Flipboard

NOVEMBER 24, 2023

JuMa is tightly integrated with a range of BMW Central IT services, including identity and access management, roles and rights management, BMW Cloud Data Hub (BMW’s data lake on AWS) and on-premises databases. Furthermore, the notebooks can be integrated into the corporate Git repositories to collaborate using version control.

ML 153

ML ML AWS AI 153

IBM Journey to AI blog

JULY 17, 2023

Visual modeling: Delivers easy-to-use workflows for data scientists to build data preparation and predictive machine learning pipelines that include text analytics, visualizations and a variety of modeling methods.

AI 75

AI AI Machine Learning Machine Learning 75

Pickl AI

OCTOBER 10, 2024

It supports batch and real-time data processing, making it a preferred choice for large enterprises with complex data workflows. Informatica’s AI-powered automation helps streamline data pipelines and improve operational efficiency.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

phData

DECEMBER 4, 2023

If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. Knowing this, you want to have data prepared in a way to optimize your load. It might be tempting to have massive files and let the system sort it out.

Clustering 98

Clustering Database SQL Data Pipeline 98

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 Machine Learning Machine Learning 52

Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production.

SQL 52

SQL ML ML Python 52

The MLOps Blog

MAY 9, 2023

The pipelines are interoperable to build a working system: Data (input) pipeline (data acquisition and feature management steps) This pipeline transports raw data from one location to another. Model/training pipeline This pipeline trains one or more models on the training data with preset hyperparameters.

ML 98

ML ML Machine Learning Machine Learning 98

DataRobot Blog

OCTOBER 3, 2017

This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance. 2) When data becomes information, many (incremental) use cases surface. We look at data as an asset, regardless of whether the use case is AML/fraud or new revenue.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52