OpenAI Model Selection and Integrations
OpenAI publishes several large language models (LLMs), each optimized for different use cases. This course teaches you how to select the right model to balance performance, accuracy, and cost.
What you'll learn
OpenAI offers customers access to their world-leading large language models and APIs via a straightforward REST interface. The developer's challenge lies in choosing the right model for the right use case. In this course, OpenAI Model Selection and Integrations, you’ll gain deep familiarity with the OpenAI model family and which to choose, when. First, you’ll differentiate the models themselves. Next, you’ll discover how to balance performance, accuracy, and cost. Finally, you’ll understand how context windows work and how to optimize your applications for the least cost and maximum effectiveness. When you’re finished with this course, you’ll have the skills and knowledge of the OpenAI models needed to provide generative AI power to your line-of-business solutions in a performant and cost-effective manner.
Table of contents
- Overview 1m
- The OpenAI Model Family 4m
- Continuous Model Upgrade 1m
- GPT Models and Their Use Cases 3m
- Get Access to the OpenAI APIs 1m
- Factors Affecting OpenAI LLM Speed 2m
- OpenAI Tokenizer 3m
- OpenAI Consumption-based Pricing 2m
- Demo: Address the OpenAI APIs Directly 11m
- Demo: Experiment with the OpenAI Playground 7m
- Demo: Explore OpenAI Pricing 1m
- For Further Learning 1m
- Summary 1m
About the author
Timothy Warner is a Microsoft Most Valuable Professional (MVP) in Cloud and Datacenter Management who is based in Nashville, TN. His professional specialties include Microsoft Azure, cross-platform PowerShell, and all things Windows Server-related. You can reach Tim via Twitter (@TechTrainerTim), LinkedIn or his blog, AzureDepot.com.