The machine learning industry is moving at a breakneck pace. That is why innovations like MLOps are so vital, and they are taking off so well. We are also seeing people discover how valuable a feature store can be. The main issue with machine learning is always how difficult it is to deploy to production. As a company, you need a skilled workforce to do it well, and you need even more people to keep things going on the operations side. AutoML exists to ensure that people who don’t have the necessary skill set can still create and deploy working machine learning models. However, companies now realize that this was never the biggest problem for the mass adoption of machine learning.
The reality is that machine learning needs its operations to get up to par with other industries. MLOps is great, but you still need more automation in the actual deployment and operations for automating machine learning. AutoMLOps seems to be with the future is heading in this industry. Companies understand that it isn’t enough to automate the training process anymore, which will create more widespread adoption.
The major innovation in MLOps for 2022 will be even more automation in the process. The biggest problem with bringing a machine learning project to production is how difficult it is for the average person. The difficulty has gone down over time, making it even more crucial to understand what is going on. Many of the important processes are automated, but it still isn’t enough.
AutoMLOps will focus more on automating the deployment aspect of it. AutoML currently focuses on how to turn your project into a working model. It is all about automating that aspect of creating the model. However, what happens when it is time to put that model into production? This is where things go back to being more difficult.
Feature Stores Will Get Popular
One of the biggest things about machine learning is that it is not too difficult for average companies to build a feature store now. Feature stores have always been the purview of massive corporations that made machine learning a foundational pillar of their business model. However, things are changing now, and feature stores are finally becoming mainstream enough to achieve wider adoption.
Companies realize how important it is to use feature stores to make it faster to get to market. They also see how you can combine feature stores with AutoML and MLOps to deliver the best results possible.
Looking At Business Impact with AI
The biggest thing that will come in 2022 with MLOps and the machine learning business is a focus on business impact. Many companies suffer from shiny object syndrome with machine learning, and that is something that will need to change for them to succeed.
Currently, the overwhelming majority of machine learning initiatives fail, and that can be attributed to companies not focusing on how those initiatives help the business. Companies will need to use statistical models to understand whether they are wasting time or not with machine learning. Companies will also need to see the ROI of things like a feature store and AutoML.