How to Put ML Data into Production

How to Put ML Data into Production

May 6, 2021 0 By Stephen Callahan

Typically, there are three vital areas that your team must think about before undertaking any Machine learning project. They include:

Algorithm trading: A mathematical system model can analyze reports and trade outcomes from news agencies and identify some circumstances with the potential to impact the stock prices to change. After that, it can make a decision on whether to sell, hold, or purchase assets or stocks depending on its projections.

Data retaining and retrieval

An ML model cannot help anyone without any data associated with it. You will probably have training, testing, evaluation, and sometimes even prediction datasets. You must come up with the answers for questions like:

  • How will your training dataset be stored?
  • What’s the size of your data?
  • What methods do you have in place to retrieve training data?
  • What methods are you going to use to extract data for prediction?

Answering the said questions guides you on the framework or tools to utilize, the means of solving your problem, and to identify ways to design your machine learning model. You should seriously consider coming up with answers to these questions before embarking on a machine learning project.

Frame works and tooling

You’ll need frameworks and tooling because your model design can’t train, run, and deploy all on its own. The available frameworks are the likes of Tensorflow, Pytorch, and Scikit-Learn to train models. Programing languages include Java, Python, and Go.

When you are done examining and preparing your data utilization, the next significant agenda should be the various types of combination of framework and tools available. At this step, it’s important that you also answer the following questions:

  • What are some of the best available tools to tackle the objective at hand?
  • Are the tools available open-source or closed?
  • How many targets or platforms support the tool?

Feedback and iterations

Machine Learning projects are always evolving. This part of engineering and design must be in the developer’s mind from the beginning. In this step, you should weigh questions like:

  • How will you receive response from a model in production?
  • How do you come up with consistent delivery?

Obtaining response from a working model is essential. It enables you to actively monitor and track the state of the model and warn you in case there’s a decay or depreciation in performance, bias creep, and data drift and skew. Getting this data in real-time helps developers to instantly solve problems before the end user notices them.