Always know what to expect from your data.
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Updated
Jun 11, 2024 - Python
Always know what to expect from your data.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
A common interface for registering, validating and auditing machine learning artifacts
The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt
AI Observability & Evaluation
The Open Source Feature Store for Machine Learning
Turns Data and AI algorithms into production-ready web applications in no time.
ML/AI meta-model, used in MLRun/Iguazio/Nuclio, see qgate-sln-<solution>
An orchestration platform for the development, production, and observation of data assets.
MLRun/Iguazio/Nuclio quality gate solution.
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Lineage metadata API, artifacts streams, sandbox, API, and spaces for Polyaxon
Serve, optimize and scale PyTorch models in production
A high-throughput and memory-efficient inference and serving engine for LLMs
Label Studio is a multi-type data labeling and annotation tool with standardized output format
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