What is MLOps?

Machine learning operations (MLOps) is the discipline of AI model delivery. MLOps enables your organization to scale production capacity to deliver faster results, generating significant value for your business.

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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 probabilistic models in production.

MLOps combines the practice of AI/ML with the principles of DevOps to define an ML lifecycle that exists alongside the software development lifecycle (SDLC) for a more efficient workflow and more effective results. 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 operations process sets the procedures for organizing your machine learning work in both a productive and repeatable way. MLOps ensures that models can be delivered as efficiently and quickly as possible, even with collaboration across various departments. Organizations often underestimate the complexity and challenges of moving ML to production, even though ML projects only provide value once models reach production. The number of ML models that are developed and never reach production tops 85% historically and delivery times are measured in months when they should be measured in hours. Algorithmia can help you change that.

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Benefits of MLOps

Accelerate time-to-value

Accelerate time-to-value

Programmatically optimize the deployment of each model dramatically to shorten the time from training to production—from months to minutes. Then continue to learn and optimize so you can scale efficiently.

Optimize productivity of teams

Optimize productivity of teams

Integrate with current workflows and tooling to provide clear roles and reduce wasted time and roadblocks between groups. Have constant access to monitor and report on current projects to make timely decisions.

Manage infrastructure

Manage infrastructure

Systemically manage computation resources across models to meet business outcomes and cost-performance requirements to dramatically lower costs. Deploy on-premises, in the cloud, or in a hybrid environment.

Protect your business

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 through continuous delivery.

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, platform choices, pipeline strategy, and monitoring applications. Download our MLOps and management framework guide to identify and assess all your key integrations between algorithms and applications.

Don't reinvent the wheel

Building an ML platform or software from scratch will set you back years and cost much more in budget when you examine the cost of staff, missed opportunities, and mistakes made along the way. Look to leverage a commercial MLOps platform or service 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 scale, and can support the infrastructure and data production environments you already have. Your MLOps model should help your workflow, not inhibit it.

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 quickly, giving you more room to use those results to serve business improvement.

Audit honestly, revise constantly

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

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. Deploy, monitor, manage, and govern your entire AI/ML pipeline in one singular place with machine learning operations. Algorithmia's MLOps creates a seamless journey through the lifecycle, getting models into production quickly and effortlessly, providing you with return on your ML investment faster.