O'Reilly on Data

MARCH 31, 2020

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Managing Machine Learning Projects” (AWS).

Management 333

Management Machine Learning Experimentation Metrics 333

Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ). Functionality comparison cheat sheet.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

CIO Business Intelligence

JUNE 12, 2023

Of course, he says, it’s interesting to try something experimental, but investing requires greater commitment to the business case. This also creates the risk of building a digital legacy with solutions that work on their own, but can’t be connected well together,” he says. It’s the simplest trick,” he says.

Sales 52

Sales Machine Learning Experimentation Deep Learning 52

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. AGI wouldn’t just perceive its surroundings; it would understand them.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

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. Managing AI/ML risk. But what kind? It ranks high (No.

Enterprise 310

Enterprise Deep Learning Data Governance Risk 310

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 72

Machine Learning Manufacturing Cost-Benefit Deep Learning 72

CDW Research Hub

AUGUST 21, 2019

AI applications include consumer sentiment analysis, market prediction and fraud risk, drug discovery and genomics, video classification, medical imaging, language processing, and sensor analysis, and numerous other AI use cases. The kit helps facilitate clients’ AI adoption journey from experimentation to production.

Strategy 45

Strategy Deep Learning Machine Learning Experimentation 45

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

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

MAY 8, 2019

That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

O'Reilly on Data

SEPTEMBER 11, 2019

Deep learning,” for example, fell year over year to No. For example, even though ML and ML-related concepts —a related term, “ML models,” (No. 106, +12) also improved, year over year—are rampant, ML-related tools and techniques are not. 40; it peaked at Strata NY 2018 at No. Neural network” also fell slightly from 2018 (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

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 178

Deep Learning Machine Learning Experimentation Modeling 178