What is MLOps? – Benefits, how it works, and DevOps vs. MLOps
What is MLOps?
Machine Learning Operations (MLOps) is a set of policies, practices, and governance that are put in place for managing machine learning and artificial intelligence solutions throughout their lifecycle.
MLOps is collaborative in nature, enabling data science, and IT teams to collaborate and boost model development and deployment pace by monitoring and validating machine learning models. It allows data scientists to track or certify every asset in the ML lifecycle and provides integrated solutions to streamline lifecycle management.
In addition, MLOPs 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.
Benefits of MLOps
Organizations with MLOps initiatives reap several benefits, including:
- Improved confidence in their model
- Improved compliance with regulatory guidance
- Faster response times to changing environmental conditions
- Lower break-fix cost
- Increased trust and ability to drive valuable insights
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 can help mitigate these common challenges while providing additional value to the organization with improved ML model quality and performance.
How does it work?
MLOps is positioned to solve many of the same issues that DevOps solves for software engineering.
DevOps solves the problems associated with developers handing off projects to IT Operations for implementation and maintenance. MLOps introduces a similar set of benefits for data scientists.
With MLOps, data scientists, ML engineers, and app developers can focus on collaboratively working towards delivering value to their customers.
Let’s understand this using an example.
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, it 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.
DevOps vs. MLOps
Though there are many similarities between DevOps projects and ML projects, it’s important not to take DevOps practices and techniques and apply them blindly to machine learning projects. The IT team does not have deep expertise on modeling algorithms, and data scientists do not want to manage infrastructure, so it’s important to bridge the gap with ML engineers for implementing MLOps.
The ML Engineer role brings a specialized skillset with the mandate of collaborating with IT and the business to ensure the models are well supported throughout the lifecycle. Aside from skillset, there are key differences in the activities taken when implementing DevOps vs. MLOps.
“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 how Neal Analytics can help you in your MLOps journey? Contact us and we can put you in touch with one of our analysts.
If you are looking for more resources on MLOps, we recommend the following content:
- Microsoft Azure: Drive efficiency and productivity with machine learning operations
- 3 tips for starting your MLOps journey
- Scale AI model governance with the AI Factory Framework
- MLOps series: AI Engineering with Opensource tools & AKS
- MLOps: The AI life cycle for IT production
This article was originally published 5/18/2020. It can also be found on LinkedIn.