The main objective behind building an ML model is to resolve an issue. It is only possible if the ML model is in production and regularly used by consumers. Bridging the gap between data science and IT can help in this regard. Both aspects could be connected if an ML model is deployed easily. This is possible through tools like Kube-Flow, TFX and Mlflow that can streamline the entire method of model deployment.
How to put machine learning models into production?
Data scientists need to assess the methods of putting ML models into production so that they can clearly understand the practices and methods and then go ahead.
- From model to production
Before beginning any project, the ML team should consider three things:
- Storing the data and retrieving it
- The tooling and the frameworks involved
- The iteration after the feedback
- Storage of data and retrieving it
An ML model is useless without data. You will have data sets for training, evaluation, testing and prediction. Now answer the following question
- How is the training data kept?
- What is the size of the data?
- How to retrieve the data for the training?
The probable answers to these could be:
- Data storage can be done on-premise or in cloud storage. The storage could also be done on a hybrid system.
- If you have a large dataset, then additional computing power is required for pre-processing steps and for optimising the model. This means you have to plan the entire process from the beginning.
- Will you opt for batch data retrieval, or will you retrieve data in real-time? You have to do this before defining the ML system. Your prediction data is relative to the training data, and the packaging is also neat.
- Frameworks and tooling
You need frameworks such as Scikit-Learn, Pytorch and Tensorflow for training models. The programming languages used will either be Go, Java or Python. You can even use cloud environments like Azure, GCP or AWS. But which tools would you choose for transferring and continuity of the model?
That can be decided with the help of three factors:
- The efficiency of the framework or production tool. How do they use the memory and CPU for a specific duration?
- The popularity of the tool in the developer community. This means the framework and tool perform well and have much client support.
- Support for the tool or framework. Is it open source or close sourced? How fast can you find tutorials for mastering it for usage in actual projects?
- Iteration and Feedback
ML projects are a part of design and engineering that are extremely critical from the very beginning of the project. You need to know how to get feedback from an in-production mode and how you can set up continuous delivery. This way, you can track the model state and be notified when the model does not produce optimally.
Continuous testing and deployment of new models minus interruptions in the existing model processes are referred to as continuous integration.
Putting an ML model into production is not a complex process. You have to just have strategies and understand what works best for you. If you wish to learn more about these or want adequate infrastructure for your ML project, visit E2E Networks’ website.