Automating and scaling machine learning workflows with CI/CD
Machine learning (ML) models have unleashed a new wave of innovation and data-driven decision-making across the software industry. But without guardrails, building ML models can be slow, expensive, and risky. To effectively incorporate and scale ML development, you need the speed, security, and consistency that a robust continuous integration and continuous delivery (CI/CD) pipeline can provide.
In this guide, you’ll learn:
- What CI/CD is and how it helps data and software teams move faster and improve the quality of their releases
- What challenges ML teams face and how you can use CI/CD to solve them
- How to effortlessly set up an end-to-end ML pipeline to automate building, training, testing, deploying, monitoring, and retraining your ML models
