Learn how to build, package, deploy and serve machine learning (ML) models on the cloud. During this session, you will first deploy a simple “pickled” model, followed by a more involved Hugging Face Transformers-based model. ...
Learn how to build and deploy a semantic search engine, first for text documents and later for images. This series of hands-on sessions will touch upon Python programming, need-to-know concepts about embeddings, vector DBs, machine learning, and cloud deployment. ...
Master MLOps fundamentals in this 2-part session. Learn model lifecycle best practices like versioning, monitoring and explainability. Then, explore deployment, optimization and governance techniques to keep models performing over time in complex environments. Walk away with practical skills to successfully manage machine learning applications from development through production. ...
Learn how to build a YouTube video summarizer and host it on a website. This series of hands-on sessions will touch upon Python programming, need-to-know concepts about data engineering, machine learning, serverless computing, cost estimation, and cloud deployment. Delivery Mode Online & In Person Duration 3 - 4 hours Prerequisites Containerization 101, Serverless Computing What Will Be Covered In this series of 3-4 sessions, you will build a web-based application in which users can indicate the URL of a YouTube video and, a few seconds later, get a summary of the key points mentioned in that video....
Active learning is a type of machine learning in which the learning algorithm actively selects the data it learns from, rather than passively accepting the data that is given to it. This is done by querying a user or other information source for labels on unlabelled data points. It is very useful when labelled data is scarce or expensive to obtain, as it can achieve high accuracy levels with a relatively small amount of labelled data. ...