What is MLOps and Why Does it Matter?

MLOps combines the terms machine learning and DevOps and stands for Machine Learning Operations. It is a set of practices that aim to not only deploy but maintain machine learning models in production reliably and efficiently. It is a core function of machine learning, distinct from a data scientist’s. 

Machine learning is a broad area. As a result, unique roles have emerged because, as we’ve previously written about, The Data Scientist Unicorn Doesn’t Exist. MLOps is one of those roles, and it is rapidly growing in prominence. MLOps focuses on deploying, monitoring, and maintaining a machine learning model in production to ensure it doesn’t drift and manage any changing parameters or conditions. The function of MLOps can be filled by either hiring or bringing in a technology solution. 

The Top Benefits of MLOps

• Efficiency. Data teams can achieve fast deployment and higher quality models.
• Scalability. Thousands of models can be deployed, overseen, and expanded upon for continuous delivery and deployment.
• Risk Reduction. Models need to be maintained to avoid drift and ensure compliance with an organization’s or industry’s policies.

MLOps Engineer vs. An Automated Solution

To solve for the MLOps role, you can either hire an MLOps Engineer, which is essentially a software engineer specialized in the production and deployment of the overall data science process, OR you bring in a tool that can manage it all for you. 

Squark automates the roles of MLOps and data scientists. It is a complete end-to-end no-code solution that allows data scientists or any analytically inclined business user to ingest, prep, feature engineer, train, model, and display results in a matter of clicks. 

Ongoing maintenance is also taken care of by Squark. Squark can quickly review and remodel the data, reducing the risk of drift and compliance issues when adding historical data to your existing training data or new production files.

To see a demo of Squark’s end-to-end process, you can schedule a call here.