Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Domino Data Lab

MARCH 20, 2019

Our mission at Domino is to enable organizations to put models at the heart of their business. Today we’re announcing two major new capabilities in Domino that make model development easier and faster for data scientists. This pain point is magnified in organizations with teams of data scientists working on numerous experiments.

Management 97

Management Experimentation Data Science Modeling 97

CIO Business Intelligence

NOVEMBER 4, 2022

The company’s multicloud infrastructure has since expanded to include Microsoft Azure for business applications and Google Cloud Platform to provide its scientists with a greater array of options for experimentation. Google created some very interesting algorithms and tools that are available in AWS,” McCowan says.

Data Lake 124

Data Lake IT Experimentation Data-driven 124

IBM Big Data Hub

OCTOBER 4, 2022

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. Multiple unsupported tools for building and deploying models. Consistent principles guiding the design, development, deployment and monitoring of models are critical in driving responsible, trustworthy AI.

Metadata 97

Metadata Risk Management Risk Experimentation 97

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. Using ML models to search more effectively brought the search space down to 102—which can run on modest hardware. Model-Driven Data Queries.

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

DataRobot Blog

MARCH 16, 2023

Why model-driven AI falls short of delivering value Teams that just focus model performance using model-centric and data-centric ML risk missing the big picture business context. We are also thrilled to share the innovations and capabilities that we have developed at DataRobot to meet and exceed those requirements.

Experimentation 98

Experimentation Data-driven Modeling Metadata 98

AWS Big Data

OCTOBER 4, 2023

An Amazon DataZone domain contains an associated business data catalog for search and discovery, a set of metadata definitions to decorate the data assets that are used for discovery purposes, and data projects with integrated analytics and ML tools for users and groups to consume and publish data assets.

Metadata 119

Metadata Data Lake Publishing Data Governance 119

datapine

JANUARY 31, 2022

They’re about having the mindset of an experimenter and being willing to let data guide a company’s decision-making process. To do so, the company started by defining the goals, and finding a way to translate employees’ behavior and experience into data, so as to model against actual outcomes.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

CIO Business Intelligence

DECEMBER 10, 2024

Companies in various industries are now relying on artificial intelligence (AI) to work more efficiently and develop new, innovative products and business models. We encourage our teams to experiment with different AI models and platforms and explore new application fields. The games industry is no exception. The KAWAII frontend.

Data-driven 98

Data-driven Metadata Interactive KPI 98

Occam's Razor

APRIL 8, 2013

Let's listen in as Alistair discusses the lean analytics model… The Lean Analytics Cycle is a simple, four-step process that shows you how to improve a part of your business. Another way to find the metric you want to change is to look at your business model. The business model also tells you what the metric should be.

Metrics 157

Metrics Analytics KPI Key Performance Indicator 157

Sisense

JULY 24, 2019

NLQ serves those users who are in a rush, or who lack the skills or permissions to model their data using visualization tools or code editors. Last, and still a very painful challenge for most users, is the familiarity with the underlying data and data model.

Analytics 89

Analytics Experimentation Metadata Big Data 89

CIO Business Intelligence

MARCH 21, 2022

The more high-quality data available to data scientists, the more parameters they can include in a given model, and the more data they will have on hand for training their models. It doesn’t conform to a data model but does have associated metadata that can be used to group it. Semi-structured data falls between the two.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Data Science 137

IBM Big Data Hub

MAY 9, 2023

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation to become business critical for many organizations. Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models.

Metadata 71

Metadata Risk Experimentation Dashboards 71

Cloudera

JUNE 6, 2019

While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake. Now the admins need to synchronize multiple copies of the data and metadata and ensure that users across the many clusters are not viewing stale information.

Metadata 86

Metadata Data Lake Optimization Strategy 86

Domino Data Lab

JUNE 13, 2019

Many data scientists and researchers have used the MNIST test set of 10,000 samples for training and testing models for over 20 years. 2018 , 2019 ], the rediscovery of the 50,000 lost MNIST test digits provides an opportunity to quantify the degradation of the official MNIST test set over a quarter-century of experimental research.”

Testing 83

Testing Data Science Experimentation Metadata 83

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.

Visualization 119

Visualization Cost-Benefit Modeling Machine Learning 119

Ontotext

MARCH 29, 2021

Vassil Momtchev: RDF-star (formerly known as RDF*) helps in every case, where the user needs to express a complex relationship with metadata associated for a triple like: 1. << Technically speaking, RDF-star is the syntactic sugar, which makes it easier to attach metadata to edges in the graph. source :TheNationalEnquirer ; 3.

Metadata 59

Metadata IT Modeling Experimentation 59

CIO Business Intelligence

MARCH 6, 2023

Advancements in analytics and AI as well as support for unstructured data in centralized data lakes are key benefits of doing business in the cloud, and Shutterstock is capitalizing on its cloud foundation, creating new revenue streams and business models using the cloud and data lakes as key components of its innovation platform.

Data Lake 88

Data Lake Cost-Benefit Recreation/Entertainment Unstructured Data 88

Ontotext

MAY 18, 2020

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data. Certainly not!

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

AWS Big Data

MAY 2, 2023

Removal of experimental Smart Sensors. This feature is particularly useful if you want to externally process various files, evaluate multiple machine learning models, or extraneously process a varied amount of data based on a SQL request. Apache Airflow v2.4.3 Airflow v2.4.0 Smart Sensors were added in v2.0 and have now been removed.

