Corinium

JUNE 6, 2019

Some of the work is very foundational, such as building an enterprise data lake and migrating it to the cloud, which enables other more direct value-added activities such as self-service. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

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 a variety of AI model development and delivery platforms , as well as packaged AI-based applications.

Cost-Benefit 114

Cost-Benefit Data Lake Software Machine Learning 114

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. The data is also distributed. So, Azure Databricks connects to many different data sources.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

CIO Business Intelligence

MARCH 13, 2024

L’analisi dei dati attraverso l’apprendimento automatico (machine learning, deep learning, reti neurali) è la tecnologia maggiormente utilizzata dalle grandi imprese che utilizzano l’IA (51,9%). Le reti neurali sono il modello di machine learning più utilizzato oggi.

Machine Learning 105

Machine Learning Deep Learning Big Data Testing 105

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. Spotify uses ML models to generate its song recommendations.

Machine Learning 50

Machine Learning Marketing Forecasting Modeling 50

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. Two labeled files have been created for this example.

Insurance 83

Insurance Visualization Data Lake Metrics 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. The workflow begins with an AWS Glue job that converts the CSV files into Apache Parquet data format.

Data Lake 88

Data Lake Deep Learning Modeling Interactive 88

IBM Big Data Hub

AUGUST 1, 2023

True to their name, generative AI models generate text, images, code , or other responses based on a user’s prompt. But what makes the generative functionality of these models—and, ultimately, their benefits to the organization—possible? That’s where the foundation model enters the picture.

Modeling 56

Modeling Cost-Benefit Data Lake Machine Learning 56

Domino Data Lab

APRIL 3, 2019

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. How did the challenges and opportunities related to security, data management, and system architecture get braided together throughout the past ~6 decades of IT?

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

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 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

O'Reilly on Data

OCTOBER 19, 2021

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 342

IT Testing Experimentation Software 342

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. Because it’s common for enterprise software development to leverage cloud environments, many IT groups assume that this infrastructure approach will succeed as well for AI model training.

Cost-Benefit 98

Cost-Benefit Experimentation Data Lake Deep Learning 98

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. RAPIDS on the Cloudera Data Platform comes pre-configured with all the necessary libraries and dependencies to bring the power of RAPIDS to your projects. Data Ingestion.

Machine Learning 138

Machine Learning Data Science Data Lake Modeling 138

AWS Big Data

MAY 16, 2024

H3 can also help create location-based profiling features for predictive machine learning (ML) models such as risk-mitigation models. About Amazon Redshift Thousands of customers rely on Amazon Redshift to analyze data from terabytes to petabytes and run complex analytical queries.

Data Warehouse 109

Data Warehouse Visualization Cost-Benefit Optimization 109

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. The answer is that generative AI leverages recent advances in foundation models. Watsonx.ai

Data Warehouse 108

Data Warehouse Machine Learning Cost-Benefit Metadata 108

Alation

APRIL 14, 2022

Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth. And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. Pushing data to a data lake and assuming it is ready for use is shortsighted.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

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. Consider deploying analytics-as-a-service .

Analytics 115

Analytics Key Performance Indicator Data Warehouse Data-driven 115

DataRobot Blog

MARCH 7, 2023

Organizations that want to prove the value of AI by developing, deploying, and managing machine learning models at scale can now do so quickly using the DataRobot AI Platform on Microsoft Azure. Models trained in DataRobot can also be easily deployed to Azure Machine Learning, allowing users to host models easier in a secure way.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59