Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

Alation

FEBRUARY 13, 2020

In an earlier blog, I defined a data catalog as “a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.”.

Metadata 52

Metadata Management Data Lake Data Governance 52

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

Octopai

APRIL 19, 2021

Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story. Donna Burbank. IRM UK Connects.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

Rocket-Powered Data Science

MARCH 10, 2020

And this: perhaps the most powerful node in a graph model for real-world use cases might be “context”. How does one express “context” in a data model? After all, the standard relational model of databases instantiated these types of relationships in its very foundation decades ago: the ERD (Entity-Relationship Diagram).

Metadata 250

Metadata Machine Learning ROI Prescriptive Analytics 250

CIO Business Intelligence

MARCH 6, 2024

The new feature, which Claire Cheng, vice president of machine learning and AI engineering at Salesforce said was in the works last month , has been launched as the Prompt Builder and has been made generally available.

IT 122

IT Metadata Interactive Enterprise 122

Rocket-Powered Data Science

JULY 19, 2023

The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage. One could say that sentinel analytics is more like unsupervised machine learning, while precursor analytics is more like supervised machine learning.

Data-driven 186

Data-driven Enterprise Analytics Machine Learning 186

Alation

JUNE 2, 2022

These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Currently, models are managed by modelers and by the software tools they use, which results in a patchwork of control, but not on an enterprise level. Machine Learning Technology. from cognilytica.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

AWS Big Data

FEBRUARY 29, 2024

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. Enterprises are doing this by using proprietary data with approaches like Retrieval Augmented Generation (RAG), fine-tuning, and continued pre-training with foundation models.

Data Governance 98

Data Governance Unstructured Data Metadata Data Lake 98

Ontotext

APRIL 19, 2024

We mainly talked about the company’s Metadata Studio and the types of features it has that give users the options I’ve listed above. NLP and the role of metadata-oriented feedback loops Moreover, text analysis or mining can add to that meaning and contribute even more to the knowledge in the graph.

Metadata 52

Metadata Statistics Interactive Enterprise 52

Smart Data Collective

APRIL 5, 2022

They are using tools like Amazon SageMaker to take advantage of more powerful machine learning capabilities. Amazon SageMaker is a hardware accelerator platform that uses cloud-based machine learning technology. IBM Watson Studio is a very popular solution for handling machine learning and data science tasks.

Cost-Benefit 123

Cost-Benefit Machine Learning Data Science Unstructured Data 123

O'Reilly on Data

JANUARY 8, 2019

In a recent O’Reilly survey , we found that the skills gap remains one of the key challenges holding back the adoption of machine learning. For most companies, the road toward machine learning (ML) involves simpler analytic applications. Sustaining machine learning in an enterprise.

IoT 204

IoT Machine Learning Internet of Things Data Science 204

CIO Business Intelligence

OCTOBER 13, 2023

Application data architect: The application data architect designs and implements data models for specific software applications. Analytics/data science architect: These data architects design and implement data architecture supporting advanced analytics and data science applications, including machine learning and artificial intelligence.

Data Architecture 132

Data Architecture Data Warehouse Statistics Visualization 132

AWS Big Data

JULY 14, 2023

Defining and capturing a business capability model If an enterprise doesn’t have a system to capture the business capability model, consider defining and finding a way to capture the model for better insight and visibility, and then map it with digital assets like APIs. It keeps evolving with business requirements and usage.

Modeling 70

Modeling Visualization Reporting Measurement 70

Ontotext

MAY 2, 2024

Another challenge is the data models that represent how insights and analytics teams see the world. These models are as important to companies as their frontline products and determine how data is managed, consumed, combined, joined, and analyzed. Here we talk about metadata management, catalog of catalogs, and so on.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Ontotext

DECEMBER 1, 2023

So, KGF 2023 proved to be a breath of fresh air for anyone interested in topics like data mesh and data fabric , knowledge graphs, text analysis , large language model (LLM) integrations, retrieval augmented generation (RAG), chatbots, semantic data integration , and ontology building. Abstract art and knowledge graphs: embracing your mess!

Metadata 78

Metadata Sales Consulting Enterprise 78

Cloudera

JUNE 7, 2023

By now, almost everyone across the tech landscape has heard of the Zero Trust (ZT) security model, which assumes that every device, application, or user attempting to access a network is not to be trusted (see NIST definitions below). But as models go, the idea is easier than the execution.

Metadata 83

Metadata Data Lake Optimization Modeling 83

CIO Business Intelligence

DECEMBER 13, 2023

and applying and enriching metadata helps organizations take a big step toward innovating with generative AI. Digitizing relevant physical assets and objects, such as those core samples, IT equipment, office equipment, etc.

Unstructured Data 98

Unstructured Data IoT Metadata Manufacturing 98

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. To build the AIgent, I started with synopses and metadata from 100,000 books.

Modeling 59

Modeling Metadata Publishing Sales 59

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. But these organizations need to be able to trust their AI/ML models before they can be operationalized and used in crucial business processes. Is it scalable?

Metadata 57

Metadata Machine Learning Data-driven Modeling 57

IBM Big Data Hub

APRIL 28, 2022

By using metadata-enriched AI and a semantic knowledge graph for automated data enrichment, a data fabric continuously identifies and connects data from disparate data stores to discover relevant relationships between the available data points. How does a data fabric impact the bottom line?

