DataRobot Blog

JANUARY 17, 2023

Not actually being a machine learning problem: Value-at-Risk modeling is the classic example here—VaR isn’t a prediction of anything, it’s a statistical summation of simulation results. As discussed, we massively accelerate that process of experimentation. Watch a demo. See DataRobot AI Platform in Action.

Machine Learning 87

Machine Learning Marketing Experimentation Data-driven 87

CIO Business Intelligence

APRIL 18, 2022

They run the risk of miscommunication and misaligned business, technology, and operational strategy across the CXO team. As much as Young wants to support small and midsize businesses, he says he has to think about the risk to the business and their customers. They invest in cloud experimentation.

Digital Transformation 145

Digital Transformation Dashboards Cost-Benefit Consulting 145

CIO Business Intelligence

JUNE 28, 2023

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs. This allowed us to derive insights more easily.”

Digital Transformation 105

Digital Transformation Cost-Benefit Informatics Data mining 105

IBM Big Data Hub

MARCH 4, 2024

While many organizations have implemented AI, the need to keep a competitive edge and foster business growth demands new approaches: simultaneously evolving AI strategies, showcasing their value, enhancing risk postures and adopting new engineering capabilities. Otherwise, the risks become too significant.

Enterprise 96

Enterprise ROI Risk Strategy 96

DataRobot Blog

NOVEMBER 15, 2022

At the event, a financial services panel discussion shared why iteration and experimentation are critical in an AI-driven data science environment. This allows GCash to maintain the pace of innovation and iteration without exposing the business to significant risk. Request a demo. Closing the Value Gap: Reducing AI Cycle Time.

Big Data 52

Big Data Experimentation Machine Learning Data-driven 52

CIO Business Intelligence

JUNE 6, 2023

Organize frequent startup pitches and demos With venture capital investment continuing to fall in 2023 , more startups should find themselves eager to partner with enterprises, and that presents IT leaders a wealth of opportunities to improve their innovation outlook. We think weeks; they think months,” says Mathieu of the startup mentality.

Key Performance Indicator 105

Key Performance Indicator Cost-Benefit Enterprise Experimentation 105

IBM Big Data Hub

DECEMBER 4, 2023

And the abundance of data available for training models has opened up vast possibilities for experimentation and learning. Ensuring that generative AI models adhere to ethical guidelines and that adequate processes are in place to mitigate risks and biases is essential.

Experimentation 63

Experimentation Machine Learning Metrics Modeling 63

Jet Global

APRIL 27, 2023

Tax teams of multinational enterprises (MNEs) in the manufacturing industry face increasing challenges to manage business and market risks effectively. For your tax team to be agile, you’ll need to optimize tax technology and processes so you can both spot data insights and mitigate risk.

Manufacturing 52

Manufacturing Marketing Reporting Risk 52

Jet Global

DECEMBER 31, 2019

If there is no advantage to taking a risk—knowing that failure is a possibility—an individual will assume business as normal. Organizations need to become really comfortable with experimentation. It’s been the domain of checklists, demos, and interviews. I believe that leaders must be supported in taking bold steps.

Digital Transformation 59

Digital Transformation Finance Experimentation Data-driven 59

IBM Big Data Hub

SEPTEMBER 25, 2023

Large, untested workloads run the risk of hogging all the resources. Data Exploration and Innovation: The flexibility of Presto has encouraged data exploration and experimentation at Uber. Request a live demo here to see Presto and watsonx.data in action Try watsonx.data for free 1 Uber. Enterprise Management Associates (EMA).

OLAP 86

OLAP Data Lake Data-driven Online Analytical Processing 86

Domino Data Lab

JULY 28, 2021

Validators and auditors can use the Knowledge Center to ensure models are safe and trusted, thus reducing potential risks. The Workbench is Domino’s notebook-based environment where data scientists can do their R&D and experimentation. Develop Stage. Have a question? Get in touch with us.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

DataRobot Blog

SEPTEMBER 30, 2022

Adoption of AI/ML is maturing from experimentation to deployment. When this type of drift occurs, your model is at risk of degradation, meaning you cannot trust the predictions anymore. Request a demo. Model Observability Features. Manage Unpredictability in Active Deployments. See DataRobot MLOps in Action.

Slice and Dice 52

Slice and Dice Modeling Metrics Experimentation 52

Domino Data Lab

JULY 2, 2019

One clear lesson of the early 21st century: strategies at scale that rely on centralization are generally risks (John Robb explores that in detail in Brave New War which I’ve just been reading – good stuff). Check out the “ Scythe ” demo referenced above and the related paper by Chenglong Wang, Alvin Cheung, and Ras Bodik from U Washington.

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

Ontotext

APRIL 3, 2024

Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. Nimit Mehta : You are talking about the three big ones: cost, revenue, and risk. And, when you get to the top, it’s about risks and existential threats to the business. To me, that was amazing.

Technology 45

Technology Risk Digital Transformation Measurement 45

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. The ability to measure results (risk-reducing evidence). Ensure a culture that supports a steady process of learning and experimentation.

Data Science 66

Data Science Experimentation Machine Learning Data-driven 66