MLOps: Machine learning operations | Algorithmia

MLOps: Machine learning operations | Algorithmia

Nothing gets to production without MLOps. Here’s everything you need to know about MLOps—and why it’s the key to generating business value from machine learning.

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What is MLOps?

Machine learning operations (MLOps) is the discipline of delivering machine learning (ML) models through repeatable and efficient workflows.

Just like DevOps defined a set of best practices for the software development lifecycle (SDLC), MLOps enables the continuous delivery of high-performing ML applications into production at scale. It takes into account the unique needs of ML to define a new lifecycle that exists alongside existing SDLC and CI/CD processes—generating a more efficient workflow and more effective results for ML.

MLOps includes all the capabilities that data science, product teams, and IT need to deploy, operate, govern, and secure models in production. It includes the following components, which together enable an automated ML pipeline that maximizes your ML performance and ROI:

  • Model serving and pipelining

  • Model service catalog(s) for all models in production

  • Model version control

  • Infrastructure management

  • Monitoring

  • Security

  • Governance

  • Connections with all data sources and best-in-class tools for model training, development, infrastructure, and compliance

AIOps, or

XOps is a general term for operationalization applied to AI systems. MLOps, ModelOps, and AIOps are all subsets of XOps.

AIOps refers to the use of AI in IT operations. MLOps and ModelOps both refer to the same thing: the operationalization of AI application delivery, which includes AI, ML, and all other types of probabilistic code. In this guide, we will use the term “MLOps”.


Organizations that have increased their ML budgets for 2021.

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Why MLOps?

ML only provides value once models reach production. However, organizations often underestimate the complexity and challenges of moving ML to production—focusing most of their resources instead on ML development, while treating ML just like standard software.

The result? Organizations fail to see results from their ML projects, resulting in lost funding, wasted resources, and difficulty retaining talent.

87% of organizations struggle with long model deployment timelines and, at 64% of organizations, it takes a month or longer to deploy a single model. All this despite the fact that 86% of organizations have increased their ML budgets for 2021.

In reality, ML isn’t like standard software. It requires its own unique approach, and the code is only a small component of what makes an AI application successful.

ML is different from standard software because data is at the core of the application. This means that the code is built around servicing the data rather than the application behavior. In addition, ML is an ever-evolving, open-loop system. Once models are deployed, the work has only just begun. Models in production must constantly be monitored, retrained, and redeployed in response to changing data signals so you can ensure maximum performance.

All of this means that ML requires its own unique lifecycle.

The ML Lifecycle

The ML Lifecycle

The ML lifecycle is a process of continuous integration, development, and delivery of ML models. It consists of three main stages—development, deployment, and operations—that models constantly cycle through to support their continuous tuning in production. Governance and security are critical components of all three lifecycle stages, and help ensure that the complete lifecycle meets enterprise standards.

MLOps supports the ML lifecycle by providing the connection between the ML code and all the other components required for ML success.

How MLOps works

MLOps supports the ML lifecycle by providing the connection between the ML code and all the other components required for ML success. This includes:

  • Training data

  • Model training

  • Model validation

  • Production data

  • Model serving and pipelining

  • Model service catalog(s)

  • Model version control

  • Infrastructure management

  • Monitoring

  • Security

  • Governance

  • Explainability and interpretability

MLOps provides all the core components and the ability to connect to the rest—from data sources to tools for compliance.

4 Machine-Learning-Ecosystem

Benefits of MLOps


Deliver more models, quicker

Your competitive advantage from ML comes from how quickly you can deploy and iterate on models. Put ML into production 12x faster* through the repeatable, scalable processes made possible through MLOps.


Maximize ROI

Without MLOps, model and infrastructure monitoring is a piecemeal effort—if it exists at all. Now, you can easily monitor and optimize your models and infrastructure, unlocking 2x incremental profit margins at 7x lower infrastructure costs*.


Increase business agility

Your models need regular tuning to stay ahead of the competition. But you don’t need to do this manually! With MLOps, you can create automated pipelines and workflows that optimize your total cost for ML while keeping you competitive.


Integrate flexibly

You want to take advantage of the latest and greatest ML tools, but the rapid pace of change makes it difficult to keep up. With MLOps, you can easily maintain integrations with a wide range of data science tools without taking on more technical debt.


Protect your business

Poorly governed ML presents a risk to your company assets—and you can’t have governance without MLOps. Minimize risk with enterprise-grade security and governance across all data, models, and infrastructure, reducing audit and compliance risk by 30%*.

* Customer-reported outcomes

How to tell if you need MLOps

Wondering if your company needs MLOps? Here are a few telltale signs.

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    It’s becoming difficult to maintain the various tools, languages, and frameworks you’re using for data science.

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    You’ve increased your investment in ML, but it isn’t taking you any less time to deploy a new model to production.

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    You have multiple models in your catalog, but can’t easily keep track of them all.
    Model version control is either nonexistent or manual.

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    You’re running ML in a hybrid environment (multi-cloud, hybrid on-premises, or hybrid cloud and on-premises).

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    You’re supporting multiple types of data workloads—anything from legacy batch and cron jobs to real-time inference.

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    Your DevOps team is monitoring the performance of your standard applications, but not your ML applications. Or, they include ML applications but it’s nowhere near as advanced.

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    Your IT team is being asked to satisfy internal requirements for ML, such as cost controls, security, and performance metrics.

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    You face external regulatory requirements for your ML efforts.

How to implement MLOps at your organization

Follow these three best practices to effectively implement MLOps at your organization.


Integrate with your existing systems

ML doesn’t happen in a vacuum. Your MLOps platform needs to integrate with your existing enterprise systems, infrastructure, development tools, and reporting applications.


Don’t reinvent the wheel

Building an MLOps platform from scratch will set you back years and cost much more than an off-the-shelf solution 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, stable, and future-proofed against new technologies.


Say no to lock-in

Don’t let your processes limit the data science technologies your teams can take advantage of. Look for an MLOps platform that supports the tools you’re already using, and allows for flexibility and scale as you grow.


Building versus buying an MLOps platform

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Let us show you why off-the-shelf MLOps platforms offer the best ROI.


Organizations where I&O plays a key role in ML decision-making.

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Who to involve

A typical MLOps implementation will include the following stakeholders.

Infrastructure and operations (I&O)

I&O is essentially the backbone that makes fast, secure, and scalable systems possible. I&O leaders are increasingly responsible for scaling ML initiatives in production, and need to be convinced that those initiatives don’t introduce new risks or operational burdens.

Head of data science

The head of data science is responsible for leading the team that owns ML initiatives. A key player in ML decision-making, their #1 responsibility is to unlock the value in an organization’s data for the business.

Business team

Business teams are responsible for creating the products that actually use machine learning on the back end. These professionals are responsible for driving market growth and helping the organization maintain their competitive edge.

Manage MLOps with Algorithmia

Algorithmia is the enterprise MLOps platform. It manages all stages of the production ML lifecycle within existing operational processes, so you can put models into production quickly, securely, and cost-effectively.

Unlike inefficient and expensive do-it-yourself MLOps management solutions that lock users into specific technology stacks, Algorithmia automates ML deployment, optimizes collaboration between operations and development, leverages existing SDLC and CI/CD systems, integrates with best-of-breed tools, and provides advanced security and governance.

Over 130,000 engineers and data scientists have used Algorithmia’s platform to date, including large and midsize enterprises, Fortune 500 companies, non-governmental organizations, and government intelligence agencies.

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