Mlops
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A curated collection of papers and articles from companies sharing real-world data science and machine learning applications in production.
An open-source AI engineering platform for debugging, evaluating, monitoring, and optimizing production LLM applications and machine learning models.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A curated index of the latest and best machine learning and AI courses available on YouTube, organized by topic.
A composable, modular, and scalable machine learning toolkit for building AI platforms on Kubernetes.
A curated guide to learning machine learning with Python and Jupyter Notebook, featuring courses, notebooks, and practical resources.
A curated guide to learning machine learning with Python and Jupyter Notebook, featuring hands-on tutorials, courses, and ethical considerations.
A Python framework for creating reproducible, maintainable, and modular data engineering and data science pipelines.
A practical booklet covering the four main steps of designing machine learning systems with 27 interview questions.
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
Deep Lake is a multimodal data lake and vector store optimized for AI, enabling scalable data management, retrieval, and training for LLM and deep learning applications.
A Python library for building production-ready model inference APIs, job queues, and multi-model serving systems for AI applications.
A platform for deploying, managing, and scaling machine learning models in production on AWS infrastructure.
An open-source Python framework to evaluate, test, and monitor ML and LLM systems with 100+ built-in metrics.
A suite of web applications for inspecting and understanding TensorFlow runs and graphs.
An open-source feature store for managing and serving machine learning features for training and online inference.
An open-source MLOps/LLMOps suite for experiment management, data management, pipelines, orchestration, scheduling, and model serving.
An open-source, self-hosted ML experiment tracker with a performant UI and SDK for comparing and querying training runs.
An open-source MLOps platform for building, orchestrating, and deploying production AI pipelines and agents.
An end-to-end framework for building custom AI applications and agents directly integrated with databases.
A compiler that extends SQL with AI capabilities to train, predict, and evaluate machine learning models directly from SQL statements.
An MLOps framework to package, deploy, monitor, and manage thousands of production machine learning models on Kubernetes.
A suite of visual diagnostic tools that extend scikit-learn to steer machine learning model selection through visualizations.
An open-source CLI tool for implementing CI/CD workflows with a focus on MLOps, automating ML experiments and reporting.
An open-source solution for continuous validation of machine learning models and data, from research to production.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
The fastest way to build data pipelines with iterative development and deployment anywhere.
A free course teaching how to design, train, and deploy a production-ready real-time financial advisor LLM system using RAG and LLMOps.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
An open-source machine learning platform for distributed training, hyperparameter tuning, experiment tracking, and resource management.
A command-line tool for creating reproducible, container-based development environments for AI/ML workflows.
An open-source Python toolkit providing a comprehensive collection of algorithms for interpreting and explaining machine learning models and datasets.
MLeap is a portable execution engine for deploying machine learning pipelines from Spark and Scikit-learn without their runtime dependencies.
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