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

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. Without clarity in metrics, it’s impossible to do meaningful experimentation. Experimentation should show you how your customers use your site, and whether a recommendation engine would help the business.

Marketing 364

Marketing Experimentation Metrics Testing 364

Analytics Vidhya

JANUARY 17, 2024

The multi-year agreement focuses on helping clients move beyond experimental stages to full-scale generative AI implementations. Capgemini and Amazon Web Services (AWS) have extended their strategic collaboration, accelerating the adoption of generative AI solutions across organizations.

Experimentation 337

Experimentation Analytics Machine Learning 337

Smart Data Collective

DECEMBER 24, 2020

Machine learning is the driving force of AI. It allows humans to essentially teach software in a matter of weeks what a human would take decades to learn. AI and machine learning are changing the world we live in and altering the way we do things. Some grad students have already learned this the hard way.

Machine Learning 122

Machine Learning Experimentation Software Reporting 122

CIO Business Intelligence

DECEMBER 12, 2024

In some use cases, older AI technologies, such as machine learning or neural networks, may be more appropriate, and a lot cheaper, for the envisioned purpose. Experimentation doesnt have to be huge, but it breeds familiarity, he says. Gen AI uses huge amounts of energy compared to some other AI tools, he notes.

IT 134

IT Data Quality Experimentation Machine Learning 134

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.

Deep Learning 356

Deep Learning Machine Learning Data-driven Modeling 356

Rocket-Powered Data Science

FEBRUARY 15, 2023

encouraging and rewarding) a culture of experimentation across the organization. there can be objective assessments of failure, lessons learned, and subsequent improvements), then friction can be minimized, failure can be alleviated, and innovation can flourish. Test early and often. Expect continuous improvement.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

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. COPML: The Glue That Holds It All Together.

Machine Learning 122

Machine Learning Modeling Experimentation IT 122

Analytics Vidhya

JUNE 9, 2023

Zomato, the renowned food and grocery delivery service, has taken a bold step into the world of artificial intelligence (AI) experimentation. Joining the ranks of businesses eager to leverage emerging technologies, Zomato aims to revolutionize the consumer experience through innovative AI-based solutions.

Experimentation 291

Experimentation Technology Analytics Machine Learning 291

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 364

IT Testing Experimentation Software 364

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

KDnuggets

DECEMBER 27, 2021

Learn how to improve ML reproducibility by treating experiments as code.

Machine Learning 159

Machine Learning Experimentation 159

O'Reilly on Data

JANUARY 11, 2022

What does “reproducibility” mean if the model is so large that it’s impossible to reproduce experimental results? We’ll see new tools and platforms for dealing with supply chain and logistics issues, and they’ll likely make use of machine learning. Supply chains and business logistics will remain under stress.

Experimentation 361

Experimentation Modeling Strategy Machine Learning 361

CIO Business Intelligence

OCTOBER 24, 2024

Forrester also recently predicted that 2025 would see a shift in AI strategies , away from experimentation and toward near-term bottom-line gains. While AI projects will continue beyond 2025, many organizations’ software spending will be driven more by other enterprise needs like CRM and cloud computing, Lovelock says.

IT 133

IT Data-driven Recreation/Entertainment Experimentation 133

AWS Big Data

JANUARY 15, 2025

Improve accuracy and resiliency of analytics and machine learning by fostering data standards and high-quality data products. In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications.

IoT 110

IoT Machine Learning Metadata Data-driven 110

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. . Collaboration and Sharing.

Testing 300

Testing Machine Learning Consulting Data Quality 300

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. So let’s introduce Cloudera Machine Learning (CML) and discuss how it addresses the aforementioned silo issues.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

CIO Business Intelligence

DECEMBER 10, 2024

Meanwhile, a separate AI agent used machine learning and analytics techniques to make underwriting and coverage decisions based on the outputs from the first model. In fact, business spending on AI rose to $13.8

Modeling 116

Modeling Insurance Unstructured Data Experimentation 116

CIO Business Intelligence

JANUARY 6, 2025

Wetmur says Morgan Stanley has been using modern data science, AI, and machine learning for years to analyze data and activity, pinpoint risks, and initiate mitigation, noting that teams at the firm have earned patents in this space. I firmly believe continuous learning and experimentation are essential for progress.

IT 140

IT Cost-Benefit Measurement Experimentation 140

O'Reilly on Data

MARCH 25, 2025

People have been building data products and machine learning products for the past couple of decades. ML apps needed to be developed through cycles of experimentation (as were no longer able to reason about how theyll behave based on software specs). This isnt anything new.

Testing 174

Testing Data-driven Software Measurement 174

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

SEPTEMBER 15, 2020

Results are typically achieved through a scientific process of discovery, exploration, and experimentation, and these processes are not always predictable. Given the scientific nature of AI, goals are better expressed as well-posed questions and hypotheses around a specific and intended benefit or outcome for a certain stakeholder.

Advertising 362

Advertising Cost-Benefit ROI Machine Learning 362

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

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.”. We can’t wait to see what you build!

Management 314

Management Machine Learning Metrics Modeling 314

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.

Machine Learning 87

Machine Learning Marketing Experimentation Data-driven 87

Corinium

JUNE 6, 2019

It is also important to have a strong test and learn culture to encourage rapid experimentation. A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

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

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

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

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

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) Industry 4.0

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

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

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

KDnuggets

NOVEMBER 19, 2019

Find out how data scientists and AI practitioners can use a machine learning experimentation platform like Comet.ml to apply machine learning and deep learning to methods in the domain of audio analysis.

Deep Learning 53

Deep Learning Machine Learning Experimentation 53

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

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. It’s a fundamentals exam, so you don’t need extensive experience to pass.

Data Science 126

Data Science Machine Learning Predictive Modeling Forecasting 126

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. Have business leaders defined realistic success criteria and areas of low-risk experimentation? Are data science teams set up for success?

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128