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What Is MLOps and What Is It Used For

What Is MLOps and What Is It Used For?

Published: November 25, 2022
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MLOps stands for ‘Machine Learning Ops’ or ‘Machine Learning Operations’. As the name implies, it involves taking a DevOps-style approach and applying it to Machine Learning (ML).

So, before we can properly define MLOps, we need to answer the question, ‘What is Machine Learning?’ The term refers to a form of artificial intelligence, one that makes software programs better able to predict outcomes by utilizing relevant historical data. This is applied in areas like customer behavior, business operations, and product development to steer strategy and development in a way that helps organizations optimize the value of products and services.

This kind of insight can be extremely useful in the hyper-competitive marketplaces of the Digital Age. However, applying machine learning practically can still pose a challenge. 

MLOps helps organizations utilize machine learning not only practically but at scale. The MLOps lifecycle involves a process of automating and putting into production machine learning flows and applications. This can be applied in various areas, including:

  • Version control
  • Application monitoring
  • Application development

MLOps bridges design, model development, and operations for machine learning, reducing turnaround time and creating viable ML solutions for end-users. Key inferences and predictions can be made based on generated data, and automated machine learning tools will be applied as part of a continuous process to get the results to a reliable level of accuracy.

Is MLOps the same as DevOps?

It’s easy to see the similarities between MLOps and DevOps. Both approaches focus on:

  • Establishing repeatable, efficient, and reliable software workflows
  • Making processes more scalable and efficient while 
  • Utilizing continuous testing and automation to optimize reliability and efficiency
  • Continuous collaboration

The difference is that MLOps introduces new structured machine learning components to traditional DevOps pipelines. Just as DevSecOps introduces security elements in a DevOps-oriented fashion, MLOps introduces the ability to interpret historical data and make valuable predictions to support development. For this reason, it would be accurate to describe MLOps as a specialized subset of DevOps.

With all this in mind, it is fairly simple for someone familiar with DevOps to pick up MLOps, provided they have adequate knowledge and experience with machine learning. 

Who uses MLOps?

MLOps is mostly used by:

  • DevOps engineers
  • IT engineers
  • Data scientists
  • Machine learning engineers

With the versatility and flexibility of DevOps-style approaches, MLOps has also been applied in organizations of different sizes, industries, sectors, and locations.

Senior DevOps engineers (or ‘DevOps leaders’) can benefit from studying MLOps and incorporating it into a pipeline. Machine learning is becoming an increasingly valuable tool, and having the expertise to implement and oversee it can greatly enhance a business. Needless to say, this can also help candidates unlock significant career opportunities.

Is MLOps important?

In today’s digital markets, competitiveness is key. To put it simply, MLOps cannot be ignored for the same reason that DevOps style management cannot be ignored: with the speed, efficiency, and reliability it offers, failing to explore it can quickly cause an organization to lose its competitive edge.

Machine Learning is already playing a significant role in many of the world’s most successful companies. It empowers them to enhance software pipelines and deliver services that meet the needs of clients with greater insight and reliability.

As for why MLOps itself is important, it simply goes back to the idea of practical utilization. It can be incorporated into a DevOps pipeline, enabling organizations to apply it on a far larger scale. Its popularity has also seen a variety of machine learning tools and platforms released to help with automation and standardization.

One of the best things about DevOps has always been its capacity to evolve. There is no one singular version of DevOps, and there is no sense in waiting around for someone to release a new iteration. Instead, it evolves as its practitioners find new elements to introduce and varied ways to improve. 

MLOps is a significant stage in the evolution of DevOps as a concept. For organizations that rely on the optimization of software pipelines, investigating the benefits of MLOps should be done sooner rather than later.