Modern software engineering is a lot easier because of DevOps. Continuous integration, testing, and deployment have made the lives of software engineers a lot easier. They can easily and quickly create, test, and deploy code without any problems. It is one of the many reasons why machine learning needs this approach. MLOps is growing, but it is not yet at the level where it is as automated as DevOps in software engineering. If everything were the same, machine learning would be easier to integrate into a normal program. MLOps engineers would be able to deploy models successfully at a much faster rate.
Difference Between DevOps and MLOps
One thing that makes MLOps different from DevOps is that machine learning and software engineering are two fundamentally different fields. In software engineering, the programmer has a streamlined approach to their goal. The programmer creates the code and then tests it before deploying it. However, machine learning is more continuous. You spend a lot of time experimenting and going back to the start in machine learning. You also have multiple people working on different stages of the pipeline. Software engineering requires a programmer for the most part. They all use the same tools and version control, so there isn’t a problem working together. Machine learning requires mountains of data, and engineers need to analyze the data to extract features and build models. There is a lot of experimentation necessary to achieve this goal.
Solving Problems in MLOps
Unlike DevOps, MLOps still has many problems that it needs to solve. These problems will require new creative solutions because machine learning requires more than just code to be successful for these projects. MLOps engineers will have to translate continuous integration and continuous deployment into machine learning concepts.
Know Where You Start
When it comes to solving problems in machine learning, it will require a new way of looking at the problem. Automation of the basic functions is one of the first things that every organization needs to look into. This automation will usually start in the form of scripts. This is what Google calls MLOps Level 0. It is the least automated, as it requires manual scripting in order for everything to work well. The highest level is MLOps Level 2. It is essentially an automated Continuous Integration and continuous deployment system that looks and feels like what people in DevOps would have.
Focus on Your Goal
As with anything in life, the first step to getting a solution is to focus on where you want to go. Your goal as a machine learning engineer will determine where you go. It means you need to have a system that will allow you to experiment, track, and quickly deploy your machine learning model to production. It will also allow you and your team to work together seamlessly.
Find Tools That Get You Closer to Your Goal
You may not find the MLOps tool to perform 100% of what you need. However, you can get 70% solved with a single tool, which is usually enough for most organizations. As this industry matures, it will become as automated as DevOps. Until then, it is crucial to spend your time on high-value tasks. You don’t want to get bogged down trying to find a tool that doesn’t exist yet.