Improving Accuracy of LLM ApplicationsSystematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.Lamini, Meta
Advanced Retrieval for AI with ChromaLearn advanced retrieval techniques to improve the relevancy of retrieved results. Learn to recognize poor query results and use LLMs to improve queries.Chroma
Efficiently Serving LLMsUnderstand how LLMs predict the next token and how techniques like KV caching can speed up text generation. Write code to serve LLM applications efficiently to multiple users.Predibase
Reinforcement Learning From Human FeedbackGet an introduction to tuning and evaluating LLMs using Reinforcement Learning from Human Feedback (RLHF) and fine-tune the Llama 2 model.Google Cloud
LLMOpsLearn LLMOps best practices as you design and automate steps to fine-tune and deploy an LLM for a specific task.Google Cloud
Introducing Multimodal Llama 3.2Try out the features of the new Llama 3.2 models to build AI applications with multimodality.Meta
Evaluating and Debugging Generative AILearn MLOps tools for managing, versioning, debugging, and experimenting in your ML workflow.Weights & Biases
Finetuning Large Language ModelsDiscover when to use finetuning vs prompting for LLMs. Select suitable open-source models, prepare data, and train & evaluate for your specific domain.Lamini
Prompt Engineering for Vision ModelsLearn prompt engineering for vision models using Stable Diffusion, and advanced techniques like object detection and in-painting. Comet
