As the markets for AI shift from those organizations that have the technical expertise required to build models from scratch to those enterprises and organizations looking to consume models built by others, the focus shifts from tooling and platforms focused solely on model development to tools and platforms focused on the overall usage, consumption, and management of models. Machine Learning Model Operationalization Management, referred to as “MLOps”, is focused on the lifecycle of model development and usage, machine learning model operationalization, and deployment. In this report, Cognilytica examines the ML Operationalization Management market, provides visibility into emerging best practices in the MLOps space, identifies open source and commercial vendor solutions, and provides a forecast for the growth of the overall market for solutions and products focused in this space.
Key Findings:

• The market for MLOps Solutions is growing from a nascent $350M in 2019 to almost $4B by 2025.
• Cognilytica expects vigorous growth in the MLOps sector, with substantial new entrants into the market place by both new, emerging companies as well as established incumbents
• The market for MLOps solutions is segmented between those that are development-centric, market-centric, and full-cycle solutions for both model development and management.
• The AI and ML markets are shifting from development-centric to consumption-centric activities.

Key Vendors & Open Source Solutions Included in this Report:

• Algorithmia
• Alteryx Promote
• Amazon SageMaker Model Monitor
• Cloudera ⭑
• cnvrg.io
• comet.ml
• Databricks Unified Analytics Platform
• Dataiku ⭑
• Datarobot (including acquisition of ParallelM) ⭑
• Datatron
• Dotscience ⭑
• HPE
• Hydrosphere
• Microsoft Azure ML Studio
• MLflow ⭑
• Metaflow ⭑
• Modzy ⭑
• PachyDerm
• Petuum
• Verta.ai

⭑ = Profiled

Report Details:

• 29 Pages
• 3 Figures

[wpdm_package id=’6427′]

• Executive Summary 2
• Key Findings 3
• Market Overview 3
• Defining the Problem 4
  • DevOps for ML vs. ML Operationalization Management 6
  • Challenges with DevOps Approaches to ML 7
  • The Emergence of Specialized MLOps Solutions 8
• Core Components of ML Model Operationalization Management Solutions 8
  • Model Lifecycle Management 9
  • Model Versioning and Iteration 9
  • Model Monitoring 10
  • Model Governance 10
  • Model Discovery 11
  • Model Security 11
• Limitations of ML Operationalization Management Solutions 12
• Cognilytica Classification 12
  • About the Cognilytica Vendor Classification System 13
• Market Size Estimates and Growth Projections 13
  • MLOps Market Size and Projections 13
• ML Operationalization Management Vendor Landscape 15
  • Open Source Solutions 16
    • MLflow 16
    • Metaflow 17
• Key Commercial Vendors 18
• Increasing Market Competition from Market Incumbents 19
• Key Vendor Profiles
  • Cloudera 18
  • Dataiku 20
  • Datarobot (including acquisition of ParallelM) 21
  • Dotscience 23
  • Modzy 24
  • Notes on Vendor Inclusion 25
• Future Market Trends and Predictions 26
• Related Research 26
• About Cognilytica 27