Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. This blog post provides a concise session summary, a video, and a written transcript. data science’s emergence as an interdisciplinary field – from industry, not academia. But for most enterprise, using machine learning…not really.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

Analytics 67

Analytics Data Quality Visualization Metadata 67

Rita Sallam

APRIL 2, 2023

We explored these questions and more at our Bake-Offs and Show Floor Showdowns at our Data and Analytics Summit in Orlando with 4,000 of our closest D&A friends and family. The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms.

Optimization 52

Optimization Machine Learning Insurance Risk 52

Cloudera

MAY 22, 2018

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. save the built model container, along with metadata like who built or deployed it. Cloudera Data Science Workbench 1.4.x

Data Science 41

Data Science Snapshot Machine Learning Metadata 41

Alation

AUGUST 30, 2022

Centralization of metadata. A decade ago, metadata was everywhere. Consequently, useful metadata was unfindable and unusable. We had data but no data intelligence and, as a result, insights remained hidden or hard to come by. This universe of metadata represents a treasure trove of connected information.

Metadata 52

Metadata Data Quality Data Governance Machine Learning 52

Cloudera

OCTOBER 11, 2022

This leads to the obvious question – how do you do data at scale ? Al needs machine learning (ML), ML needs data science. Data science needs analytics. And they all need lots of data. And that data is likely in clouds, in data centers and at the edge.

Snapshot 95

Snapshot Data Science Digital Transformation Metadata 95

IBM Big Data Hub

SEPTEMBER 20, 2022

Organizations are increasingly depending upon artificial intelligence (AI) and Machine Learning (ML) to assist humans in decision making. The right data fabric solution will naturally support these pillars and help you build trustworthy AI models. Comprehensive, trusted data sets. Processes that provide AI governance.

Metadata 56

Metadata Machine Learning Data-driven Modeling 56

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML). Capture and document model metadata for report generation.

Risk 71

Risk Modeling Management Metadata 71

Ontotext

FEBRUARY 1, 2024

When our auto-categorization engine, Graphite Knowledge Studio, tags content with metadata derived from taxonomies, we also capture and store this content metadata inside the knowledge graph, thereby linking concepts to the content they describe. What do we mean by content aware knowledge graphs?

Enterprise 59

Enterprise Management Metadata Modeling 59

Cloudera

MAY 12, 2021

Use cases could include but are not limited to: predictive maintenance, log data pipeline optimization, connected vehicles, industrial IoT, fraud detection, patient monitoring, network monitoring, and more. DATA FOR ENTERPRISE AI. The post Announcing the 2021 Data Impact Awards appeared first on Cloudera Blog.

Digital Transformation 70

Digital Transformation Machine Learning Optimization Data Lake 70

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. The post The Future of the Data Lakehouse – Open appeared first on Cloudera Blog.

Data Lake 94

Data Lake Data Warehouse Machine Learning Cost-Benefit 94

Alation

DECEMBER 15, 2022

June 2017: Dresner Advisory Services names Alation the #1 data catalog in its inaugural Data Catalog End-User Market Study. August 2017: Alation debuts as a leader in the Gartner MQ for Metadata Management Solutions. August 2018: Gartner names Alation a 2X Leader in the MQ for Metadata Management Solutions.

Metadata 52

Metadata Data Governance Data Quality Marketing 52

AWS Big Data

MAY 4, 2023

Amazon’s Open Data Sponsorship Program allows organizations to host free of charge on AWS. Over the last decade, we’ve seen a surge in data science frameworks coming to fruition, along with mass adoption by the data science community. Data scientists have access to the Jupyter notebook hosted on SageMaker.

Data Processing 94

Data Processing Metadata Informatics Interactive 94

Cloudera

FEBRUARY 11, 2019

The January 2019 “Magic Quadrant for Data Management Solutions for Analytics” provides valuable insights into the status, direction, and players in the DMSA market. In this blog, we share our takeaways as they relate to the DMSA market trends. Cloudera’s Positioning. Cloudera is positioned furthest for “Completeness of Vision.”

Management 43

Management Metadata Analytics Machine Learning 43

Ontotext

APRIL 15, 2020

Ontotext’s knowledge graph technology is at the core of Cochrane’s data architecture developed by our partners from Data Language. Here, GraphDB is used for storing the ontology models, the vocabulary, the content metadata and the graphs from the PICO ontology.

Publishing 105

Publishing Metadata Data mining Technology 105

Timo Elliott

JANUARY 5, 2022

This is a English translation of an article by Thérèse van Bellinghen that first appeared on the SAP News Blog. . They discussed how medium and small sized enterprises should handle the digital transformation, and the concrete roles of Data Protection Officers and Innovation Evangelists during this process.

Digital Transformation 115

Digital Transformation Machine Learning Enterprise Insurance 115

Alation

FEBRUARY 13, 2020

Making datasets easy to find, understand, and access is the purpose of data curation—a purpose that demands well-described datasets. Data curation is a metadata management activity and data catalogs are essential data curation technology. Who Are the Data Curators? What about Data Stewards?

