Data Science Blog

MAY 20, 2024

It offers full BI-Stack Automation, from source to data warehouse through to frontend. It supports a holistic data model, allowing for rapid prototyping of various models. It also supports a wide range of data warehouses, analytical databases, data lakes, frontends, and pipelines/ETL.

Data Pipeline 262

Data Pipeline Data Warehouse Azure Data Lakes 262

Cambridge Intelligence

OCTOBER 19, 2023

That’s why our data visualization SDKs are database agnostic: so you’re free to choose the right stack for your application. There have been a lot of new entrants and innovations in the graph database category, with some vendors slowly dipping below the radar, or always staying on the periphery.

Database 59

Database Azure Analytics Analytics 59

IBM Journey to AI blog

MARCH 29, 2024

GraphQL GraphQL is a query language and API runtime that Facebook developed internally in 2012 before it became open source in 2015. Each schema specifies the types of data the user can query or modify, and the relationships between the types. GraphQL is defined by API schema written in the GraphQL schema definition language.

Data Profiling 94

Data Profiling Database Data Modeling Data Models 94

The MLOps Blog

JANUARY 11, 2023

The result of this assessment process led to conceptualizing and designing a framework that offers an environment for building, managing, and automating processes or workflows with which the data, models, and code Ops based on the needs of individuals and across teams can be realized.

AWS 52

AWS Machine Learning Machine Learning ML 52

Flipboard

JULY 31, 2023

Nowadays, with the advent of deep learning and convolutional neural networks, this process can be automated, allowing the model to learn the most relevant features directly from the data. Model Training: With the labeled data and identified features, the next step is to train a machine learning model.

Machine Learning 123

Machine Learning Machine Learning Deep Learning Deep Learning 123

AWS Machine Learning Blog

OCTOBER 11, 2024

This post dives deep into Amazon Bedrock Knowledge Bases , which helps with the storage and retrieval of data in vector databases for RAG-based workflows, with the objective to improve large language model (LLM) responses for inference involving an organization’s datasets. The LLM response is passed back to the agent.

Database 104

Database AWS Clustering Data Lakes 104