ML Model Management and Operations 2020 (“MLOps”)

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.
Premium PDF. Source: Copyright © Cognilytica LLC 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
Table of Contents
  • 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
Although Cognilytica believes that the results, conclusions, and analysis produced in support of this report are well informed, comprehensive, and reasonable, Cognilytica cannot guarantee future results, accuracy of market predictions, or applicability of conclusions to report purchaser or reader’s business. Moreover, Cognilytica does not assume responsibility for the accuracy and completeness of such statements. The information derived in this report are statements of opinion only, and Cognilytica shall not be held liable in any manner for any conclusions or actions taken pursuant to this report. The information contained herein has been obtained from sources believed to be reliable. Cognilytica shall have no liability for errors, omissions, or inadequacies in the information contained herein or for interpretations thereof. Report purchaser and/or reader assumes sole responsibility for the selection of these materials to achieve its intended results. The opinions expressed herein are subject to change without notice. Cognilytica does not make open its research methods, underlying data, sources, or means and methods of analysis for inquiry, evaluation, or examination.

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