Metadata 87

Metadata Data Warehouse Forecasting Predictive Modeling 87

Cloudera

AUGUST 26, 2021

Ozone natively provides Amazon S3 and Hadoop Filesystem compatible endpoints in addition to its own native object store API endpoint and is designed to work seamlessly with enterprise scale data warehousing, machine learning and streaming workloads. Ozone Namespace Overview. Data ingestion through ‘s3’. Create External Hive table.

Data Science 112

Data Science Forecasting Metadata Machine Learning 112

CIO Business Intelligence

MARCH 24, 2023

The Data Governance Institute defines it as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

Data Governance 142

Data Governance Management Metadata Data Quality 142

Rita Sallam

APRIL 2, 2023

The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms. Here is the link to Alteryx’s Data Science and Machine Learning Bake-Off video. We did two Bake-Offs this year. In 2000, the Netherlands had 8.5

Optimization 52

Optimization Machine Learning Insurance Risk 52

CIO Business Intelligence

AUGUST 7, 2023

With data, an organization can do super powerful things with AI and machine learning, like building models that do everything from enhancing fraud detection capabilities to identifying gaps in the market for the organization to separate from the pack.

Metadata 97

Metadata Risk Software Enterprise 97

erwin

JANUARY 30, 2020

However, more than 50 percent say they have deployed metadata management, data analytics, and data quality solutions. erwin Named a Leader in Gartner 2019 Metadata Management Magic Quadrant. All of these factors have an impact on a well-defined data integration model. Most have only data governance operations.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

Ontotext

FEBRUARY 1, 2024

We as a taxonomy community are facing rapid technological change and grappling with where our discipline fits into a world captivated by ChatGPT and other generative models. The challenge of complexity Knowledge models for taxonomy have responded to an increasing need for sophistication and expressiveness.

Enterprise 59

Enterprise Management Metadata Modeling 59

Octopai

MAY 31, 2022

A data catalog uses your data ecosystem’s metadata to build a complete picture of every data asset: definition, calculation, access level, stewardship roles, and previews. Bonus tool: active metadata management. An active metadata management tool adds a maître d’ to self-serve analytics. when compared to a DIY version.

Management 52

Management Metadata Business Intelligence ROI 52

DataRobot Blog

MARCH 16, 2023

This integration, which leverages the ChatGPT model in Azure OpenAI, provides a conversational AI experience that will allow you to interact with and interpret model results and predictions directly. For example, generating code to prepare data as well as train and deploy a model.

Data Science 98

Data Science Technology Data-driven Modeling 98

IBM Big Data Hub

JUNE 26, 2023

It’s like having a conversation with a very smart machine. Generative AI uses an advanced form of machine learning algorithms that takes users prompts and uses natural language processing (NLP) to generate answers to almost any question asked. What is generative AI? in 2022 and it is expected to be hit around USD 118.06

Risk 72

Risk Deep Learning Metadata Modeling 72

CIO Business Intelligence

APRIL 29, 2022

Software development, once solely the domain of human programmers, is now increasingly the by-product of data being carefully selected, ingested, and analysed by machine learning (ML) systems in a recurrent cycle. Many organisations focus too heavily on fine tuning their computational models in their pursuit of ‘quick-wins.’

Data Governance 145

Data Governance IT Risk Data Lake 145

AWS Big Data

JANUARY 9, 2024

Now users seek methods that allow them to get even more relevant results through semantic understanding or even search through image visual similarities instead of textual search of metadata. Traditional lexical search, based on term frequency models like BM25, is widely used and effective for many search applications.

Cost-Benefit 94

Cost-Benefit Visualization Modeling Machine Learning 94

Cloudera

AUGUST 3, 2021

Risk modeling was once a qualitative guess, now modeling can leverage enterprise data to deliver quantitative assessments from a larger and more diverse data set. Point solutions promote themselves as being specialists in specific business process areas and the preferred solution – data warehousing, machine learning as examples.

Analytics 109

Analytics Digital Transformation Risk Forecasting 109

Cloudera

MARCH 17, 2023

The table format provides the necessary structure for the unstructured data that is missing in a data lake, using a schema or metadata definition, to bring it closer to a data warehouse. Some of the popular table formats are Apache Iceberg, Delta Lake, Hudi, and Hive ACID. Also, the data lake layer is not limited to cloud object stores.

Unstructured Data 62

Unstructured Data Data Lake Data Warehouse Machine Learning 62

IBM Big Data Hub

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. Customer-facing AI use cases Deliver superior customer service Customers can now be assisted in real time with conversational AI.

Cost-Benefit 89

Cost-Benefit Insurance Unstructured Data Machine Learning 89

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. You must detect when the model has become stale, and retrain it as necessary. The Marketing team built the first model, but because it was from marketing, the model optimized for CTR and lead conversion.

Marketing 362

Marketing Experimentation Metrics Testing 362

IBM Big Data Hub

JULY 6, 2022

starts at the data source, collecting data pipeline metadata across key solutions in the modern data stack like Airflow, dbt, Databricks and many more. Moreover, mean time to repair (MTTR) is also improved as contextual metadata helps data engineers focus on the source of the problem, rather than debugging where the problem stems from.

Metadata 57

Metadata Data Quality Snapshot Cost-Benefit 57