What is MLOps?

What is MLOps?

Machine learning operations (MLOps) is the discipline of AI model delivery. It is what allows organizations to scale their production capacity to a point of generating significant business value and delivering results.

Machine learning operations

Machine learning operations

MLOps includes all the capabilities that data science, product teams, and IT operations need to deploy, manage, govern, and secure machine learning and other probabalistic models in production.

MLOps combined the practice of AI/ML with the principles of DevOps to define an ML lifecycle that exists alongside the software development lifecycle (SDLC). Its purpose is to support the continuous integration, development, and delivery of AI/ML models into production at scale.

ML Lifecycle
ML Lifecycle

A machine learning lifecycle supports model delivery at speed and scale

A machine learning lifecycle supports model delivery at speed and scale

An MLOps process is the repeatable organization of work that guarantees models can be delivered as efficiently and quickly as possible. Organizations often underestimate the complexity and challenges of moving ML to production. As a result, the number of ML models that are developed and never reach production tops 85% and delivery times are measured in months when they should be measured in hours.

Machine learning projects only provide value once models reach production. Delivering models to production involves a unique set of architecture and programmatic challenges around:

  • hardware orchestration,
  • integration of language and framework SDKs,
  • container management,
  • model versioning,
  • use of new CPUs and GPUs,
  • inference API management,
  • load balancing,
  • security and governance of users, models, data, and operations.

The organization, management, and monitoring of all of this into a programmatic process is MLOps.

Benefits of MLOps

Benefits of MLOps

Protect models from tampering with an enterprise-class framework of access controls, security APIs, and system encryption.


  • Accelerate time-to-value Programmatically optimize the deployment of each model dramatically to shorten the time from training to production—from months to minutes.
  • Optimize productivity of teams Integrate with current workflows and tooling to provide clear roles and reduce wasted time.
  • Manage infrastructure Systemically manage computation resources across models to meet business outcomes and cost-performance requirements to dramatically lower costs.
  • Protect your business Maintain enterprise-grade security and role-based access controls across users, data, models, and resources to ensure the continuity of your business.

How to introduce MLOps to your company

How to introduce MLOps to your company

Integrate with your existing systems
ML does not happen in a vacuum. The development systems you introduce need to integrate with your existing enterprise systems, plaform choices, and monitoring applications. Download our MLOps and managemen framework guide to identity and assess all your key integrations between algorithms and applications.

Don't reinvent the wheel
Building an ML platform from scratch will set you back years and cost much more in budgett when you examine the cost of staff, missed opportunities, and mistakes made along the way. Look to leverage a commercial MLOps platform that is more cost effective, more stable, and future-proofed against new technologies.

Say no to lock-in
Don't allow your existing operational processes to limit the data science technologies your teams can take advantage of. Look for an MLOps process that allows for flexibility and can support the infrastructure and data production environments you already have.

Accelerate time to delivery
Ensure that your operational approach to ML is reducing your time to delivery and increasing the capacity of models in production. A robust MLOps program will deliver models rapidly and allow your organization to react to market changes.

Audit honestly, revise constantly
Audit the performance of your models in a production environment, and do the same for your production processes. At every sprint, take a moment to reconsider your strategy, infrastructure, and ROI projections, and keep stakeholders informed.

Manage MLOps with Algorithmia

Manage MLOps with Algorithmia

Algorithmia was created to help organizations deploy their models into production faster. This is possible because of robust MLOps processes that manage and govern models throughout the entire ML lifecycle. Algorithmia's MLOps creates a seamless journey through the lifecycle, getting models into production quickly and effortlessly, providing you with the return on your ML investment faster.

Learn more

MLOps and management framework guide
Download our guide to learn more about the importance of implementing MLOps. Download
Roadmap to
ML maturity
See where your organization stands on Algorithmia's roadmap to ML maturity. Download
Get an MLOps starter kit
Sign up for a custom demo of Algorithmia and receive our MLOps starter kit if your organization is qualified for our solution.