Enterprise Readiness of Cloud MLOps: A GigaOm Benchmark Reportv1.0

Azure Machine Learning, Amazon SageMaker, and Google Vertex AI

Table of Contents

  1. Executive Summary
  2. MLOps Enterprise Readiness
  3. Competitive Platforms
  4. Field Test Setup
  5. Field Test Results
  6. Conclusion
  7. Appendix: Assessment Methodology and Scoring
  8. About William McKnight
  9. About Jake Dolezal

1. Executive Summary

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 for 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 support MLOps: the major offerings are Microsoft Azure ML, Google Vertex AI, and Amazon SageMaker. We looked at these offerings from the perspective of enterprise features and time to value.

For the analysis, we used categories of time to value and enterprise capabilities. As shown in Table 1, our assessment resulted in a score of 2.95 (out of 3) for Azure ML using managed endpoints, 2.12 for Google Vertex AI, and 2.83 for Amazon SageMaker. The higher the score, the better, and the scoring rubric and methodology are detailed in an appendix to this report.

Table 1. Overall Assessment Scores

Vendor Time to Value & Enterprise Capabilities
Azure ML 2.95 (out of 3)
Amazon SageMaker   2.83 (out of 3)
Google Vertex AI 2.12 (out of 3)
Source: GigaOm 2022

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 to look past marketing messages and discern for yourself what is valuable in technical terms, according to your goals. You are encouraged to compile your own representative MLOps use cases and workflows and review these platforms in a way that applies to your requirements.

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