Videos - Predictum Inc.

CMC, SVEM, Neural Networks, DoE and Complexity: It’s All About Prediction
41:07

In this video, Predictum’s Philip Ramsey explores the advanced capabilities of SVEM Machine Learning with Patricia McNeill of Lunbeck Pharmaceuticals through a joint case study. During this time, they explore the problem of complexity in analytical work along the CMC pathway and how the S-VEM methodology provides practitioners with high accuracy predictions for small experimental data sets.

Leveraging Machine Learning and AI in R&D, Engineering and Science
51:50

In this video, we will give an overview of advanced capabilities for addressing the operational challenges faced by engineers, scientists, and researchers with the increased use of machine learning and artificial intelligence.

Major Advantages in Building Predictive Models from DOEs and Other Small Data Sets
37:01

Computing has made possible new methods for analyzing designed experiments and other low-data, multiple column data sets. Dr. Philip Ramsey, Sr. Data Scientist at Predictum, reviews the method here.

Modernizing Development Practices for Applications that Use JSL
44:43

This is a recording of a BrightTalk session which explores how Predictum’s development team has modernized its development environment for building analytical applications, which are based on JMP software. Scripters who work with JMP Scripting Language (JSL) will learn best practices to enable them to modernize their own development environment.

Facilitating CMC Development and Regulatory Filings with Phase Appropriate QbD
41:23

Pharmaceutical and biotechnology companies are under increasing pressure to reduce timelines and costs to speed up the delivery of safe and high-quality products to patients. Even though timelines may change, the activities within the CMC regulatory pathway must be fulfilled and it is essential to look at the CMC program holistically and in early development.

Enhancing and Extending JMP’s Process Screening Platform
22:48

JMP Add-Ins are extremely practical for users who are required to perform a complex set of tasks in a routine manner. To help these types of users in a client project, we were challenged with extending and customizing the Process Screening platform in JMP software to make it more comprehensive for our client KLA’s new product development application. The Process Screening platform was customized, allowing it to be simplified in some respects and extended in other ways.

CMC, SVEM, Neural Networks, DoE and Complexity: It’s All About Prediction
41:07

Leveraging Machine Learning and AI in R&D, Engineering and Science
51:50

Major Advantages in Building Predictive Models from DOEs and Other Small Data Sets
37:01

Modernizing Development Practices for Applications that Use JSL
44:43

Facilitating CMC Development and Regulatory Filings with Phase Appropriate QbD
41:23

Enhancing and Extending JMP’s Process Screening Platform
22:48

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CMC, SVEM, Neural Networks, DoE and Complexity: It’s All About Prediction 41:07

Leveraging Machine Learning and AI in R&D, Engineering and Science 51:50

Major Advantages in Building Predictive Models from DOEs and Other Small Data Sets 37:01

Modernizing Development Practices for Applications that Use JSL 44:43

Facilitating CMC Development and Regulatory Filings with Phase Appropriate QbD 41:23

Enhancing and Extending JMP’s Process Screening Platform 22:48

1,000+ scientists, engineers, and analysts read our monthly briefing to scale analytical programs in their organizations

CMC, SVEM, Neural Networks, DoE and Complexity: It’s All About Prediction
41:07

Leveraging Machine Learning and AI in R&D, Engineering and Science
51:50

Major Advantages in Building Predictive Models from DOEs and Other Small Data Sets
37:01

Modernizing Development Practices for Applications that Use JSL
44:43

Facilitating CMC Development and Regulatory Filings with Phase Appropriate QbD
41:23

Enhancing and Extending JMP’s Process Screening Platform
22:48