mlops-zoomcamp
MLOps training
Teaches practical aspects of productionizing ML services
Free MLOps course from DataTalks.Club
11k stars
185 watching
2k forks
Language: Jupyter Notebook
last commit: about 2 years agomachine-learningmlopsmodel-deploymentmodel-monitoringworkflow-orchestration
Related projects:
| Repository | Description | Stars |
|---|---|---|
 yorko/mlcourse.ai | An online learning platform covering various machine learning topics from basics to advanced algorithms | 9,831 |
 gokumohandas/made-with-ml | Teaches machine learning fundamentals and software engineering practices for building production-ready ML applications | 37,816 |
 dotnet/machinelearning | A cross-platform machine learning framework for .NET that enables developers to build, train, and deploy models without prior expertise in ML. | 9,071 |
 graviraja/mlops-basics | A structured learning series covering MLOps basics from data preparation to deployment and model monitoring. | 6,120 |
 oxid15/cascade | A lightweight MLOps library for small teams and individuals to manage machine learning model development lifecycle | 22 |
 mlflow/mlflow | A platform for managing machine learning projects from inception to deployment | 19,021 |
| dotnet/machinelearning-samples | A collection of samples and examples demonstrating the usage of ML.NET for machine learning tasks in .NET applications. | 4,508 |
 mlpack/mlpack | A C++ machine learning library with bindings to other languages and bindings for multiple programming languages. | 5,151 |
 omegaml/omegaml | An MLOps platform providing tools and services to deploy, collaborate and manage machine learning models and data pipelines in a simplified way | 96 |
 visenger/handson-ml | Teaches Machine Learning fundamentals in Python using Scikit-Learn and TensorFlow | 5 |
 microsoft/ml-for-beginners | A structured curriculum teaching classic machine learning techniques using Python and Scikit-learn, with a focus on practical projects and hands-on exercises. | 70,139 |
 polyaxon/polyaxon | A platform for managing and orchestrating the machine learning lifecycle | 3,581 |
 trekhleb/homemade-machine-learning | Practices implementing popular machine learning algorithms from scratch to gain a deeper understanding of their mathematics | 23,191 |
 jphall663/interpretable_machine_learning_with_python | Teaching software developers how to build transparent and explainable machine learning models using Python | 673 |
 runllm/aqueduct | An MLOps framework that allows developers to define and deploy machine learning workloads on any cloud infrastructure using a Python native API. | 520 |