Made-With-ML
ML development guide
Teaches machine learning fundamentals and software engineering practices for building production-ready ML applications
Learn how to design, develop, deploy and iterate on production-grade ML applications.
38k stars
1k watching
6k forks
Language: Jupyter Notebook
last commit: 3 months ago
Linked from 1 awesome list
data-engineeringdata-qualitydata-sciencedeep-learningdistributed-mldistributed-trainingllmsmachine-learningmlopsnatural-language-processingpythonpytorchray
kelvins/awesome-mlopsRelated projects:
| Repository | Description | Stars |
|---|---|---|
 zuzoovn/machine-learning-for-software-engineers | A structured study plan to help software developers learn machine learning and become a machine learning engineer | 28,167 |
 dotnet/machinelearning | A cross-platform machine learning framework for .NET that enables developers to build, train, and deploy models without prior expertise in ML. | 9,035 |
| dotnet/machinelearning-samples | A collection of samples and examples demonstrating the usage of ML.NET for machine learning tasks in .NET applications. | 4,490 |
 microsoft/ml-for-beginners | A structured learning platform providing a comprehensive curriculum for beginners in machine learning and related data science topics | 69,811 |
 chiphuyen/machine-learning-systems-design | A resource guide covering the four main steps of designing a machine learning system: project setup, data pipeline, modeling, and serving. | 9,156 |
 mlflow/mlflow | A platform to manage the entire machine learning lifecycle, from experiment tracking to model deployment. | 18,781 |
 datatalksclub/mlops-zoomcamp | Teaches practical aspects of productionizing ML services | 11,154 |
 alirezadir/production-level-deep-learning | A guide to building production-ready deep learning systems for real-world applications | 4,351 |
 trekhleb/homemade-machine-learning | Practices implementing popular machine learning algorithms from scratch to gain a deeper understanding of their mathematics | 23,121 |
 iterative/cml | Automates machine learning workflows and generates reports on every pull request. | 4,038 |
 eriklindernoren/ml-from-scratch | Provides implementations of fundamental machine learning models and algorithms from scratch in Python | 24,003 |
 ml5js/ml5-library | Makes machine learning algorithms accessible to web developers | 6,494 |
 dair-ai/ml-papers-explained | An explanation of key concepts and advancements in the field of Machine Learning | 7,315 |
 ml-tooling/opyrator | Automates conversion of machine learning code into production-ready microservices with web API and GUI. | 3,102 |
| ml-tooling/ml-workspace | An all-in-one web-based IDE for machine learning and data science | 3,434 |