IBM Big Data Hub

DECEMBER 15, 2023

When AI algorithms, pre-trained models, and data sets are available for public use and experimentation, creative AI applications emerge as a community of volunteer enthusiasts builds upon existing work and accelerates the development of practical AI solutions.

Deep Learning 70

Deep Learning Experimentation Machine Learning Risk 70

CIO Business Intelligence

APRIL 25, 2023

A transformer is a type of AI deep learning model that was first introduced by Google in a research paper in 2017. Five years later, transformer architecture has evolved to create powerful models such as ChatGPT. Meanwhile, however, many other labs have been developing their own generative AI models.

Experimentation 116

Experimentation Enterprise Deep Learning Modeling 116

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 105

IT Digital Transformation IoT Cost-Benefit 105

IBM Big Data Hub

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Figure 1 illustrates the framework for Scope 3 emission estimation employing a large language model.

Experimentation 80

Experimentation Modeling Deep Learning Data-driven 80

IBM Big Data Hub

DECEMBER 5, 2023

With the rise of highly personalized online shopping, direct-to-consumer models, and delivery services, generative AI can help retailers further unlock a host of benefits that can improve customer care, talent transformation and the performance of their applications.

Unstructured Data 95

Unstructured Data Cost-Benefit Experimentation Machine Learning 95

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

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 ). Training domain-specific models.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

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. It’s all called AI, she says.

Sales 52

Sales Machine Learning Experimentation Deep Learning 52

Cloudera

AUGUST 20, 2020

Machine Learning (ML) and Artificial Intelligence (AI), while still emerging technologies inside of enterprise organisations, have given some companies the ability to dynamically change their fortunes and reshape the way they are doing business — that is if they are brave enough to experiment and explore the unknown.

Enterprise 120

Enterprise Experimentation Uncertainty Deep Learning 120

Data Science and Beyond

SEPTEMBER 19, 2016

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. Causality and experimentation.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

FineReport

DECEMBER 19, 2019

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Reporting Experimentation 98

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

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.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. However, the concept is quite abstract.

IT 346

IT Testing Experimentation Software 346

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. . Meta-Orchestration .

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. This unified experience optimizes the process of developing and deploying ML models by streamlining workflows for increased efficiency.

Machine Learning 78

Machine Learning Manufacturing Cost-Benefit Deep Learning 78

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

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Doesn’t this seem like a worthy goal for machine learning—to make the machines learn to work more effectively? Introduction. Welcome back to our monthly burst of themespotting and conference summaries.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Domino Data Lab

FEBRUARY 12, 2020

for model serving (experimental), are implemented with Ray internally for its scalable, distributed computing and state management benefits, while providing a domain-specific API for the purposes they serve. Motivations for Ray: Training a Reinforcement Learning (RL) Model. for hyper parameter tuning, and ? annotation.

Experimentation 137

Experimentation Modeling Data Science Management 137

CDW Research Hub

AUGUST 21, 2019

Some solutions require enough servers to start filling a rack in order to achieve the computer processing needed to create more powerful AI models. Equipped with IBM’s Deep Learning frameworks bundle, Watson Machine Learning Accelerator (WMLA), formerly called PowerAI, the solution is designed for use by 3-5 data scientists.

Strategy 45

Strategy Deep Learning Machine Learning Experimentation 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. An ML-related topic, “models,” was 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. AI Platform Single-Tenant SaaS are fully managed by DataRobot and replace disparate machine learning tools, simplifying management.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

Domino Data Lab

OCTOBER 14, 2020

We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. For example, our models can show farmers how to increase their production while using less fertilizer.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

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 179

Deep Learning Machine Learning Experimentation Modeling 179

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. Intro to Machine Learning. Machine Learning. Deep Learning. This reality powers my impostor syndrome, and (yet?)

Marketing 136

Marketing Analytics Machine Learning Strategy 136