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. Mixed approach of DV 2.0

Data Pipeline 262

Data Pipeline Data Warehouse Azure Data Lakes 262

AWS Machine Learning Blog

JANUARY 24, 2025

This workflow provides secure data handling across multiple AWS accounts while maintaining customer control over sensitive information using customer managed encryption keys. Therefore, data will not be available to model providers for them to improve their base models. The following code is a sample resource policy.

AWS 117

AWS AI AI Machine Learning 117

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 Models Data Modeling 94

Cambridge Intelligence

OCTOBER 19, 2023

Multi-model databases combine graphs with two other NoSQL data models – document and key-value stores. RDF vs property graphs Another way to categorize graph databases is by their data structure. RDF vs property graphs Another way to categorize graph databases is by their data structure.

Database 59

Database Azure Analytics Analytics 59

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

The MLOps Blog

NOVEMBER 30, 2022

Starting from AlexNet with 8 layers in 2012 to ResNet with 152 layers in 2015 – the deep neural networks have become deeper with time. Deeper networks mean increased hyperparameters, more experiments, and in turn more model information to save in a form that can be easily retrieved when needed.

ML 52

ML ML Machine Learning Machine Learning 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