PostgresML is an open-source library available in Python, Javascript, and SQL, extending PostgreSQL into a machine learning platform, allowing you to train models and make predictions using SQL right inside your database. #AILibraryOfTheDay
PostgresML 🐘 extends PostgreSQL into a complete platform for classical machine learning and AI. It enables AI engineers to train and deploy models directly in their database using standard SQL queries. Its main benefit is eliminating the complex microservice infrastructure typically needed for model management.
1️⃣ GPU-powered inference for low latency predictions and streaming response support from large language models like GPT-3
2️⃣ Manage open source ML models from HuggingFace 🤗 and track experiment results
3️⃣ Train tabular data on 50+ algorithms like random forests 🌳 and neural networks 🧠
4️⃣ Generate and index vector embeddings for text search, recommendations, etc
5️⃣ Horizontal scalability to millions of predictions per second utilizing PostgreSQL's reliability and tooling
By consolidating your model data pipeline into PostgreSQL, PostgresML streamlines MLOps. You can go from training to production deployment with simple SQL, keeping your models close to your data and application for faster insights. 🚀
Overall, PostgresML simplifies machine learning infrastructure by leveraging PostgreSQL's mature data management capabilities. It's like bringing your models directly into your database.
- ✅ Simplifies infrastructure - Consolidates the entire machine learning pipeline from data access to model deployment in PostgreSQL, eliminating complex microservices. 🛠️
- 🔎 Unified interface - Allows training, deployment, and predictions via simple SQL queries instead of disjoint APIs and platforms. 👩💻
- ⚡️ Low latency - GPU acceleration and vector indexes provide faster response and throughput compared to HTTP predictions. ⚡️
- 🧮 Reduces costs - Decreases reliance on external services by hosting models directly in your data warehouse. 💰
- 🔐 Enhances security - Keep your data, models, and predictions within your private network without external calls. 🔒
In summary, PostgresML streamlines end-to-end MLOps by extending PostgreSQL into a machine learning platform. This tight integration offers AI engineers simplicity, speed, cost savings, and control over their ML infrastructure.
- 👷🏽♀️ Builders: Lev Kokotov, Montana Low, Santi Adavani, Daniel Illenberger
- 👩🏽💼 Builders on LinkedIn: https://www.linkedin.com/in/levkk/, https://www.linkedin.com/in/montanalow/, https://www.linkedin.com/in/daniel-illenberger-0021094b/, https://www.linkedin.com/in/adavanisanti/
- 👩🏽🏭 Builders on X: https://twitter.com/montanalow, https://twitter.com/adavanisanti, https://twitter.com/DPChillenberger
- 👩🏽💻 Contributors: 39
- 💫 GitHub Stars: 4.7k
- 🍴 Forks: 205
- 👁️ Watch: 48
- 🪪 License: MIT
- 🔗 Links: Below 👇🏽
- GitHub Repository: https://github.com/postgresml/postgresml
- Official Website: https://postgresml.org/
- LinkedIn Page: https://www.linkedin.com/company/postgresml/
- Discord Server: https://discord.com/invite/DmyJP3qJ7U
- X Page: https://twitter.com/postgresml
- Profile in The AI Engineer: https://github.com/theaiengineer/awesome-opensource-ai-engineering/blob/main/libraries/postgresml/README.md
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