DataKitchen

APRIL 13, 2021

Testing and Data Observability. Process Analytics. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Testing and Data Observability.

Testing 300

Testing Machine Learning Consulting Data Quality 300

BizAcuity

APRIL 6, 2022

But without deep research on the customer base and the industry, such an endeavor would be quite risky. Crypto casinos need a solid operation strategy, an analytics platform and a fail-safe mechanism in place. Moreover, there should be a powerful data management and analytics pipeline for operational usage.

Data Analytics 52

Data Analytics Analytics B2B Deep Learning 52

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. Data integration and cleaning.

Machine Learning 255

Machine Learning Data Quality Statistics Modeling 255

CIO Business Intelligence

JUNE 13, 2022

And the chatbot would be able to understand what you were asking, run analytics on your purchases, and give you a total. In some parts of the world, companies are required to host conversational AI applications and store the related data on self-managed servers rather than subscribing to a cloud-based service. IT Leadership

Data Processing 87

Data Processing Deep Learning Insurance Cost-Benefit 87

IBM Big Data Hub

NOVEMBER 14, 2023

Data monetization strategy: Managing data as a product Every organization has the potential to monetize their data; for many organizations, it is an untapped resource for new capabilities. But few organizations have made the strategic shift to managing “data as a product.”

Strategy 106

Strategy Cost-Benefit Data-driven Measurement 106

Smart Data Collective

OCTOBER 13, 2021

As we have already said, the challenge for companies is to extract value from data, and to do so it is necessary to have the best visualization tools. Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields).

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

Machine Learning 73

Machine Learning Manufacturing Cost-Benefit Deep Learning 73

Jedox

JULY 31, 2018

Now, we will take a deeper look into AI, Machine learning and other trending technologies and the evolution of data analytics from descriptive to prescriptive. Analytic Evolution in Enterprise Performance Management. Advanced analytics responds to next-generation requirements. advanced analytics.

Technology 44

Technology Prescriptive Analytics Machine Learning Business Intelligence 44

Cloudera

NOVEMBER 19, 2020

Too often, though, legacy systems cannot deliver the needed speed and scalability to make these analytic defenses usable across disparate sources and systems. For many agencies, 80 percent of the work in support of anomaly detection and fraud prevention goes into routine tasks around data management.

Machine Learning 117

Machine Learning Data-driven Modeling Deep Learning 117

Ontotext

FEBRUARY 26, 2024

From a technological perspective, RED combines a sophisticated knowledge graph with large language models (LLM) for improved natural language processing (NLP), data integration, search and information discovery, built on top of the metaphactory platform. Market sentiment analysis : Events can significantly influence market sentiment.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Sisense

AUGUST 6, 2020

Today, we’re developing AI in an era where data is treated as code, or at least as an extension of code, because the code alone cannot achieve deep learning without the data. Moreover, training data must conform to the app design to such an extent that data preparation resembles coding.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Cloudera

FEBRUARY 4, 2019

Process millions of real-time messages per second to feed into your data lake or for immediate streaming analytics. Streaming Analytics – Analyze millions of streams of data in real-time using advanced techniques such as aggregations, time-based windowing, content-filtering etc.,

IoT 73

IoT Prescriptive Analytics Digital Transformation Internet of Things 73

CDW Research Hub

MAY 28, 2021

As we move forward, hybrid cloud continues to be the data storage strategy that helps organizations gain cost-effectiveness and increase data mobility between on-premises, public cloud and private cloud without compromising data integrity. Investment in AI.

Optimization 52

Optimization Deep Learning Enterprise Software 52

AWS Big Data

JUNE 26, 2023

Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data. These sources are often related but use different naming conventions, which will prolong cleansing, slowing down the data processing and analytics cycle. This will open the ML transforms page.

Insurance 83

Insurance Visualization Data Lake Metrics 83

Ontotext

NOVEMBER 6, 2019

This new version sees the release of essential plug-ins: data virtualization, a sophisticated ranking of semantic search results, new types of indexing and advanced graph analytics. GraphDB has always been compliant with W3C standards and an active member of the semantic web community. The Plugins.

Experimentation 45

Experimentation Deep Learning Software Modeling 45

Domino Data Lab

APRIL 3, 2019

The longer answer is that in the context of machine learning use cases, strong assumptions about data integrity lead to brittle solutions overall. We keep feeding the monster data. We find ways to improve machine learning so that it requires orders of magnitude more data, e.g., deep learning with neural networks.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84