Most Useful ML Ops Applications

Machine learning operations, or ML Ops, focus on helping data scientists and others who work with machine learning models deploy them more efficiently and experience fewer problems while doing so. Many ML Ops tools allow overseeing the entire machine learning model life cycle. Here are some of the most worthwhile ones to consider. 

ClearML has several products under the company umbrella. Together, they allow users to develop, orchestrate and automate their algorithms. For example, the ClearML Tasks product saves users time by automatically logging everything that happens within the machine learning process. It’s an open-source experiment management tool. 

There’s also ClearML Cloud. It helps users modify, replicate or launch ML workflows from a local location or in the cloud. Then, ClearML Remote is especially helpful for people working in distributed teams. It allows people to share local or cloud-based GPU machines, helping team members collaborate more smoothly and efficiently. 

These are some of the ClearML products most relevant to ML Ops. However, if they sound applicable to your work, consider checking out all the offerings and exploring how they might help you get more things done while encountering fewer struggles. 

Metaflow captured headlines in 2019 after Netflix made it available as an open-source tool. It has stayed relevant by allowing data scientists to focus on what they do best and less on the often-cumbersome engineering aspects. 

This application supports Python and R, meaning many data scientists who start using it don’t have many new things to learn. Plus, Metaflow assists users through all steps of building an algorithm. It allows them to design the workflow, run it at scale and then deploy it to a production environment. 

People can also use the built-in integrations that make Metaflow work with cloud environments from providers like AWS and Azure without making code changes first. That capability makes projects scalable without hassles. 

Ray comes with native and scalable machine-learning libraries that make it easier to scale compute-intensive workloads. People who want to use distributed hyperparameter tuning on their models can start with only 10 lines of code. 

Ray’s features also allow people to build and run distributed apps without prior knowledge of working with distributed systems. This tool handles everything from scheduling to fault tolerance, reducing the manual tasks users must do. 

It also automatically provisions or removes nodes as needed when workload sizes change. Also, since Ray runs on a variety of platforms and infrastructure types, people can start using it quickly, making few or no changes beforehand. 

ML Ops is becoming highly applicable to people who use and develop machine learning algorithms. You can expect it to eventually become part of well-rounded curriculums for students interested in learning more about the subject. Many people at the companies behind the most useful ML Ops tools know that the overall experience is highly important, especially for users who are relatively new to managing their ML models this way. 

Galileo stands out for being feature-rich but easy to use. For example, it automatically screens data for gaps and errors that could otherwise slow down machine learning projects. People can also use a centralized dashboard to track all data and model-related changes. From there, it’s easy to share reports with team members, keeping everyone on the same page. 

Galileo also gives people the intelligence and insights they need to understand how well a machine-learning model will likely work. They can get those details before putting it into production, allowing people to correct mistakes and save time in the process. When solving problems in training data becomes more efficient, the respective model should perform better, too. Galileo helps that happen. 

ML Ops is a relatively new focus for many companies, but it’s become essential as those businesses increasingly rely on machine learning for various operations. No matter how familiar you are with ML Ops, these products can help you embrace it more thoroughly and improve your chances of succeeding with machine learning models. 

Before trying one of these tools, think about the existing weaknesses in your machine-learning process and what goals you want to set. From there, you’ll be in a good position to explore how the applications here and others could aid in continually improving your processes and results.