MindsDB

Check out our latest tutorial on Database Mind! Chris S. teaches us how to leverage our Completions API to answer questions in plain language directly from your database. Check it out now: https://lnkd.in/gvhNhEnn

🧠 Building AI solutions? Discover the 12 key aspects to consider when choosing a large language model in this comprehensive blog post! From model performance to cost and ease of integration, this article covers it all. 🌟 🔗 Read more here: https://s.mdb.ai/3L9cPF7

The time is here! 🚀 Join us on our Month-End Community Call where we'll be showcasing our beta release of Database Minds! Get a sneak peek at our latest features and meet our talented team during the live Q&A session. Register now to secure your spot: https://s.mdb.ai/3W22Zvh

Excellent, simple breakdown on ML. It’s also a great time to mention why it’s critical to have good, clean data in your ecosystem. A machine won’t be able to tell the difference between a yellow circle and a green square if you keep feeding it purple scribbles…

🚀 New MindsDB release is here! 🔹 Fix for duplicate column names 🔹 Migrated OpenAI’s json_struct mode 🔹 Improved Agent logging 🔹 Updated docs for overriding prompt templates 🔹 Requirements fixes 🔹 Updated Postgres handler with schema in table query 🔹 REST API docs 🔹 Python SDK docs for agents 🔹 Fixed LangChain Circular Import Shout out to all the contributors for their hard work! Check out the full changelog here: https://lnkd.in/gaPDu7nu

Exciting news from MindsDB! We’re thrilled to announce the second beta release of Database Mind. Database Mind answers questions in plain language directly from a database. It supports popular databases such as: MySQL, PostgreSQL, Snowflake, Google BigQuery, MariaDB, and ClickHouse. Learn more here: https://lnkd.in/gR9HU_3a In this release, Database Mind: - Adds support for more complex, multi-step conversations using the OpenAI Assistants API We believe that app developers will appreciate a comprehensive API that can support a full conversation, taking questions and returning answers based on data contained in databases and data warehouses. Key Benefits We remove the complexity of: - Building and connecting a reasoning and execution loop - Managing and optimizing an AI app infrastructure stack - New! Maintaining context across a multi-step or complex conversation Coming Soon - Faster responses - Support for streaming responses - More complex database schemas - Rich Logging - More Secure access and authorization options Next Steps We're thrilled to continue improving Database Mind, that can take a question and return plain language answers based on data contained in databases. Explore its capabilities, share your feedback, and help shape our platform's future. Try it now! https://lnkd.in/gR9HU_3a

This past weekend we attended the UC Berkeley AI hackathon 😄 It was incredible seeing the students ship innovative projects! On Saturday we also hit #2 on GitHub's trending list thanks to all the buzz at our booth ✨ A huge shoutout to Berkeley SkyDeck for hosting an amazing event 🚀 Can't wait for the next hackathon! 🌟

How to Pick the Best-Performing Time-Series AI Model for Your Data 1. Time-series models forecast the future using historical data, making them essential for applications in finance, weather forecasting, and more. 2. Different AI frameworks perform differently on various data types, so experimentation is key to finding the best model for your data. 3. Platforms like MindsDB allow you to experiment with models from Nixtla, such as StatsForecast, NeuralForecast, and TimeGPT. By experimenting, you can see how each model performs with your specific data, helping you understand which model fits best. 4. Benchmarking these models to evaluate their performance and accuracy is crucial for ensuring reliable predictions. 5. Last but not least, regularly retrain models with new data to maintain accuracy and adapt to changing patterns. 6. Finding the right AI model is a journey of experimentation and discovery. With the right tools, you can unlock the full potential of your data! 7. To learn more, check out the article below: https://lnkd.in/g6viXh6K

What is machine learning? 🤖 Part 2! Check out Chris S. explaining supervised vs. unsupervised learning. Learn how models use labeled data to recognize patterns or find hidden patterns without labels.

What is machine learning? Part 1 🤖 Check out Chris S. breaking down the basics of machine learning and how it uses data to make predictions and decisions!