Mlearning.ai

AUGUST 14, 2023

Creating Contextual, Personalized Chatbots with ChatGPT and Redis We’ve all seen how helpful generative AI can be and since the advent of ChatGPT , AI has taken the industry by storm. To achieve this, we harness the power of Redis, a versatile in-memory data structure store, for effective context management and message retrieval.

Database 52

Database AI AI Python 52

ODSC - Open Data Science

JANUARY 10, 2024

Feature Stores in Practice: Build and Deploy a Model with Featureform, Redis, Databricks, and Sagemaker Simba Khadder | Founder & CEO | Featureform This session provides a practical blueprint to efficiently harness feature stores within ML workflows, effectively bridging the chasm between theoretical understanding and actionable implementation.

ML 52

ML ML Deep Learning Deep Learning 52

AWS Machine Learning Blog

MARCH 1, 2024

In this post, we discuss the new feature of hybrid search, which you can select as a query option alongside semantic search. Knowledge Bases for Amazon Bedrock currently supports four vector stores: Amazon OpenSearch Serverless , Amazon Aurora PostgreSQL-Compatible Edition , Pinecone , and Redis Enterprise Cloud.

AWS 107

AWS Machine Learning Machine Learning Database 107

AWS Machine Learning Blog

FEBRUARY 29, 2024

Knowledge Bases for Amazon Bedrock supports multiple vector databases, including Amazon OpenSearch Serverless , Amazon Aurora , Pinecone, and Redis Enterprise Cloud. Yes, there are costs and payments discussed in the search results. Are there any costs or payments?

AWS 112

AWS Machine Learning Machine Learning ML 112

AWS Machine Learning Blog

SEPTEMBER 18, 2023

In this post, we discuss the benefits of using Ray and Amazon SageMaker for distributed ML, and provide a step-by-step guide on how to use these frameworks to build and deploy a scalable ML workflow. Data scientists have to address challenges like data partitioning, load balancing, fault tolerance, and scalability.

Machine Learning 99

Machine Learning Machine Learning ML ML 99

Snorkel AI

JUNE 27, 2023

And then you might want to load them into some key-value store like a database or Redis for faster retrieval. So from an additional perspective, batch prediction looks something like this. First, you generate predictions and you store them in a data warehouse.

AI 52

AI AI Data Warehouse Machine Learning 52

Snorkel AI

JUNE 27, 2023

And then you might want to load them into some key-value store like a database or Redis for faster retrieval. So from an additional perspective, batch prediction looks something like this. First, you generate predictions and you store them in a data warehouse.

AI 52

AI AI Data Warehouse Machine Learning 52

Smart Data Collective

AUGUST 9, 2022

Today we’ll discuss what NoSQL databases are, what types of them exist, as well as their advantages and disadvantages they offer to data-driven businesses. Some of the most popular NoSQL databases currently are: Redis , MongoDB , Couchbase. NoSQL databases are the alternative to SQL databases. Let’s jump right into it.

Database 107

Database SQL Big Data Big Data 107

Flipboard

OCTOBER 9, 2023

For example, containers exist to configure NGINX, Redis, and many other popular tools. Breaking Down the Dockerfile, Step by Step ```Dockerfile FROM python:3.10 Using the FROM command allows you to specify a template pre-existing Docker Image you would like to build your own image on top of.

Machine Learning 116

Machine Learning Machine Learning Deep Learning Deep Learning 116

Becoming Human

MAY 15, 2023

NoSQL Databases are MongoDB , Redis , Cassandra DB , etc. Note: Start Reading Research Paper The Next Section is not mandatory, it’s an optional one for freshers, but as we discussed earlier, Data Science is a very vast field. SQL Databases are MySQL , PostgreSQL , MariaDB , etc.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. But yeah, for the most part.

ML 52

ML ML Data Scientist Machine Learning 52