Serverless NLP Model Training

Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matrix factorization and modern approaches, ...  Show more

Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems, the limitations of optimizing purely for accuracy, and how ideas from social ch ...  Show more

Simon is joined by Stephen Liedig to discuss the evolution of serverless technology and its impact on application development, exploring benefits like scalability, cost optimization, and faster dev cycles. They delve into key services and concepts in serverless design, including ...  Show more

Today Simon is joined by Rishabh Ray Chaudhury, Sr. Product Manager, to learn more about SageMaker Serverless Inference. We will dive deep into some of the customer use cases that Serverless Inference solves, key benefits of the feature, and how customers can leverage this SageMa ...  Show more

Today we’re joined by Nir Bar-Lev, co-founder and CEO of ClearML. In our conversation with Nir, we explore how his view of the wide vs deep machine learning platforms paradox has changed and evolved over time, how companies should think about building vs buying and integration, ...  Show more

Machine learning models need updating - what's the reliable way to do it? While in Romania, Richard sat down with Annie Talvasto to talk about her work helping to build DevOps practices around machine learning: Building repeatable processes for data ingestions, cleaning, organ ...