CIO Business Intelligence

FEBRUARY 22, 2024

With the aim to accelerate innovation and transform its digital infrastructures and services, Ferrovial created its Digital Hub to serve as a meeting point where research and experimentation with digital strategies could, for example, provide new sources of income and improve company operations.

IT 104

IT Digital Transformation IoT Cost-Benefit 104

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

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

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. 4) AIOps increasingly became a focus in AI strategy conversations.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

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 307

Testing Machine Learning Consulting Data Quality 307

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AutoML tools: Automated machine learning, or autoML, supports faster model creation with low-code and no-code functionality.

Machine Learning 78

Machine Learning Manufacturing Cost-Benefit Deep Learning 78

CDW Research Hub

AUGUST 21, 2019

Organizations across all industries are seeing the value and competitive advantage of having an artificial intelligence (AI) strategy. The challenges of implementing an AI strategy are many. The kit helps facilitate clients’ AI adoption journey from experimentation to production. 10 GbE or 100 GbE used to connect nodes.

Strategy 45

Strategy Deep Learning Machine Learning Experimentation 45

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

Domino Data Lab

JULY 2, 2019

Doesn’t this seem like a worthy goal for machine learning—to make the machines learn to work more effectively? Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. of relational databases represent early forms of machine learning.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Domino Data Lab

FEBRUARY 12, 2020

If you’re wondering if Ray should be part of your technical strategy for Python-based applications, especially ML and AI, this post is for you. for reinforcement learning (RL), ? Motivations for Ray: Training a Reinforcement Learning (RL) Model. RL is the type of machine learning that was used recently to ?beat

Experimentation 137

Experimentation Modeling Data Science Management 137

Domino Data Lab

OCTOBER 14, 2020

With our platform, farmers can understand the impact of a particular fertilization strategy and adjust as needed. The introduction of machine learning to the agricultural domain is relatively new. To enable a digital transformation in agriculture we must experiment and learn quickly across the entire model lifecycle.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. One sample strategy: Expect that I'll ask open-ended questions (If a company has 90% Reach on TV, why the heck do they need digital?). Beautiful, right? SCOTUSblog FTW!

Marketing 136

Marketing Analytics Machine Learning Strategy 136