Book Image

Machine Learning in Biotechnology and Life Sciences

By : Saleh Alkhalifa
Book Image

Machine Learning in Biotechnology and Life Sciences

By: Saleh Alkhalifa

Overview of this book

The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You’ll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data. By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP.
Table of Contents (17 chapters)
1
Section 1: Getting Started with Data
6
Section 2: Developing and Training Models
13
Section 3: Deploying Models to Users

Summary

In this chapter, we steered away from the development of models and focused more on how models can be deployed to interact with web applications. We investigated the idea of data transfer via APIs, and we also learned about some of the most common frameworks. We investigated one of the most common Python web application frameworks known as Flask. Using Flask, we developed an NLP summarization model that allows both human users and other web applications to interact with it and use its capabilities. In addition, we learned how to deploy previously trained models, such as those from scikit-learn.

In each of these instances, we launched our models locally as we developed their frameworks and capabilities. In the next chapter, we will make our model available to others by using Docker containers and AWS to deploy our model to the cloud.