MLOps: Current problems and Future Trends


MLOps: Current problems and Future Trends Team
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Machine learning and artificial intelligence have moved from the realm of research and into practical consumer applications. With this move, there are now many challenges to overcome. It is a relatively new field, so people will have to develop open-source toolkits to facilitate the automation of many machine learning and data science functions like what has happened with software engineering. Just a few decades ago, DevOps wasn’t a separate field, and every company needed its own solutions for build automation and deployment. Today, DevOps is a growing field with many off-the-shelf and open-source tools to automate almost everything related to building, testing, and deploying software. Machine learning will have to go through this similar phase and develop its own tools for managing and orchestration.

The Big Issue to be Solved

The biggest challenge today is that data science teams are not spending the majority of their time doing any data science. They are spending that time working with the various frameworks and infrastructure needed, which MLOps is hoping to solve. They are also spending a significant portion of their time preparing the data to be manipulated. The machine learning pipeline will need to be optimized using MLOps pipeline automation tools. MLOps seeks to be this grand solution that does what DevOps does for software engineering, but for machine learning and data science.

Problems with Data

On top of all of these concerns, there is the issue of working with data from models and into production. The majority of data scientists work with data using tools like R and Matlab. However, this data is often highly curated, and there are no real-time demands placed on the data scientist. Working with the data in production is different. There are extra considerations that need to be taken by the data scientist, as they need to efficiently process that data quickly so that the applications don’t slow down. They also cannot make any mistakes in production, as it could crash the applications or cause it to have inaccurate results. To solve this problem, organizations are looking into building their own feature stores. It is a solution that companies like Twitter and Netflix have done.

Autoscaling Machine Learning Using a Serverless Architecture

In production, scalability is also another consideration. Companies are looking into using a serverless architecture to solve that problem. A serverless architecture involves abstracting the server away from your application. In doing this, the application can scale from 1 to n nodes seamlessly without thinking about the underlying hardware. It also means companies can dedicate fewer people to managing their data science and machine learning pipeline. It also helps with pipeline automation as it adds automatic scaling to production workloads.

Automating Using Feature Stores

Feature Stores are also becoming a must-have for production machine learning workloads. However, only a few corporations like Uber have built their own, as they are notoriously complicated and difficult to build without plenty of resources. Feature stores are a huge problem that will need to be overcome with new innovative platforms and open source tools.

About the Author Team
Enterprise AI/ML Application Lifecycle Management Platform

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