Automating Amazon SageMaker Pipelines DAG Creation – Framework Overview

Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. In this post, we introduced a dynamic framework for automating the creation of a directed acyclic graph (DAG) for Amazon SageMaker Pipelines based on simple configuration files. This framework enables ML practitioners to quickly build and iterate on ML models, while also empowering ML engineers to run through continuous integration and continuous delivery (CI/CD) ML pipelines faster, decreasing time to production for models.

The proposed framework uses configuration files to orchestrate preprocessing, training, evaluation, and registration steps for both single-model and multi-model use cases. By following the provided steps, users can easily set up the framework and deploy their ML pipelines on Amazon SageMaker, allowing for automation, reproducibility, scalability, flexibility, and model governance.

The framework’s architecture diagram showcases how it can be used during both experimentation and operationalization of ML models. By following the deployment instructions, users can organize their model training repositories, set up environment variables, create and activate a virtual environment, install required Python packages, and call the framework’s entry point to create or update and run the SageMaker Pipelines training DAG.

The configuration file structure is detailed, outlining the framework configuration and model configuration requirements. Users can specify preprocessing, training, transforming, metrics calculation, and model registration parameters for each model in their project. The structure allows for flexibility in defining dependencies and chaining steps in the SageMaker Pipelines DAG.

The examples provided in the post demonstrate single-model training scenarios using LightGBM and LLM fine-tuning, as well as a multi-model training example involving PCA and TensorFlow Multilayer Perceptron models. These examples showcase how the framework can be applied to different machine learning use cases with varying complexities.

In conclusion, the presented framework offers a robust solution for automating SageMaker Pipelines DAG creation, providing users with the tools to efficiently orchestrate their machine learning workflows. By leveraging the configuration files and following the deployment steps, ML practitioners and engineers can streamline their model development and deployment processes, ultimately contributing to the success of their ML initiatives. For more information and implementation details, users are encouraged to review the provided GitHub repository.

Meet the Authors:
– Luis Felipe Yepez Barrios
– Jinzhao Feng
– Harsh Asnani
– Hasan Shojaei
– Alec Jenab

These professionals specialize in areas such as scalable distributed systems, Generative AI, operationalizing ML workloads, data science, and machine learning solutions at scale. Their expertise and experience contribute to the development and implementation of innovative solutions in the field of machine learning.