Cloudera

AUGUST 20, 2020

UOB used deep learning to improve detection of procurement fraud, thereby fighting financial crime. Personally, it’s examples such as UOB — ones solving real-world problems and delivering value through data analytics while employing ML and AI — that makes this category really exciting. It’s about how you approach AI and ML.

Enterprise 121

Enterprise Experimentation Uncertainty Deep Learning 121

CIO Business Intelligence

JUNE 12, 2023

It can be about anything from classic data analysis and advanced data analysis, to robotics or machine learning. The vast majority of companies already have a structure for analytics and machine learning, so we’re already there; it doesn’t add much,” she adds. But maybe the next step for salespeople will be to learn it too.”

Sales 52

Sales Machine Learning Experimentation Deep Learning 52

FineReport

DECEMBER 19, 2019

For newbies, a tool with low learning difficulty but powerful analytical performance cannot be better. In addition, Jupyter Notebook is also an excellent interactive tool for data analysis and provides a convenient experimental platform for beginners. Reporting of FineReport. Dashboard of FineReport. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Ontotext

NOVEMBER 6, 2019

This new version sees the release of essential plug-ins: data virtualization, a sophisticated ranking of semantic search results, new types of indexing and advanced graph analytics. GraphDB has always been compliant with W3C standards and an active member of the semantic web community. The Plugins.

Experimentation 45

Experimentation Deep Learning Software Modeling 45

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 89

Cost-Benefit Modeling Manufacturing Interactive 89

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. This is critical in our massively data-sharing world and enterprises.

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

O'Reilly on Data

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. It seems as if the experimental AI projects of 2019 have borne fruit. Supervised learning is dominant, deep learning continues to rise.

Enterprise 318

Enterprise Deep Learning Data Governance Risk 318

DataKitchen

APRIL 13, 2021

Process Analytics. 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. .

Testing 300

Testing Machine Learning Consulting Data Quality 300

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. Automated development: With AutoAI , beginners can quickly get started and more advanced data scientists can accelerate experimentation in AI development.

Machine Learning 71

Machine Learning Manufacturing Cost-Benefit Deep Learning 71

CDW Research Hub

AUGUST 21, 2019

Finally, perhaps the biggest obstacle faced when adopting an AI project is the time it takes to configure the server, prepare the data, build and train the model, and deploy and infer for deep learning. The kit helps facilitate clients’ AI adoption journey from experimentation to production.

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. It used deep learning to build an automated question answering system and a knowledge base based on that information.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

JULY 2, 2019

Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Here’s a sampler of related papers and articles if you’d like to dig in further: “ Synthesizing Programs with Deep Learning ” – Nishant Sinha (2017-03-25). “ Software writes Software?

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

O'Reilly on Data

SEPTEMBER 11, 2019

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. Strata attracts the leading names in the fields of data management, data engineering, analytics, ML, and artificial intelligence (AI). The term “ML” is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

DataRobot Blog

MARCH 7, 2023

Organizations that want to prove the value of AI by developing, deploying, and managing machine learning models at scale can now do so quickly using the DataRobot AI Platform on Microsoft Azure. The capability to rapidly build an AI-powered organization with industry-specific solutions and expertise.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

Domino Data Lab

OCTOBER 14, 2020

Guest post by Jeff Melching, Distinguished Engineer / Chief Architect Data & Analytics. Experimentation and collaboration are built into the core of the platform. We needed an “evolvable architecture” which would work with the next deep learning framework or compute platform. Hyperparameter Tuning.

Experimentation 99

Experimentation Deep Learning Modeling 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. Like this blog, it will be particularly relevant for those who are in digital analytics and digital marketing. Analytical Thinking: Skills, Interviewing, Value.

Marketing 136

Marketing Analytics Machine Learning Strategy 136

O'Reilly on Data

JANUARY 7, 2020

In fact, in our 2019 surveys, more than half of the respondents said AI (deep learning, specifically) will be part of their future projects and products—and a majority of companies are starting to adopt machine learning. To stay competitive, data scientists need to at least dabble in machine and deep learning.

Deep Learning 184

Deep Learning Machine Learning Experimentation Modeling 184