IBM Journey to AI blog

APRIL 20, 2023

Before a bank can start the process of certifying a risk model, they first need to understand what data is being used and how it changes as it moves from a database to a model. The value of data lineage applies across all industries, but there are three key focuses when you consider it for banking use cases: 1.

Database 86

Database Data Engineering Data Engineering Data Engineering 86

Flipboard

FEBRUARY 13, 2023

Project Structure Creating Our Configuration File Creating Our Data Pipeline Preprocessing Faces: Detection and Cropping Summary Citation Information Building a Dataset for Triplet Loss with Keras and TensorFlow In today’s tutorial, we will take the first step toward building our real-time face recognition application. The dataset.py

Data Pipeline 145

Data Pipeline Deep Learning Deep Learning Python 145

Flipboard

MARCH 6, 2023

In the previous tutorial of this series, we built the dataset and data pipeline for our Siamese Network based Face Recognition application. Specifically, we looked at an overview of triplet loss and discussed what kind of data samples are required to train our model with the triplet loss. Download the code!

Deep Learning 144

Deep Learning Deep Learning Data Pipeline Computer Science 144

IBM Journey to AI blog

DECEMBER 16, 2022

You have a specific book in mind, but you have no idea where to find it. You enter the title of the book into the computer and the library’s digital inventory system tells you the exact section and aisle where the book is located. After all, Alex may not be aware of all the data available to her.

Data Quality 52

Data Quality Data Governance Data Scientist Data Wrangling 52

phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

Alation

JUNE 14, 2023

Anyone building anything net-new publishes to Snowflake in a database driven by the use case and uses our commoditized web-based GUI ingestion framework. Data Pipeline Capabilities This team’s scope is massive because the data pipelines are huge and there are many different capabilities embedded in them.

Data Pipeline 52

Data Pipeline ETL Data Warehouse SQL 52

phData

NOVEMBER 9, 2023

A cloud data warehouse is designed to combine a concept that every organization knows, namely a data warehouse, and optimizes the components of it, for the cloud. If you’d like a more personalized look into the potential of Snowflake for your business, definitely book one of our free Snowflake migration assessment sessions.

Data Warehouse 98

Data Warehouse Data Lakes Cloud Data Clustering 98

AWS Machine Learning Blog

SEPTEMBER 24, 2024

An optional CloudFormation stack to deploy a data pipeline to enable a conversation analytics dashboard. Booking – This demonstrates an example of routing the caller to a live agent queue. Choose an option for allowing unredacted logs for the Lambda function in the data pipeline. Choose Create data source.

AWS 102

AWS AI AI Analytics 102

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Market participants who are receiving either live or historical data feeds need to ingest this data and perform one or more steps, such as parse the message out of a binary protocol, rebuild the limit order book (LOB), or combine multiple feeds into a single normalized format.

AWS 114

AWS ML ML Clustering 114

IBM Journey to AI blog

JULY 1, 2024

Whenever anyone talks about data lineage and how to achieve it, the spotlight tends to shine on automation. This is expected, as automating the process of calculating and establishing lineage is crucial to understanding and maintaining a trustworthy system of data pipelines.

ETL 40

ETL Data Lakes Database Data Pipeline 40

phData

OCTOBER 20, 2023

What is Semi-structured Data? Semi-structured data, also called partially structured data, is a form that does not adhere to the conventional tabular structure found in relational databases or other data tables. Semi-structured data can come from many sources, including applications, sensors, and mobile devices.

Big Data 40

Big Data Big Data Database Hadoop 40

PyImageSearch

JANUARY 8, 2024

We will understand the dataset and the data pipeline for our application and discuss the salient features of the NSL framework in detail. config.py ) The data pipeline (i.e., Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! model.py ) Additionally, the robust.py

Deep Learning 65

Deep Learning Deep Learning Data Pipeline Computer Science 65

phData

SEPTEMBER 27, 2024

. “ This sounds great in theory, but how does it work in practice with customer data or something like a ‘composable CDP’? Well, implementing transitional modeling does require a shift in how we think about and work with customer data. It often involves specialized databases designed to handle this kind of atomic, temporal data.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

AWS Machine Learning Blog

JANUARY 15, 2025

The solution extracts valuable insights from diverse data sources, including OEM transactions, vehicle specifications, social media reviews, and OEM QRT reports. By employing a multi-modal approach, the solution connects relevant data elements across various databases.

AWS 104

AWS SQL AI AI 104

AWS Machine Learning Blog

DECEMBER 18, 2024

To further enrich the dataset, Fastweb generated synthetic Italian data using LLMs. High-quality Italian web articles, books, and other texts served as the basis for training the LLMs to generate authentic-sounding synthetic content that captured the nuances of the language.

Clustering 101

Clustering AWS AI AI 101

Flipboard

APRIL 15, 2025

With new tools and frameworks emerging weekly, its natural to focus on tangible things we can controlwhich vector database to use, which LLM provider to choose, which agent framework to adopt. The real challenge is ensuring your synthetic data actually triggers the scenarios you want to test. This isnt surprising.

AI 166

AI AI Database ML 166