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train.py / evaluate.py
# Build, evaluate, deploy.
from workbench import RAGPipeline
pipeline = RAGPipeline(
retriever="semantic",
model="your-model"
)
results = pipeline.evaluate(dataset)
print(results.summary())ILLUSTRATIVE EVALUATIONModel quality
Retrieve → Evaluate → Improve
Retrieve → Evaluate → Improve⌘
PythonData, ML & automation
◉
PyTorchDeep learning in practice
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LLMsPrompts, models & agents
⚙
MLOpsReliable AI systems
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RAGKnowledge-driven AI
Services
From idea to production
GenAI & RAG
Build practical AI applications with your data, from retrieval systems to agent workflows.
Explore serviceMLOps & Deployment
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Learn by building
Python
Practical Python for ML
Build the Python foundations for machine learning through data handling, notebooks, and practical projects.
PythonDataNotebooks
RAG
Build RAG Applications
Learn to build retrieval-augmented generation systems with your own data, from ingestion to evaluation.
LLMsRAGVector databases
Deployment
Deploy ML with FastAPI
Turn machine learning models into APIs with testing, containerization, monitoring, and reliable deployment.
FastAPIDockerMLOps
Blog
Notes from the workbench
Machine Learning
Choosing the right embedding model
A practical guide to embedding models, including trade-offs, evaluation methods, and retrieval quality.
MLOps
Deploy your first ML API with FastAPI
Turn a model into a production-ready API with validation, testing, and containerization.
Machine Learning
What model accuracy doesn’t tell you
Go beyond a single number. Explore evaluation metrics, test sets, and practical failure analysis.
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