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Machine Learning Operations

With Machine Learning Model Operationalization Management (MLOps), we want to provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.

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Getting started

Being an emerging field, MLOps is rapidly gaining momentum amongst Data Scientists, ML Engineers and AI enthusiasts. Following this trend, the Continuous Delivery Foundation SIG MLOps differentiates the ML models management from traditional software engineering and suggests the following MLOps capabilities:

  • MLOps aims to unify the release cycle for machine learning and software application release.
  • MLOps enables automated testing of machine learning artifacts (e.g. data validation, ML model testing, and ML model integration testing)
  • MLOps enables the application of agile principles to machine learning projects.
  • MLOps enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems.
  • MLOps reduces technical debt across machine learning models.
  • MLOps must be a language-, framework-, platform-, and infrastructure-agnostic practice.

Motivation for MLOps

You will learn for what to use Machine Learning, about various scenarios of change that need to be managed and the iterative nature of ML-based software development. Finally, we provide the MLOps definition and show the evolution of MLOps.

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Designing ML-powered Software

This part is devoted to one of the most important phase in any software project — understanding the business problem and requirements. As these equally apply to ML-based software you need to make sure to have a good understanding before setting out designing things.

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End-to-End ML Workflow Lifecycle

In this section, we provide a high-level overview of a typical workflow for machine learning-based software development.

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Three Levels of ML-based Software

You will learn about three core elements of ML-based software — Data, ML models, and Code. In particular, we will talk about

  • Data Engineering Pipelines
  • ML Pipelines and ML workflows.
  • Model Serving Patterns and Deployment Strategies

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MLOps Principles

In this part, we describe principles and established practices to test, deploy, manage, and monitor ML models in production.

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You will learn about the standard process model for machine learning development.

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MLOps Stack Canvas

In this part, you will learn how to specify an architecture and infrastructure stack for MLOps by applying a general MLOps Stack Canvas framework, which is designed to be application- and industry-neutral.

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ML Model Governance

This part presents an overview of governance processes, which are an integral part of MLOps.

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Need Help?

MLOps consulting services by INNOQ

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