This course is designed for individuals at both an intermediate and beginner level, including data scientists, AI enthusiasts, and professionals seeking to harness the power of Azure for Large Language Models (LLMs). Tailored for those with foundational programming experience and familiarity with Azure basics, this comprehensive program takes you through a four-week journey. In the first week, you'll delve into Azure's AI services and the Azure portal, gaining insights into large language models, their functionalities, and strategies for risk mitigation. Subsequent weeks cover practical applications, including leveraging Azure Machine Learning, managing GPU quotas, deploying models, and utilizing the Azure OpenAI Service. As you progress, the course explores nuanced query crafting, Semantic Kernel implementation, and advanced strategies for optimizing interactions with LLMs within the Azure environment. The final week focuses on architectural patterns, deployment strategies, and hands-on application building using RAG, Azure services, and GitHub Actions workflows. Whether you're a data professional or AI enthusiast, this course equips you with the skills to deploy, optimize, and build robust large-scale applications leveraging Azure and Large Language Models.

In this advanced course, you will gain practical expertise in scaling data engineering systems using cutting-edge tools and techniques. This course is designed for data scientists, data engineers, and anyone with a foundational understanding of data handling who desires to escalate their skills to handle larger, more complex datasets efficiently.

Throughout the course, you'll master the application of technologies such as Celery with RabbitMQ for scalable data consumption, Apache Airflow for optimized workflow management, and Vector and Graph databases for robust data management at scale. The course will culminate with hands-on projects that offer real-world experience, where you'll put your acquired skills to test in solving data engineering challenges. You will not only learn to create scalable data systems but also to analyze their performance and make necessary adjustments for optimum results. This invaluable experience in advanced data engineering techniques will prepare you for the demanding tasks of handling massive datasets, streamlining complex workflows, and optimizing data operations for businesses of any scale.

This course will teach you how to deploy and manage large language models (LLMs) in production using AWS services like Amazon Bedrock. By the end of the course, you will know how to:

Choose the right LLM architecture and model for your application using services. Optimize cost, performance and scalability of LLMs on AWS using auto-scaling groups, spot instances and container orchestration Monitor and log metrics from your LLM to detect issues and continuously improve quality Build reliable and secure pipelines to train, deploy and update models using AWS services Comply with regulations when deploying LLMs in production through techniques like differential privacy and controlled rollouts This course is unique in its focus on real-world operationalization of large language models using AWS. You will work through hands-on labs to put concepts into practice as you learn. Whether you are a machine learning engineer, data scientist or technical leader, you will gain practical skills to run LLMs in production.

By the end of this course, a learner will master Databricks to perform data engineering and data analytics tasks for data science workflows. Additionally, a student will learn to master running local large language models like Mixtral via Hugging Face Candle and Mozilla llamafile.

Learn the fundamentals of large language models (LLMs) and put them into practice by deploying your own solutions based on open source models. By the end of this course, you will be able to leverage state-of-the-art open source LLMs to create AI applications using a code-first approach.

You will start by gaining an in-depth understanding of how LLMs work, including model architectures like transformers and advancements like sparse expert models. Hands-on labs will walk you through launching cloud GPU instances and running pre-trained models like Code Llama, Mistral, and stable diffusion. The highlight of the course is a guided project where you will fine-tune a model like LLaMA or Mistral on a dataset of your choice. You will use SkyPilot to easily scale model training on low-cost spot instances across cloud providers. Finally, you will containerize your model for efficient deployment using model servers like LoRAX and vLLM. By the end of the course, you will have first-hand experience leveraging open source LLMs to build AI solutions. The skills you gain will enable you to further advance your career in AI.