Data plays a key role in training AI models for life sciences. The common saying is “garbage in, garbage out,” which means that the quality of your training data directly impacts the quality of outputs and results you’ll get from your model.
Quality data is the backbone of any high-performance machine learning (ML) model. This, combined with proper training practices, ensures that your model will perform optimally when you begin harnessing it in your operations. Depending on your particular needs, you may need one (or more) types of ML models:
- NLPs – used to process and understand human language.
- Vision – analyzes images and videos.
- Tabular – processes and analyzes traditional structured data.
We’ll be covering the best practices for training AI models for life sciences, from data preparation to training methods, and more.
Machine Learning Workflow Explained
Let’s start by examining the standard procedure that governs AI model training. The ML workflow consists of two phases: the training phase and the inference phase.
The training phase consists of identifying and collecting historical data and sending it through a ML pipeline.. The result of this is a trained AI model. Once the model has been trained, the inference phase begins.
The trained model is deployed within an organization and new data is sent as input into the ML model for inference or scoring. This is done to assess how well the model performs in a real production situation based on real data. The data scientist/s testing the model then evaluates the results for accuracy and quality. It’s important to always have at least one human monitoring the model to assess its results, throughout all phases.
At Saama, we call this keeping a “human in the loop”, ensuring that your model is being monitored, managed, and (if necessary), retrained for optimal and accurate output.
If you’d like to learn more about how our proprietary suite of AI-powered platforms and solutions can benefit your data management operations, book a demo with us.
