Table of Contents
- MLOps Enterprise Readiness
- Competitive Platforms
- Field Test Setup
- Field Test Results
- TCO Analysis
- Appendix: Assessment Methodology and Scoring
- About William McKnight
- About Jake Dolezal
MLOps is a practice for collaboration between data science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
- Faster time to market of ML-based solutions
- More rapid rate of experimentation, driving innovation
- Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
For the analysis, we used categories of Total Cost of Ownership (TCO) time-to-value and enterprise capabilities. Our assessment resulted in a score of 2.9 (out of 3) for Azure ML using managed endpoints, 1.9 for Google Vertex AI, and TK for AWS SageMaker. The assessment and scoring rubric and methodology are detailed in an annex to this report.
We hope this report is informative and helpful in uncovering some of the challenges and nuances of platform selection: we leave the issue of fairness for the reader to determine. We strongly encourage you, as the reader, to look past marketing messages and discern for yourself what is of value in technical terms, according to the goals you are looking to achieve. You are encouraged to compile your own representative MLOps use cases and workflows, and review these platforms in a way that is applicable to your requirements.