How Adopting MLOps Can Help Companies with ML Culture?

Businesses are always looking for ways to stay ahead of the competition without compromising on the customers' needs. Hence the high performing brands and firms are eager to invest in new forms of technologies as soon as it enters the market. While now the companies are implementing machine learning into their culture, mere training of machine learning models aren't sufficient to provide required capabilities. Not only that, but many of these firms also find it hard to find critical use cases or struggle to deploy. In the previous year, IDC predicted that up to 88 percent of all AI and ML projects would fail during the test phase. This leads to fatigue and frustration and talent misuse. Often, companies are clueless about where ML needs to be applied, whether that's in customer service or predictive maintenance. Therefore, to address these organizations must achieve MLOps capabilities.

MLOps is a set of practices of collaboration and communication between data scientists and the operations or production team for better management of the ML lifecycle. It combines Machine Learning, DevOps, and Data Engineering to enable asset tracking, certification, auditing, waste elimination, automation, provision of streamlined services, and improve process quality. Along with that using these tools, enterprises can handle the range of ML model-specific management needs. For instance, model creation and operationalization, transparency maintenance, real-time data monitoring, and performance.

So it is evident that just like DevOps or DataOps approaches, MLOps aims to increase automation within companies and improve both production and process quality. MLOps drives trustworthy insights with each iteration, that can put into play more quickly. Plus, it makes sure that models do not go off track or stagnate. Yet there are specific key differences like Machine learning techniques that are experimental, and Continuous training (CT) is unique to ML systems. It requires a multi-step pipeline to retrain and deploy the model automatically.

These include sequential adoption of data extraction from various sources, data analysis to understand expected data schema and characteristics and, therefore, to identify the needed preparation of data and feature engineering for the creation of the model. The later step is followed by training the model with different algorithms, evaluation, and verifying if the model is ready to be used. This deployment can occur either in micro-services with a REST API to serve online predictions, in an embedded model to an edge or mobile device or be a part of a batch prediction system. After that, the models are closely monitored to incite a new iteration in the ML process.

There are unique benefits of having introduced MLOps to an organization. It fosters opens up communication channels between data science teams and operations teams. It dilutes congestions formed by siloed models. The MLOps based model is more flexible as it can be positioned anywhere as it does not have a single deployment. Further, it prevents work lag, bias, and allows automatic, streamlined changes. Besides, it can give focused feedback on areas that demand improvement by detecting anomalies in machine learning development.

The work in MLOps is brand new and currently in its beginner stage. Although there is a rising shift towards incorporating MLOps solutions to simplify the running and utilization of various AI and ML models. As these models become more and more common, so shall the need for their regulation. And MLOps tools will be present to enhance credibility, reliability, and productivity. Thus this exciting discipline is likely to become mainstream.