One of the biggest misconceptions in machine learning is how the lifecycle should flow. It is challenging because there isn’t a standard way of doing things in this growing field. However, the machine learning lifecycle you decide upon will impact your ML models. Your ML pipeline is crucial, as it dictates how quickly you can iterate and get the results you want.
One of the many reasons that people don’t understand the ML pipeline is because they look at it from a theoretical point of view. Machine learning is relatively new, and there aren’t many platforms available. A good ML platform in a mature industry would be able to do everything a company needs. However, we are not in the area yet, so companies are still fine-tuning what their machine learning lifecycle will look like.
The Machine Learning Lifecycle
Unfortunately, most companies and machine learning practitioners only look at the first part of this process. They see the solution as success in the machine learning lifecycle. The issue with this point of view is that the machine learning model isn’t the end goal in a system. In fact, it is actually just the beginning. Practitioners forget that the point of building machine learning models is to create a production application that delivers value for the business. They get caught up in the theory of the ML pipeline instead of looking at the business problem being solved. Does that machine learning model solve your problem?
That is one of the many reasons why the production part of the model lifecycle is also crucial. ML models don’t just stop once you have delivered a working one to production. Many other considerations need to be made, and they can have a profound impact on how you look at the ML pipeline.
What You Can Learn from Experienced ML Teams
With everything being said, there are a few things you can learn from the best machine learning teams. They have a specific way of making the ML pipeline that gives them exceptional results. In a word, they don’t treat the machine learning lifecycle as one where experimentation is the focus.
The main thing is they begin with the end in mind. There is a clear path to production with every machine learning model being developed. All pieces of the puzzle are in agreement, making it possible for them to work towards the same goal. Machine learning engineers, DevOps professionals, and data scientists all have the same objectives, making it easy to build production applications in a flash. When they complete a successful experiment, they can easily transition it into a working production application.
Implement the Machine Learning Lifecycle Easily
One of the biggest things that companies can do is to utilize an ML platform to make the machine learning lifecycle easier. This is because you get a centralized location to conduct every part of the ML model building process. It means your models get built faster, meaning you can drive value for your business without the manpower and other requirements that other approaches might have. A great example of such an ML platform is xpresso.ai. You get all the tools and functionality combined into one simple-to-use platform for machine learning.