How an MLOps Platform Helps You Manage the ML Lifecycle

While machine learning models are usually what people focus on, the ML lifecycle is a lot more complicated and intricate. There are many different processes that go along together to make the machine learning lifecycle a successful one. Building a machine learning model involves developing a plan, collecting your data, cleaning and preparing that data, training your model, and testing everything. All of these tasks are quite involved, and you need specialized MLOps tools to get the results you want.

This is why it is so crucial for a good MLOps platform to be the first place you turn when working on the machine learning lifecycle. If you want, there are also data science platforms you can leverage in the process as well. As you might remember, machine learning involves data scientists and machine learning engineers coming together to build a sophisticated ML model that will provide the outcomes the company is looking for. However, before looking for the right MLOps platform, you should start by understanding how the entire machine learning lifecycle works.

Understand the ML Lifecycle

The Machine learning lifecycle is critical to understand because it is usually how many people fail at building a model. They don’t follow the lifecycle as well as they should, leading to them doing a lot of unimportant steps or missing ones altogether. The first step in the machine learning lifecycle is to gather your data. This assumes you have devised a reasonable plan of how this machine learning model will help your business. There are plenty of sources you can get this data from, and you can even generate the data yourself.

After this step, the next one is to prepare and analyze the data. This step is usually where you select your analytical techniques and annotate the data to make it useful for training. Training the data involves applying various machine learning algorithms to the data set to extract patterns and answers based on the knowledge you are trying to discover. It is critical to perform this step well because it can have long-reaching implications for the future of your project.

Once you have confidently developed your model, the next step is to test it. The model testing and deployment phases are next, and they are crucial to ensuring that your machine learning model is ready for prime time. The various MLOps tools available only make this process easier. Generally, data science platforms are only good for the first few steps in this process.

A Few MLOps Platforms Compared

A good MLOps platform for various data science platforms is useful for helping you understand the ML lifecycle even more. The first thing to note is that there are many tools like xpresso.ai that can help you do well. On top of xpresso.ai, you have other tools like:

• Amazon SageMaker
• Azure Machine Learning
• Google Cloud AI
• Metaflow
• Paperspace

No matter which platform you choose, it must have all the features necessary to help you achieve everything in the ML lifecycle.

Choosing the Right MLOps Platform

Not all of these data science platforms will have the robust features you get with xpresso.ai. That is why it is the recommended choice because it offers an easy-to-use system, making it easy for you to achieve all the tasks in the ML lifecycle without breaking a sweat. You don’t need to be a PhD-level machine learning engineer to bring your own models to production. It is the MLOps platform for people who just want good results.