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End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

evidentlyai / evidently

Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

higgsfield-ai / higgsfield

Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters

hemansnation / AI-Engineer-Headquarters

A collection of scientific methods, processes, algorithms, and systems to build stories & models.

apache / hamilton

Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

GoogleCloudPlatform / vertex-ai-samples

Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.

statmike / gcp-mlops

Google Cloud Platform Vertex AI end-to-end workflows for machine learning operations

superlinked / VectorHub

Deprecated historical repo. Superlinked now develops SIE, a self-hosted inference engine for embeddings, reranking, OCR, extraction, and document processing.

zorost / AI-Engineering-Lab

A free, self-paced 24-week AI engineering course: Python, machine learning, LLMs, RAG, fine-tuning, agents and MCP, Azure and Vertex and Bedrock, and Databricks. 43 runnable notebooks, one continuous case study. MIT licensed, no signup. By Zorost Intelligence AI Lab.

Bennett-MLOps-Lab / SCSE3040-Lab

Lab Assignment Files

mhmd-81 / mlops

just to learn