Cloudera

AUGUST 20, 2020

It’s been a year filled with disruption and uncertainty. Businesses had to literally switch operations, and enable better collaboration and access to data in an instant — while streamlining processes to accommodate a whole new way of doing things. One day we were all going to the office, and the next we were working from home.

Enterprise 120

Enterprise Experimentation Uncertainty Deep Learning 120

CIO Business Intelligence

JULY 17, 2023

What are some of the unique data and cybersecurity challenges that Havmor faces as a vast customer-centric business? Data and cybersecurity issues challenge every IT leader. With cybersecurity and data protection, end-user awareness presents itself as a key challenge. We are working on similar projects for supply chain as well.

IT 98

IT Digital Transformation IoT Internet of Things 98

CIO Business Intelligence

APRIL 6, 2022

Our world today is experiencing an extremely social, connected, competitive and technology-driven business environment. If anything, the past few years have shown us the levels of uncertainty we are facing. Infosys Living Labs helps customers solve business problems with their emerging technology solutions and service offerings.

Experimentation 103

Experimentation Uncertainty Data Lake Enterprise 103

CIO Business Intelligence

AUGUST 25, 2023

It’s around these four work streams that leading organizations are positioning themselves to mature their data strategies and, in doing so, answer not only today’s AI questions but tomorrow’s. So, if you, too, want to leverage AI to its fullest extent, you must first look in the mirror: Can I manage this growing volume of data?

Strategy 98

Strategy Data Strategy Experimentation Uncertainty 98

DataRobot Blog

OCTOBER 4, 2022

The shift in consumer habits and geopolitical crises have rendered data patterns collected pre-COVID obsolete. This has prompted AI/ML model owners to retrain their legacy models using data from the post-COVID era, while adapting to continually fluctuating market trends and thinking creatively about forecasting.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

Jet Global

DECEMBER 31, 2019

You help companies adapt to a changing, tech-driven economy. Among several services my organization provides; we help individuals, enterprises, and public agencies plan, prepare, and manage through the uncertainty, demands, and challenges of the future. How quickly do companies need to become “data-driven”?

Digital Transformation 59

Digital Transformation Finance Experimentation Data-driven 59

O'Reilly on Data

OCTOBER 13, 2020

From a technical perspective, it is entirely possible for ML systems to function on wildly different data. For example, you can ask an ML model to make an inference on data taken from a distribution very different from what it was trained on—but that, of course, results in unpredictable and often undesired performance. I/O validation.

Management 251

Management Machine Learning Metrics Modeling 251

CIO Business Intelligence

JULY 31, 2023

To allow or not According to various news reports, some big-name companies initially blocked generative AI tools such as ChatGPT for various reasons, including concerns about protecting proprietary data. 1 question now is to allow or not allow,” says Mir Kashifuddin, data risk and privacy leader with the professional services firm PwC US.

Strategy 98

Strategy Risk Enterprise Risk Management 98

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Without large amounts of labeled training data solving most AI problems is not possible.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. It helps you to amplify what’s proven to work, throw away what isn’t, and tweak the goal-posts when data indicates that they may be in the wrong place.

Metrics 157

Metrics KPI Analytics Key Performance Indicator 157

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

by NIALL CARDIN, OMKAR MURALIDHARAN, and AMIR NAJMI When working with complex systems or phenomena, the data scientist must often operate with incomplete and provisional understanding, even as she works to advance the state of knowledge. There has been debate as to whether the term “data science” is necessary. Some don’t see the point.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. The term “AI,” meanwhile, is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20