Microsoft is a leader in enterprise-focused AI and Data Science tools and platforms. However, it can be confusing to choose between services that have overlapping feature sets. What factors are important to consider when choosing between Fabric and Azure Machine Learning?
Explore Azure Machine Learning's selection of pre-trained models from leading sources like OpenAI, Microsoft, and Hugging Face. In this video I'll guide you through the process of choosing, deploying a model on Azure's computing services, and accessing it through a REST API.
Leverage Azure OpenAI in a Fabric Jupyter notebook to summarize user review data, securely store secrets in Azure Key Vault, and handle data in Fabric Lakehouse’s Delta tables.
This video shows you how to use Azure AI sentiment analysis machine learning models within a Fabric Jupyter notebook to add user sentiment features to Data Lake tables.
We can leverage Azure AI services directly in Spark notebooks to enrich and transform data using Language AI Services. Learn how to use these powerful services using a Python Jupyter notebook stored in a Fabric workspace.
Azure AI Document Intelligence can read images and PDF scans of forms, extracting data for later use in data solutions. While various language SDKs are available, it's also possible to call these services directly using the REST API. This tutorial walks through the REST API process.
Last week Google shipped the first version of its Gemini large language model and made APIs available to developers. In this post we'll build a fully functional Gemini Chatbot using Streamlit!
This post demonstrates how to use Retrieval Augmented Generation (RAG) to query OpenAI's Large Language Model (LLM) using a Vector database. This technique leverages the language understanding and summarization capabilities of Generative AI while introducing semantic understanding of our own data.
This post builds on the concepts in a previous post by exporting an Azure AI Custom Vision model to run within an iOS application.