AI Articles

Richmond Alake, Staff Developer Advocate for AI and ML at MongoDB, teaches you how to put the concepts you learned in the first part of the two part series into practice.

Adel, the VP of Media at DataCamp, walks you through coding workflows with ChatGPT o1-preview, going over multiple use-cases, highlighting how it differs from other models on the market today, and when you should use o1 vs other ChatGPT models.

Explore the role of LLMs for coding tasks, focusing on hands-on examples that demonstrate effective prompt engineering techniques to optimize code generation.

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.

Maxime, one of the world's leading thinkers in generative AI research, shows you how to fine-tune the Llama 3 LLM using Python and the Hugging Face platform.

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.

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, 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.