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⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Sample code and notebooks for Generative AI on Google Cloud, with Gemini platform
- generative-ai
- llm
- vertex-ai
- langchain
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Kubernetes Community Documentation
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
The Hunting ELK
- hunting
- elasticsearch
- kibana
- logstash
🛠 All-in-one web-based IDE specialized for machine learning and data science.
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.
- data-science
- python
- dag
- data-engineering
food image to recipe with deep convolutional neural networks.
- convolutional-neural-networks
- keras
- chef
- deep-learning
Docker container for Kafka - Spark Streaming - Cassandra
Big Data essentials: Hadoop, MapReduce, Spark. Explore tutorials and demos in Jupyter notebooks—most are self-contained and live, ready to run with a click.
Various Jupyter notebooks about Common Crawl data
- jupyter-notebook
- common-crawl
- aws-athena
- webarchiving
CNN, Caffe, LaMem,Azure