Metadata 52

Metadata Data Warehouse Data Lake Data Governance 52

Cloudera

JUNE 7, 2022

We deliver cloud-native data analytics across the full data lifecycle – data distribution, data engineering, data warehousing, transactional data, streaming data, data science, and machine learning – that’s portable across infrastructures.

IT 113

IT Data Architecture Unstructured Data Big Data 113

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 brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building.

Machine Learning 138

Machine Learning Data Science Data Lake Modeling 138

Cloudera

SEPTEMBER 28, 2020

The data lifecycle model ingests data using Kafka, enriches that data with Spark-based batch process, performs deep data analytics using Hive and Impala, and finally uses that data for data science using Cloudera Data Science Workbench to get deep insights. on roadmap).

Testing 132

Testing Metadata Risk Data Science 132

Domino Data Lab

JUNE 13, 2019

Domino Data Science Field Notes provide highlights of data science research, trends, techniques, and more, that support data scientists and data science leaders accelerate their work or careers. . “In the same spirit as [Recht et al., ” They also were able to.

Testing 83

Testing Data Science Experimentation Metadata 83

AWS Big Data

JUNE 7, 2023

This is a guest blog post co-written with Sumesh M R from Cargotec and Tero Karttunen from Knowit Finland. Cargotec captures terabytes of IoT telemetry data from their machinery operated by numerous customers across the globe. An AWS Glue job (metadata exporter) runs daily on the source account.

Metadata 55

Metadata Data Lake Machine Learning Big Data 55

Cloudera

AUGUST 31, 2021

It outperforms other data warehouses on all sizes and types of data, including structured and unstructured, while scaling cost-effectively past petabytes. Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. Consideration of both data & metadata in the migration.

Data Warehouse 83

Data Warehouse Cost-Benefit Metadata Data-driven 83

Cloudera

AUGUST 24, 2022

Cloudera Machine Learning (CML) is a cloud-native and hybrid-friendly machine learning platform. It unifies self-service data science and data engineering in a single, portable service as part of an enterprise data cloud for multi-function analytics on data anywhere. Next Steps.

Machine Learning 84

Machine Learning Metadata Testing Data Science 84

Cloudera

AUGUST 30, 2018

Consider the following practices that, until recently, were relegated to the R&D department: Data-driven decision making – the collection and analysis of data to guide decisions that improve success. How do I enable self-service for my rapidly growing data science teams? appeared first on Cloudera Blog.

Enterprise 40

Enterprise Data-driven Digital Transformation Machine Learning 40

CDW Research Hub

APRIL 17, 2019

Collect additional high-fidelity data sources, like endpoint activity and network metadata to drive advanced attack detection. The data sources in this stage unlock a very rich set of detection capabilities. Threat hunters rely on DNS and advanced endpoint data to uncover and track adversaries dwelling in the network.

Analytics 40

Analytics Data-driven Machine Learning Metadata 40

Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities. Research VP, Business Analytics and Data Science.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

Cloudera

DECEMBER 15, 2022

With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Metadata management .

Metadata 80

Metadata Data Warehouse Snapshot Data Quality 80

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks. General concerns.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

Alation

FEBRUARY 13, 2020

We have a BI/BA organization that we wanted to keep focused on the very deep dive, heavy analytics and data science, but we needed to give our business partners the tools so they could do their own reporting, see the successes of their products and see the steps they needed to take. Subscribe to Alation's Blog. Thank you Sharon.

Digital Transformation 52

Digital Transformation Data-driven Business Intelligence Big Data 52

IBM Big Data Hub

OCTOBER 4, 2023

Typically, on their own, data warehouses can be restricted by high storage costs that limit AI and ML model collaboration and deployments, while data lakes can result in low-performing data science workloads. New insights and relationships are found in this combination. All of this supports the use of AI.

Data Lake 78

Data Lake Metadata Cost-Benefit Data Warehouse 78

Cloudera

SEPTEMBER 9, 2022

Cloudera has been supporting data lakehouse use cases for many years now, using open source engines on open data and table formats, allowing for easy use of data engineering, data science, data warehousing, and machine learning on the same data, on premises, or in any cloud.

Metadata 103

Metadata Machine Learning Unstructured Data Data Lake 103

Cloudera

AUGUST 30, 2022

Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Towards Data Science ).

Data Architecture 80

Data Architecture Data Lake Metadata Data Warehouse 80

IBM Big Data Hub

APRIL 27, 2022

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. What’s a data mesh? 11 May 2021. . 3 March 2022.

Management 80

Management Metadata Data Architecture Data Lake 80

Cloudera

OCTOBER 7, 2022

To date, dbt was only available on proprietary cloud data warehouses, with very little interoperability between different engines. For example, transformations performed in one engine are not visible across other engines because there was no common storage or metadata store. CDP Public Cloud via Cloudera Machine Learning.

Data Warehouse 82

Data Warehouse Data Transformation Testing Data Lake 82

IBM Big Data Hub

JULY 17, 2023

In this blog, I will cover: What is watsonx.ai? is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. What capabilities are included in watsonx.ai? What is watsonx.data? How can you get started today?

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

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. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]

Data Warehouse 107

Data Warehouse Machine Learning Cost-Benefit Metadata 107