MindsDB VS Embeddinghub

Install MindsDB locally or sign up for the MindsDB Cloud account. - Source: dev.to / about 2 months ago

Step 1: Create a MindsDB Cloud Account, If you already haven't done so. - Source: dev.to / 7 months ago

You check out MindsDB by signing up for a demo account. If you would like to learn more you can visit MindsDB's Documentation. If you want to contribute to MindsDB, visit their Github repository and if you like it give it a star. MindsDB has a vibrant Slack Community and amazing team that provides technical support, if you would like to join you can sign up here. - Source: dev.to / 8 months ago

Using Large Language Models in your database can help improve your product by helping you gain insights from data, make relevant predictions, understand user behavior, and generate contextually relevant human-like content. MindsDB allows you to build AI applications fast by simplifying the processes of using ML models inside your database. The models are designed to be production ready by default without the need... - Source: dev.to / 9 months ago

MindsDB provides all users with a free MindsDB Cloud version that they can access to generate predictions on their database. You can sign up for the free MindsDB Cloud Version by following the setup guide. Verify your email and log into your account and you are ready to go. Once done, you should be seeing a page like this :. - Source: dev.to / about 1 year ago

Featureform is a virtual feature store. It enables data scientists to define, manage, and serve their ML model's features. Featureform sits atop your existing infrastructure and orchestrates it to work like a traditional feature store. By using Featureform, a data science team can solve the organizational problems:. Source: almost 2 years ago

Usually embeddings — dense numerical representations of real-world objects and relationships, expressed as a vector — are stored in database servers such as PostgreSQLEmbedding. However Embeddinghub makes it easier to store your embeddings and load them. You can get started with minimal setup, and it also makes your code look less verbose as compared to, say, building a KNN model using scikit-learn. - Source: dev.to / almost 2 years ago