All Rights Reserved. 34