What Is MLOps? Machine Learning Operations Explained
If you are part of an IT or data team at any growing organization, you’re familiar with the term machine learning.
Actually a method of computer function improvement that has been around since the 1950s, until recently—2015 to be exact—many people didn’t understand the power of ML. But, with the influx of data science innovations and advancements in AI and compute power, the autonomous learning of systems has grown leaps and bounds to become an essential part of operations.
“Today, ML has a profound impact on a wide range of verticals such as financial services, telecommunications, healthcare, retail, education, and manufacturing. Within all of these sectors, ML is driving faster and better decisions in business-critical use cases, from marketing and sales to business intelligence, R&D, production, executive management, IT, and finance.”
The possibilities are endless and the result is that many organizations dedicate entire teams to ML operations. In this post we’ll take a look at Machine Learning Operations (MLOps), including:
Deciding if your organization is ready for an MLOps team starts here.
MLOps is defined as “a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle. Similar to the DevOps or DataOps approaches, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements.”
In short, MLOps is all the engineering pieces that come together and often help to deploy, run, and train AI models. With that, we can see that there are three tightly interwoven components of MLOps:
Each component contributes key elements that work to close the ML lifecycle loop within an organization.
With origins in the development of practices used to help data scientists and DevOps teams better communicate using machine learning, MLOps began as simple workflows and processes to deploy during implementations in order to manage the difficulties faced with ML.
Leaps and bounds ahead of where MLOps was just years ago, today MLOps accounts for 25% of GitHub’s fastest growing projects. The benefits of dependable deployments and maintenance of ML systems in production are enormous. No longer just simple workflows and processes, now full-on benchmarks and systemization. IT and Data teams in all sorts of industries are trying to figure out how to better implement MLOps.
A deeper look into how MLOps works will reveal both the positive side and the problem side of this process. As discussed in an article from Medium:
“MLOps follows a similar pattern to DevOps. The practices that drive a seamless integration between your development cycle and your overall operations process can also transform how your organization handles big data. Just like DevOps shortens production life cycles by creating better products with each iteration, MLOps drives insights you can trust and put into play more quickly.”
When considering data as a key business tool that directly relates to how an organization adapts future system operations, essentially MLOps is the process of taking both data and code in order to produce predictions that describe which deployment to put into production. This requires both operations (code) and data engineering (data) teams to work hand in hand.
Among many positive aspects of ML, a few topline benefits directly relate to any organization’s ability to stay relevant and grow in this tech and information-driven world. Most experts agree, as outlined by Geniusee, that the MLOps positive impacts are:
From data processing and analysis to resiliency, scalability, tracking, and auditing—when done correctly—MLOps is one of the most valuable practices an organization can have. Releases will end up with more valuable impact to users, the quality will be better, as well as performance over time.
