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 / about 2 years ago

Would you like additional capabilities like connecting to Machine Learning, Dashboards and Quicksight and leveraging other tools like Comprehend. - Source: dev.to / 11 months ago

Once again, I asked ChatGPT to perform this analysis. I could have used some of the AI tools provided by AWS, like the detectSentiment API from Amazon Comprehend, but tools like ChatGPT make it so easy to just add a simple "also, tell me in one word what the sentiment is" clause to a query I'm asking. - Source: dev.to / about 1 year ago

And now we can run amplify push to create the resources in AWS. The AWS service that will be used for this functionality is Amazon Comprehend. The pricing for this service can be found here. - Source: dev.to / about 1 year ago

Amazon has developed its own NLP service called Amazon Comprehend, which is designed to extract insights and relationships from unstructured text data. Source: about 1 year ago

First, can you use a different AWS service, such as Comprehend or SageMaker? You only "pay for what you use" instead of paying for an idle server. This is especially helpful for a start up, since you don't pay a lot if you don't have a lot of customers.. Source: over 1 year ago