Cloudera

NOVEMBER 19, 2021

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. But, we also know that experimentation alone doesn’t yield business value.

Machine Learning 122

Machine Learning Modeling Experimentation IT 122

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. However, the concept is quite abstract.

IT 353

IT Testing Experimentation Software 353

Domino Data Lab

MAY 15, 2019

Machine Learning Projects are Hard: Shifting from a Deterministic Process to a Probabilistic One. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work. Product Management for Machine Learning.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

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 290

Strategy Experimentation Uncertainty Machine Learning 290

DataKitchen

APRIL 13, 2021

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. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. . Meta-Orchestration .

Testing 312

Testing Machine Learning Consulting Data Quality 312

O'Reilly on Data

JANUARY 11, 2022

This trend started with the gigantic language model GPT-3. This may encourage the creation of more large-scale models; it might also drive a wedge between academic and industrial researchers. What does “reproducibility” mean if the model is so large that it’s impossible to reproduce experimental results?

Experimentation 313

Experimentation Modeling Strategy Machine Learning 313

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

O'Reilly on Data

MARCH 19, 2020

We use it as a data source for our annual platform analysis , and we’re using it as the basis for this report, where we take a close look at the most-used and most-searched topics in machine learning (ML) and artificial intelligence (AI) on O’Reilly [1]. What’s driving this growth? disproportionately involve Python. What’s in a name?

Deep Learning 283

Deep Learning Machine Learning Data-driven Modeling 283

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.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Cloudera

APRIL 13, 2021

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. It’s a pie-in-the-sky statement that sounds great, right?

Machine Learning 98

Machine Learning Enterprise Experimentation Modeling 98

David Menninger's Analyst Perspectives

MAY 17, 2023

And recently, ChatGPT has raised awareness of AI and instigated research and experimentation into new ways in which AI can be applied. This perspective, the second in a series on generative AI, introduces some of the concepts behind ChatGPT, including large language models and transformers.

Modeling 147

Modeling Experimentation Data-driven Business Intelligence 147

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Predictive Analytics – AI & machine learning. This integration is key in assuring that models evolve with the data – to avoid, for example, model drift.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

Management 267

Management Machine Learning Metrics Modeling 267

CIO Business Intelligence

DECEMBER 10, 2024

Large language models (LLMs) just keep getting better. In just about two years since OpenAI jolted the news cycle with the introduction of ChatGPT, weve already seen the launch and subsequent upgrades of dozens of competing models. From Llama3.1 to Gemini to Claude3.5 From Llama3.1 to Gemini to Claude3.5

Modeling 116

Modeling Insurance Unstructured Data Experimentation 116

O'Reilly on Data

SEPTEMBER 15, 2020

It also helps companies learn how to translate existing AI capabilities into solving specific real-world problems and use cases. In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. characters, words, or sentences). Conclusion.

Advertising 302

Advertising Cost-Benefit ROI Machine Learning 302

DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

CIO Business Intelligence

NOVEMBER 27, 2024

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

Data Architecture 128

Data Architecture Cost-Benefit Machine Learning Experimentation 128

DataRobot Blog

JANUARY 17, 2023

Recently, a prospective customer asked me how I reconcile the fact that DataRobot has multiple very successful investment banks using DataRobot to enhance the P&L of their trading businesses with my comments that machine learning models aren’t always great at predicting financial asset prices. For price discovery (e.g.,

Machine Learning 87

Machine Learning Marketing Experimentation Data-driven 87

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning. (4) See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Cloudera

JULY 27, 2021

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. We believe the best way to learn what a technology is capable of is to build things with it.

Data Science 117

Data Science Enterprise Experimentation Machine Learning 117

CIO Business Intelligence

OCTOBER 3, 2024

Rather than pull away from big iron in the AI era, Big Blue is leaning into it, with plans in 2025 to release its next-generation Z mainframe , with a Telum II processor and Spyre AI Accelerator Card, positioned to run large language models (LLMs) and machine learning models for fraud detection and other use cases.

Machine Learning 140

Machine Learning Experimentation Insurance Modeling 140

DataRobot Blog

MARCH 8, 2023

Today, SAP and DataRobot announced a joint partnership to enable customers connect core SAP software, containing mission-critical business data, with the advanced Machine Learning capabilities of DataRobot to make more intelligent business predictions with advanced analytics.

