Benefits of ML What is possible? We look at what biotech manufacturers can achieve with ML projects and why they should seize the opportunity.
Removing Barriers to Adoption If it was easy everyone would have done it. There are challenges to ML rollouts, but they can be overcome with the right approach.
Real-world Use Cases Results matter. We show what is possible with case studies from biotech customers that show the business impact of ML.
Yet, in manufacturing, there is so often a choice to be made between speed, quality, and cost. In the biotech space, achieving fast, high-quality, low-cost manufacturing is even more of a challenge with complex processes and regulatory requirements to consider. However, Machine Learning (ML) offers an answer.
In our latest whitepaper, we give a comprehensive guide to implementing machine learning models to make a tangible difference for biotech manufacturers. Focusing on the example of improving outputs by introducing mid-manufacture quality control, we cover everything from selecting the right approach to getting a model up and running.
With real-world case studies to demonstrate the results that can be achieved, it is a complete overview of potential problems of ML adoption and, more importantly, how to solve them.
.png?width=250&height=55&name=Untitled%20design%20(69).png "Untitled design (69)")
.png?width=729&height=596&name=Untitled%20design%20(92).png "Untitled design (92)")
