What is MLOps?
Machine Learning Operations (MLOps) is the set of policies, practices, and governance that are put into place for managing machine learning and artificial intelligence solutions throughout their lifecycle.
It focuses on building a common set of practices which data scientists, ML engineers, app developers, and IT Operations can follow for systematically managing analytics initiatives. Organizations with MLOps initiatives reap several benefits, including:
- Improved confidence in their models
- Improved compliance with regulatory guidance
- Faster response times to changing environmental conditions
- Lower break-fix costs
These benefits put organizations with MLOps initiatives ahead of the competition as their counterparts continue to struggle packaging, deploying, and maintaining stable model versions.
MLOps is positioned to solve many of the same issues that DevOps solves for software engineering.
For example, DevOps solves the problems associated with developers handing off projects to IT Operations for implementation and maintenance, while MLOps introduces a similar set of benefits for data scientists. With MLOps, data scientists, ML engineers, and app developers can focus on collaboratively work towards delivering value to their customers.
Traditionally speaking, packaging and deploying machine learning solutions has been a manual and error-prone processes. One likely scenario is that data scientists build models in their preferred environment and later hand off their completed model to a software engineer for implementation in another language like Java.
This is incredibly error prone, as the software engineer may not understand the nuances of the modeling approach or the underlying packages used. Additionally, it requires a significant amount of work each time the underlying modeling framework needs to be updated. A much better approach is to use automated tools and processes to implement CI/CD for machine learning.
This is where MLOps comes in. The modeling code, dependencies, and any other runtime requirements can be packaged to implement reproducible ML. Reproducible ML will help reduce the costs of packaging and maintaining model versions (giving you the power to answer the question about the state of any model in its history). Additionally, since it has been packaged, it will be much easier to deploy at scale. This step of reproducibility provides and is one of several key steps in the MLOps journey.
MLOps aims to support machine learning models throughout their lifecycle by implementing a common set of practices. These include a broad range of tasks, from implementing source control to maintaining a registry of model versions, packaging standards, validation checklists, deployment strategies, and monitoring protocols.
Well-established MLOps practices allow organizations to understand when it is time to retrain models because the monitoring pipelines will have detected data drift. Additionally, MLOps can help answer questions like what data, model version, and codebase was used to generate a specific prediction.
These are increasingly important topics, especially in an era where concepts like Responsible AI are becoming more popular or required.
“How do I get started with MLOps?”
This is perhaps the most common question we receive.
One thing that must be reinforced: MLOps is not a product that you can buy. It’s a way of working.
Implementing MLOps is just as much about change management and making sure that the right mix of personnel are involved throughout the ML lifecycle. Additionally, the level of effort to implement MLOps practices can vary significantly depending on the maturity level of the organization. Neal Analytics is prepared to help you assess this maturity and get you started in your journey.
Want to learn more about how Neal Analytics can help you in your MLOps journey? Contact Us and we can put you in touch with one of our analysts.
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