Enterprise 105

Enterprise Experimentation Machine Learning Data Science 105

CIO Business Intelligence

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. CIOs and CDOs should lead ModelOps and oversee the lifecycle Leaders can review and address issues if the data science teams struggle to develop models.

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

Paul DeBeasi

JANUARY 30, 2019

Machine learning projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains, including statistical analysis, data analysis and application development. Four Options for Integrating Machine Learning with IoT.

Machine Learning 56

Machine Learning IoT Experimentation Statistics 56

CIO Business Intelligence

JANUARY 19, 2024

The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more.

Data Science 126

Data Science Machine Learning Predictive Modeling Forecasting 126

Cloudera

JANUARY 30, 2019

Data scientists require on-demand access to data, powerful processing infrastructure, and multiple tools and libraries for development and experimentation. To move beyond laptop experimentation to impacting the business, data teams also need seamless sharing and native integration with the production data platform. Sound familiar?

Data Science 76

Data Science Machine Learning Experimentation Visualization 76

CIO Business Intelligence

DECEMBER 4, 2024

At AWS re:Invent Tuesday, JPMorgan Chase Global CIO Lori Beer detailed the evolving partnership between the financial services giant and AWS, which finds JPMorgan pushing the AWS SageMaker machine learning platform and AWS Bedrock generative AI platform beyond experimentation into production applications.

Experimentation 122

Experimentation Risk Marketing Enterprise 122

CIO Business Intelligence

MAY 12, 2022

The tools include sophisticated pipelines for gathering data from across the enterprise, add layers of statistical analysis and machine learning to make projections about the future, and distill these insights into useful summaries so that business users can act on them. Drag-and-drop Modeler for creating pipelines, IBM integrations.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

DataRobot

MARCH 10, 2021

The current generation of AI systems is powered by machine learning , a technology that involves learning by example rather than waiting for humans to manually code rules into a computer system. Deploy the machine learning model into production. Is autonomy a realistic promise or is it simply marketing hype?

Machine Learning 145

Machine Learning Experimentation Key Performance Indicator Reporting 145

CIO Business Intelligence

OCTOBER 5, 2023

Sandeep Davé knows the value of experimentation as well as anyone. As chief digital and technology officer at CBRE, Davé recognized early that the commercial real estate industry was ripe for AI and machine learning enhancements, and he and his team have tested countless use cases across the enterprise ever since.

Experimentation 130

Experimentation Strategy Machine Learning Interactive 130

Domino Data Lab

NOVEMBER 14, 2019

James Murdoch, Chandan Singh, Karl Kumber, and Reza Abbasi-Asi’s recent paper, “Definitions, methods, and applications in interpretable machine learning” Introduction. Model interpretability continues to spark public discourse among industry. Yet there are tradeoffs to consider when selecting a model.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

Rocket-Powered Data Science

OCTOBER 16, 2020

First, we heard how Big Data, Data Science, Machine Learning (ML) and Advanced Analytics would have the honor to be the technologies that would cure cancer, end world hunger and solve the world’s biggest challenges. We build models to test our understanding, but these models are not “one and done.” The Age of Hype Cycles.

Digital Transformation 142

Digital Transformation Experimentation Data Science Data Strategy 142

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. Capabilities Beyond Classic Jupyter for End-to-end Experimentation.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

CIO Business Intelligence

NOVEMBER 4, 2022

Rigid requirements to ensure the accuracy of data and veracity of scientific formulas as well as machine learning algorithms and data tools are common in modern laboratories. Analytics, CIO 100, Cloud Computing, Healthcare Industry, Machine Learning

Data Lake 124

Data Lake IT Experimentation Data-driven 124

CIO Business Intelligence

DECEMBER 12, 2024

Confidence from business leaders is often focused on the AI models or algorithms, Erolin adds, not the messy groundwork like data quality, integration, or even legacy systems. In some industries, companies are using legacy software and middleware that arent designed to collect, transmit, and store data in ways modern AI models need, he adds.

IT 134

IT Data Quality Experimentation Machine Learning 134