Jedox

JULY 31, 2018

Part one of our blog series explored how people are the driving force behind the digital transformation and how it is fueled by artificial intelligence and machine learning. Now, we will take a deeper look into AI, Machine learning and other trending technologies and the evolution of data analytics from descriptive to prescriptive.

Technology 44

Technology Prescriptive Analytics Machine Learning Business Intelligence 44

IBM Big Data Hub

AUGUST 23, 2023

When it comes to hosting applications on Amazon Web Services (AWS), one of the most important decisions you will need to make is which Amazon Elastic Compute Cloud (EC2) instance type to choose. These instances provide a balance of compute, memory, and network resources, and is a good choice for many applications.

Data Processing 40

Data Processing Optimization Testing Modeling 40

Cloudera

NOVEMBER 16, 2017

On-demand compute. GPU acceleration for deep learning on demand. For more detail on user monitoring, read this article on the Cloudera Engineering Blog. Learn more about how Cloudera Data Science Workbench makes your data science team more productive. appeared first on Cloudera Blog. CDSW Release 1.2

Data Science 40

Data Science Deep Learning Big Data Software 40

CDW Research Hub

NOVEMBER 12, 2020

Compute where the data lives. The first business lesson many companies learn the hard way is that AI projects are iterative and never-ending. CPUs are sufficient for basic AI workloads, but GPUs are more ideally suited for deep learning workloads, which can require multiple large datasets and scalable neural networks.

ROI 79

ROI Deep Learning Machine Learning Business Objectives 79

CDW Research Hub

MARCH 21, 2019

Our dedicated Big Data and Analytics practice brings together deep expertise with the power of NVIDIA’s high-speed GPU DGX-1 and the robust storage architecture of Dell EMC’s Isilon, resulting in a truly holistic, future-ready deep learning solution,” says Chris Mierzwa, Chief Technology Officer at Sirius (read more here ).

Deep Learning 40

Deep Learning Big Data Analytics Data Analytics 40

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. RAPIDS brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

Cloudera

NOVEMBER 3, 2021

Thanks to pioneers like Andrew NG and Fei-Fei Li, GPUs have made headlines for performing particularly well with deep learning techniques. Today, deep learning and GPUs are practically synonymous. While deep learning is an excellent use of the processing power of a graphics card, it is not the only use.

Deep Learning 117

Deep Learning Machine Learning Data Science Cost-Benefit 117

Domino Data Lab

AUGUST 9, 2021

Just as the human brain can solve problems by applying lessons we learn from past experiences to new situations, a model is trained on a set of data and can solve problems with new sets of data. Before selecting a tool, you should first know your end goal – machine learning or deep learning. What Are Modeling Tools?

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Domino Data Lab

JULY 15, 2021

For data scientists they need a way to access those data sources easily and cleanse, transform, and combine data.Often the processing power of the database may be used for complex computations or scoring of records. In the multiverse of data science, the tool options continue to expand and evolve. What Are Data Science Tools? Snowflake ).

Data Science 145

Data Science Visualization Modeling Enterprise 145

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”. The field of AI product management continues to gain momentum.

Management 244

Management Metrics Machine Learning Modeling 244

Cloudera

JULY 26, 2021

The irreversible shift towards digital-native / digital-first consumption , working and learning paradigms has introduced new remote or hybrid working. The alleviation of infrastructure and computational constraints associated with solely on-premises data platforms; Data Products can now use different deployment models (e.g.,

Strategy 117

Strategy Data Science Marketing Unstructured Data 117

Domino Data Lab

FEBRUARY 12, 2020

this post on the Ray project blog ?. for reinforcement learning (RL), ? 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. If your team has started using ? that work best.

Experimentation 137

Experimentation Modeling Data Science Management 137

Cloudera

OCTOBER 17, 2017

Deep learning is in the news. But deep learning is a tool that enterprises use to solve practical problems. In this blog, we provide a few examples that show how organizations put deep learning to work. In this blog, we provide a few examples that show how organizations put deep learning to work.

Deep Learning 47

Deep Learning Data Science Machine Learning Modeling 47

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. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

Machine Learning 81

Machine Learning Manufacturing Cost-Benefit Deep Learning 81

Domino Data Lab

OCTOBER 14, 2020

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. With our platform, farmers can understand the impact of a particular fertilization strategy and adjust as needed.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99