Machine Learning

Suman Debnath, Principal Developer Advocate for Machine Learning at AWS, guides you through the essentials of running machine learning workflows on AWS.

Two Microsoft experts teach you how to get started with Microsoft Fabric. You'll learn about common use cases, how to build Fabric into your data workflows, and get tips on how to be productive with Fabric.

Shingai Manjengwa, the Head of AI Education at Theoriq (ChainML Labs), will discuss the principles of responsible AI and demonstrate how they may be implemented in a world with multiple collaborating agents.

Aimée, a Learning Solutions Architect at DataCamp, takes you through how to use Claude. You'll get prompt engineering tips, see a data analysis workflow, and learn how to generate Python and SQL code with Claude.

Roie, a Staff Developer Advocate at Pinecone, shows you how to build an enterprise-quality RAG application.

Andrea, a Computing Engineer at CERN, and Josep, a Data Scientist at the Catalan Tourist Board, will walk you through the steps needed to customize the open-source Mistral LLM.

Richie, a Senior Data Evangelist at DataCamp, shows you how to use the function calling features in the OpenAI API and GPT-4o to create a simple AI assistant that has a natural language interface.

Michelle Conway, Lead Data Scientist at Lloyds Banking Group, will walk you through a simple machine learning example on banking data, including feature engineering, training a model, making predictions, and assessing model performance.

In this code-along, Richie shows you how to use the OpenAI API and GPT-4o to create an AI assistant for data science tasks.

In this session Francesca Donadoni, a Curriculum Manager at DataCamp, shows you how to make use of ChatGPT to implement a simple machine learning workflow.

In this session, you'll learn how to create a simple deep learning model using PyTorch Lightning. You'll work through the steps of defining and training the model, making predictions, and evaluating model performance.

Find out how you can spin up a pay-as-you-go instance of OpenAI, and you’ll build a few simple applications that show the power of LLMs and GenAI against your data.