Understanding Machine Learning Model Training
The point of machine learning is to get a model into production. A machine learning model is simply the result of feeding data into an algorithm to enable that algorithm to make predictions about what will happen in the future. Model training involves giving meaning to data and then using an algorithm to get meaning from that data.
The ML model training process is quite intricate, meaning that there are plenty of ways that things can go around. Training machine learning models is a major piece of the puzzle in building these types of programs. They have a significant effect on what happens in your program. Model training is the most important part of the data science development lifecycle. This is where the rubber truly meets the road. If you can train a machine learning model successfully, you can be confident that it will be quite useful in the future.
Why Is Model Training Important
You cannot get anywhere in machine learning without effective model training. The quality of your machine learning model depends on how well it is trained. On top of that, the training phase must be repeated multiple times before it can be effective. You don’t just do the model training process once.
It is something you continually do to improve the results you get from everything. It also allows you to figure out whether your intuition about which algorithm to choose was correct or not. This gives a lot of people the information they need to get effective results. It will also be one of the best parts of building a machine learning model.
How to Do Machine Learning Model Training Well
There is a systematic way to do machine learning model training well. It allows you to utilize all your available resources, meaning you can be confident in your results.
The first step is to identify the problem you are trying to solve. This is typically a stage that many businesses fail at. They focus on how great building a machine learning model is instead of actually solving a problem. ML model training means nothing if you don’t end up solving a real problem for your business.
The next step is to split the data set and select algorithms to test. Data is the most important part of the puzzle in the training process. The quality of your results will depend on how good your data is. The next part of that puzzle is choosing an algorithm to test. The algorithms you use will depend on how good your data is. Model training on bad data or algorithms will result in bad things for your program.
You also have the opportunity to tune your parameters and your model. There are various awesome algorithms to choose which will depend on the monitoring process you go through and what you are trying to achieve. Either way, this part of the data science development lifecycle is where a lot of people can stumble as well.
Optimizing and Making Your Model Better
Ultimately, you also need to focus on choosing the best model. The model training process will probably lead to multiple different models for you to put into your application. The key is to choose the machine learning model that is the most accurate. You also want to have practical considerations of the deployment stage as well. This gives you awesome results that can boost your business. Otherwise, you might be one of the many statistics about failed machine learning initiatives.