O'Reilly Media

OCTOBER 19, 2021

This introduces further requirements: The scale of operations is often two orders of magnitude larger than in the earlier data-centric environments. Not only is data larger, but models—deep learning models in particular—are much larger than before.

ML 145

ML ML Data Scientist AWS 145

IBM Journey to AI blog

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. models are trained on IBM’s curated, enterprise-focused data lake. That’s where the foundation model enters the picture. All watsonx.ai

AI 70

AI AI Machine Learning Machine Learning 70

Pickl AI

AUGUST 9, 2024

Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Students should learn about the architecture of data warehouses and how they differ from traditional databases.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

DagsHub

NOVEMBER 11, 2024

Creating multimodal embeddings means training models on datasets with multiple data types to understand how these types of information are related. Multimodal embeddings help combine unstructured data from various sources in data warehouses and ETL pipelines.

AI 52

AI AI Data Lakes Database 52

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

IBM Journey to AI blog

OCTOBER 16, 2023

Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions. Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection. The platform has three powerful components: the watsonx.ai

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Alation

APRIL 14, 2022

Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. Pushing data to a data lake and assuming it is ready for use is shortsighted. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning.

Data Governance 59

Data Governance ML ML Cloud Data 59

AWS Machine Learning Blog

OCTOBER 24, 2024

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. You can build and manage an incremental data pipeline to update embeddings on Vectorstore at scale. LLMs are incredibly flexible. You can choose a wide variety of embedding models.

AWS 116

AWS Data Pipeline Database Big Data 116

AWS Machine Learning Blog

NOVEMBER 1, 2024

Instead, a core component of decentralized clinical trials is a secure, scalable data infrastructure with strong data analytics capabilities. Amazon Redshift is a fully managed cloud data warehouse that trial scientists can use to perform analytics.

AWS 112

AWS Data Lakes Machine Learning Machine Learning 112