Machine Learning Models Are Useless If They Aren’t In Production
The point of building machine learning models is to put them into a production application. That is why your people must have experience doing just that. There is a step-by-step process that they need to go through, but they also need to have the model deployment experience that can make the difference in tricky situations.
Because of the reality of machine learning, we have to realize that ML model deployment is ultimately the most important part of the process. Everything else is just theorizing until your machine learning models are in a working production application. Having hands-on experience and putting a model into a production application is now a must for data scientists. For most companies, it is unreasonable to expect them to have a team for model creation and another for deployment. Only a few mega-corporations can afford to do that. However, putting anML model in production is a combination of art and science. It is something that almost any data scientist can learn and master.
Getting Infrastructure Right for Data
Every machine learning project needs good data to be successful. However, having the proper infrastructure for that data is often a problem for most companies. They don’t understand where to put the data to make it as effective as possible. Moreover, they might run into problems with the architecture of their application. You must have the necessary infrastructure to feed your model the data it needs to make accurate predictions.
You need to answer questions about where and how you store your training data. You also need to get a handle on the size of your data set as well. How will you retrieve that data set for training and production? These things are important to know because they will determine how you structure your infrastructure in a way that will serve your specific needs. It is for this reason why ML model deployment is such a difficult task.
Choosing the Right Tooling
The next step in the process is choosing the right tooling for your projects. The first choice is usually the cloud solution you will choose. For example, you can choose between AWS, Azure, and Google Cloud Platform. Each solution will have its own way of doing things, but they are not all you have available. You can also choose an end-to-end system like xpresso.ai.
Building a machine learning model is usually the scientific part of the equation. The art begins with choosing the right software tools and hardware infrastructure. The reason why software solutions are so important is that you might run into trouble when trying to deploythemto a cloud platform. How popular is that software solution? It is important to answer this question because it will determine how easy it is to get help when you have these problems.
The Right MLOps Pipeline
Ultimately, it will take an entire team of people to make the right choices and deploy your machine learning models to production. As long as you understand the entire process, you will come out way ahead of your competition.
If you are struggling to deploy your machine learning models, it might be worth your time and effort to have an end-to-end solution that can do everything for you. It means your engineers don’t need to be as skilled, and they can focus on building a model and training data. Solutions like xpresso.ai provide this and much more.
