Smart Data Collective

FEBRUARY 20, 2022

Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps. AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms.

Cost-Benefit 116

Cost-Benefit Data Lake Software Machine Learning 116

IBM Big Data Hub

MAY 9, 2023

Over the past decade, deep learning arose from a seismic collision of data availability and sheer compute power, enabling a host of impressive AI capabilities. models are trained on IBM’s curated, enterprise-focused data lake, on our custom-designed cloud-native AI supercomputer, Vela. All watsonx.ai

Enterprise 135

Enterprise Technology Modeling Cost-Benefit 135

Jen Stirrup

MAY 5, 2020

Azure allows you to protect your enterprise data assets, using Azure Active Directory and setting up your virtual network. Other technologies, such as Azure Data Factory, can help process large amounts of data around in the cloud. So, Azure Databricks connects to many different data sources. Azure Data Lake Store.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

IBM Big Data Hub

JUNE 14, 2023

Traditional AI tools, especially deep learning-based ones, require huge amounts of effort to use. You need to collect, curate, and annotate data for any specific task you want to perform. Sometimes the problem with artificial intelligence (AI) and automation is that they are too labor intensive.

Modeling 95

Modeling Data Lake Enterprise Deep Learning 95

IBM Big Data Hub

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 Marketing Forecasting Modeling 52

AWS Big Data

JUNE 26, 2023

Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data.

Insurance 76

Insurance Visualization Data Lake Metrics 76

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

AWS Big Data

MARCH 1, 2023

Finding similar columns in a data lake has important applications in data cleaning and annotation, schema matching, data discovery, and analytics across multiple data sources. In this example, we searched for columns in our data lake that have similar Column Names ( payload type ) to district ( payload ).

Data Lake 81

Data Lake Deep Learning Modeling Interactive 81

CIO Business Intelligence

MARCH 9, 2022

However, they do contain effective data management, organization, and integrity capabilities. As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Warehouse, data lake convergence. Meet the data lakehouse.

Data Lake 118

Data Lake Unstructured Data Data Warehouse Data Quality 118

KDnuggets

DECEMBER 14, 2021

We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.

Data Science 157

Data Science Machine Learning Analytics Data Lake 157

CIO Business Intelligence

MAY 23, 2022

The traditional approach for artificial intelligence (AI) and deep learning projects has been to deploy them in the cloud. Different companies approach this from different angles, and some will naturally gravitate to cloud, based on where their data sets are created and live,” he says.

Cost-Benefit 98

Cost-Benefit Experimentation Data Lake Deep Learning 98

Domino Data Lab

APRIL 3, 2019

Data coming from machines tends to land (aka, data at rest ) in durable stores such as Amazon S3, then gets consumed by Hadoop, Spark, etc. Somehow, the gravity of the data has a geological effect that forms data lakes. DG emerges for the big data side of the world, e.g., the Alation launch in 2012.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

IBM Big Data Hub

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

Modeling 61

Modeling Cost-Benefit Data Lake Machine Learning 61

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. Data Ingestion. The raw data is in a series of CSV files. Introduction. For AWS this means at least P3 instances. P2 GPU instances are not supported.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

O'Reilly on Data

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. Compute.

IT 346

IT Testing Experimentation Software 346

CIO Business Intelligence

MARCH 22, 2022

Which type(s) of storage consolidation you use depends on the data you generate and collect. . One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Just starting out with analytics?

Analytics 114

Analytics Key Performance Indicator Data Warehouse Data-driven 114

IBM Big Data Hub

MAY 9, 2023

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. A data store built on open lakehouse architecture, it runs both on premises and across multi-cloud environments.

Data Warehouse 110

Data Warehouse Cost-Benefit Machine Learning Modeling 110

Alation

APRIL 14, 2022

Pushing data to a data lake and assuming it is ready for use is shortsighted. Organizations launched initiatives to be “ data-driven ” (though we at Hired Brains Research prefer the term “data-aware”).

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Cloudera

FEBRUARY 4, 2019

Stream Processing – Manage and process multiple streams of real-time data using the most advanced distributed stream processing system – Apache Kafka. Process millions of real-time messages per second to feed into your data lake or for immediate streaming analytics.

IoT 73

IoT Prescriptive Analytics Digital Transformation Internet of Things 73

AWS Big Data

NOVEMBER 15, 2023

With new capabilities for self-service and straightforward builder experiences, you can democratize data access for line of business users, analysts, scientists, and engineers. Hear also from Adidas, GlobalFoundries, and University of California, Irvine.

Data Lake 65

Data Lake Data Warehouse B2B Deep Learning 65

DataRobot Blog

MARCH 7, 2023

The DataRobot AI Platform seamlessly integrates with Azure cloud services, including Azure Machine Learning, Azure Data Lake Storage Gen 2 (ADLS), Azure Synapse Analytics, and Azure SQL database. The capability to rapidly build an AI-powered organization with industry-specific solutions and expertise.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59