Testing 131

Testing Experimentation Management Metadata 131

AWS Big Data

JUNE 12, 2024

By using infrastructure as code (IaC) tools, ODP enables self-service data access with unified data management, metadata management (data catalog), and standard interfaces for analytics tools with a high degree of automation by providing the infrastructure, integrations, and compliance measures out of the box.

Data Architecture 116

Data Architecture Cost-Benefit Data-driven Experimentation 116

AWS Big Data

JULY 20, 2023

Amazon SageMaker is used to build, train, and deploy a range of ML models. Additionally, SageMaker training jobs are employed for training the models. After the models are trained, they are deployed and used to identify anomalies and alert customers in real time to potential security threats.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

IBM Big Data Hub

JANUARY 17, 2023

Furthermore, a global effort to create new data privacy laws, and the increased attention on biases in AI models, has resulted in convoluted business processes for getting data to users. The automated metadata generation is essential to turn a manual process into one that is better controlled. AI is no longer experimental.

Data-driven 56

Data-driven Enterprise Data Governance Data Science 56

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. A wheel should be a standardized part that you don’t have to think twice about before you incorporate it into a new car model. Let’s take a look. Six DataOps best practices.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

Cloudera

JANUARY 11, 2021

And we want to model it quickly with some historic customer usage data…and oh yeah, it should be about 100TB, per day.”. Provides a pay-as-you-go model. . Experimental and production workloads access the same data without users impacting each others’ SLAs. We have this new data set, actually it is sensor data.

Data Warehouse 63

Data Warehouse Data Lake Cost-Benefit Machine Learning 63

AWS Big Data

JULY 3, 2023

With scalable metadata indexing, Apache Iceberg is able to deliver performant queries to a variety of engines such as Spark and Athena by reducing planning time. Our model portfolio will buy stocks that are added to the index, known as going long, and will sell an equivalent amount of stocks removed from the index, known as going short.

Snapshot 105

Snapshot Data Lake Testing Strategy 105

Ontotext

DECEMBER 30, 2022

9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. Metadata Studio – our new product for streamlining the development and operation of solutions involving text analysis. Ontotext develops re-usable domain models as pre-packaged knowledge graphs.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

AWS Big Data

JULY 26, 2023

This enables you to process a user’s query to find the closest vectors and combine them with additional metadata without relying on external data sources or additional application code to integrate the results. We recognize that many of you are in the experimentation phase and would like a more economical option for dev-test.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 364

Marketing Experimentation Metrics Testing 364

CIO Business Intelligence

AUGUST 5, 2024

Healthcare Domain Expertise: It cannot be said enough that anyone developing AI-driven models for healthcare needs to understand the unique use cases and stringent data security and privacy requirements – and the detailed nuances of how this information will be used – in the specific healthcare setting where the technology will be deployed.

Experimentation 124

Experimentation Risk Metadata Data-driven 124

CIO Business Intelligence

FEBRUARY 14, 2024

That’s not just about the cost of preparing a larger data set than you need, which takes expertise that’s still uncommon and commands a high salary, but also what you’re teaching the model. Do you want to have an even more powerful search capability with AI in your data, and to be unsure about how you’ve organized that data?”

IT 143

IT Metadata Data-driven Management 143

O'Reilly on Data

JULY 25, 2023

I’m a professor who is interested in how we can use LLMs (Large Language Models) to teach programming. Here’s how I worked on it: I subscribed to ChatGPT Plus and used the GPT-4 model in ChatGPT (first the May 12, 2023 version, then the May 24 version) to help me with design and implementation.

Consulting 246

Consulting Interactive Software Metadata 246

Cloudera

SEPTEMBER 17, 2020

A large oil and gas company was suffering over not being able to offer users an easy and fast way to access the data needed to fuel their experimentation. To address this, they focused on creating an experimentation-oriented culture, enabled thanks to a cloud-native platform supporting the full data lifecycle.

Data Warehouse 81

Data Warehouse Experimentation Dashboards Visualization 81

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

AWS Big Data

MAY 9, 2025

Representative use case The following are common scenarios where PyIceberg can be particularly useful: Data science experimentation and feature engineering In data science, experiment reproducibility is crucial for maintaining reliable and efficient analyses and models. For more details, see Getting started with AWS CloudShell.

Snapshot 91

Snapshot Analytics Data-driven Data Processing 91

O'Reilly on Data

APRIL 15, 2025

Even small UX decisionslike where to place metadata or which filters to exposecan make the difference between a tool people actually use and one they avoid. The most successful teams flip this model by giving domain experts tools to write and iterate on prompts directly. Our model suffers from hallucination issues.

Experimentation 332

Experimentation Testing Metrics Measurement 332

CIO Business Intelligence

MAY 6, 2025

Predictive analytics: Turning insight into foresight Predictive analytics uses historical data and statistical models or machine learning algorithms to answer the question, What is likely to happen? If your models are trained on inconsistent, incomplete or inaccurate data, the results will be flawed no matter how advanced the algorithm.

Prescriptive Analytics 78

Prescriptive Analytics Predictive Analytics Descriptive Analytics ROI 78