AI is in its early days of adoption and platform realization. Despite the noise surrounding model benchmarks, context window sizes, and chat interfaces, the real platform shift hasn’t happened yet.
In this post we’re going to talk about GraphRag — one of the techniques we can use to add knowledge to the context of a large language model prompt. We'll go through the concepts, theory, and end with a video walk-through of a fully functional full-stack GraphRAG application.
I recently added a DGX Spark to my lab and have been putting it through its paces. In this video, I'll unpack what the DGX Spark actually is, why NVIDIA built it, and who it’s really for (and who it isn’t for).
Enterprise success with Generative AI requires more than building models—it demands a cross-functional team that unites innovation, governance, and ROI. Learn how to structure full-stack AI teams that deliver scalable, sustainable business impact.
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.