Insight

MAY 7, 2019

Transfer learning has simplified image classification tasks. For image classification tasks, transfer learning has proven to be very effective in providing good accuracy with fewer labeled datasets. Transfer learning is a technique that enables the transfer of knowledge learned from one dataset to another.

Modeling 75

Modeling Testing Machine Learning Interactive 75

Domino Data Lab

AUGUST 6, 2021

In the world of machine learning (ML) and artificial intelligence (AI), governance is a lifelong pursuit. All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards.

Machine Learning 145

Machine Learning Modeling Testing Risk 145

IBM Big Data Hub

DECEMBER 15, 2023

The diversity and accessibility of open-source AI allow for a broad set of beneficial use cases, like real-time fraud protection, medical image analysis, personalized recommendations and customized learning. TensorFlow is a flexible, extensible learning framework that supports programming languages like Python and Javascript.

Deep Learning 71

Deep Learning Experimentation Machine Learning Risk 71

Smart Data Collective

FEBRUARY 20, 2022

Rather, AWS offers a variety of data movement, data storage, data lakes, big data analytics, log analytics, streaming analytics, and machine learning (ML) services to suit any need. AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms. Artificial intelligence (AI).

Cost-Benefit 116

Cost-Benefit Data Lake Software Machine Learning 116

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. Types of Artificial Intelligence: Machine Learning, Deep Learning. Analytic Evolution in Enterprise Performance Management.

Technology 43

Technology Prescriptive Analytics Machine Learning Business Intelligence 43

FineReport

MAY 6, 2020

Machine learning. Machine learning murni menekankan kepada kemampuan prediktif dan pengimplimentasian algoritma tetapi statistik menekankan kepada tingkat penginterpretasiannya. Di-update setiap minggunya, termasuk blog, tutorial, kelas, buku, karir, dan sebagainya. Machine Learning. Machine Learning.

Data mining 98

Data mining Data Warehouse Machine Learning Big Data 98

Cloudera

OCTOBER 30, 2023

The public cloud is increasingly becoming the preferred platform to host data analytics – related projects, such as business intelligence, machine learning (ML), and AI applications. However, the reality is that the “move to cloud” is a turbulent flight for many of them. Why FinOps?

Analytics 80

Analytics Cost-Benefit ROI Business Objectives 80

IBM Big Data Hub

MAY 1, 2023

In this blog post, we will review the various methods of GCP cloud cost management, what problems they address and how GCP users can best use them. Learn more about IBM Turbonomic. The first is performance-based recommendations that are based on CPU and RAM currently allocated to the on-premises virtual machine (VM).

Optimization 86

Optimization ROI Consulting Dashboards 86

Cloudera

APRIL 13, 2021

Gavin leads the hardware alliances team and has made big strides in Cloudera’s relationship with Dell/EMC , launched a new strategic partnership with NVIDIA to help GPU acceleration for CDP-PvC Base, and recently became a first-time dad. Obviously, the Pandemic changed that and we got a Nespresso machine. I don’t think I will go back.

Management 60

Management Sales Marketing Strategy 60

IBM Big Data Hub

AUGUST 23, 2023

EC2 instances are virtual machines that allow you to run your applications on AWS. In this blog post, we will explore the challenges of selecting the best Amazon EC2 instance type for your application and provide you with some best practices for how to make the right decision.

Data Processing 40

Data Processing Optimization Testing Modeling 40

DataRobot Blog

JULY 27, 2022

However, machine learning utilization is often unpredictable, which makes scaling sometimes a huge challenge. From our experience working with organizations across different industries, we learned about the main challenges related to this process. Learn More About DataRobot MLOps. Learn More.

Metrics 52

Metrics Deep Learning Modeling Machine Learning 52

IBM Big Data Hub

AUGUST 1, 2023

We are bringing the power of foundation models with the availability of a GPU as a service on IBM Cloud offering to help organizations tap into artificial intelligence (AI) in a secured environment while aiming to mitigate third- and fourth-party risk.

Digital Transformation 67

Digital Transformation Risk Data Processing Enterprise 67

CDW Research Hub

NOVEMBER 12, 2020

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. Sometimes, faster is just faster!

ROI 79

ROI Deep Learning Machine Learning Business Objectives 79

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. Introduction.

Machine Learning 138

Machine Learning Data Science Data Lake Modeling 138

Insight

MARCH 27, 2019

To learn more about how I built this, read on! To address this problem, Google published AutoAugment last year, which discovers optimized augmentations for the given dataset using reinforcement learning. Using Google’s AutoAugment requires powerful computational resources due to the reinforcement learning module. 2018 , blog ).

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

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 119

Deep Learning Machine Learning Data Science Cost-Benefit 119

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

Cloudera

NOVEMBER 24, 2020

With the complexity of data growing across the enterprise and emerging approaches to machine learning and AI use cases, data scientists and machine learning engineers have needed more versatile and efficient ways of enabling data access, faster processing, and better, more customizable resource management across their machine learning projects.

Machine Learning 100

Machine Learning Data Science Management Enterprise 100

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.”.

Management 244

Management Metrics Machine Learning Modeling 244

Cloudera

OCTOBER 1, 2022

Some organizations are choosing to confront these challenges with the help of tools like machine learning (ML) and artificial intelligence (AI) to automate, streamline, and scale compliance. . It is pretty impressive just how much has changed in the enterprise machine learning and AI landscape. Infrastructure.

Data Governance 76

Data Governance Strategy Enterprise Machine Learning 76

Cloudera

AUGUST 28, 2023

In the following section, we are going to walk you through our newest Applied Machine Learning Prototype (AMP), “LLM Chatbot Augmented with Enterprise Data”. In Cloudera Machine Learning (CML), you can select and deploy a complete ML project from the AMP catalog with a single click. V100, A100, T4 GPUs).

Enterprise 78

Enterprise Machine Learning Modeling Data Processing 78

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

DataRobot Blog

FEBRUARY 2, 2023

Large language models, also known as foundation models, have gained significant traction in the field of machine learning. Learn how you can easily deploy a pre-trained foundation model using the DataRobot MLOps capabilities, then put the model into production. What Are Large Language Models?

Modeling 52

Modeling Machine Learning Data Processing Interactive 52

IBM Big Data Hub

SEPTEMBER 7, 2023

Today we are announcing our latest addition: a new family of IBM-built foundation models which will be available in watsonx.ai , our studio for generative AI, foundation models and machine learning. At 13 billion parameter models the Granite models are more efficient than larger models, fitting onto a single V100-32GB GPU.

Modeling 105

Modeling Risk Unstructured Data Enterprise 105

IBM Big Data Hub

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications. IBM watsonx consists of the following: IBM watsonx.ai

Modeling 90

Modeling Enterprise Software Machine Learning 90

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 48

Deep Learning Data Science Machine Learning Modeling 48

Domino Data Lab

FEBRUARY 12, 2020

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. this post on the Ray project blog ?. Ray is an open-source system for scaling Python applications from single machines to large clusters. for reinforcement learning (RL), ?

Experimentation 137

Experimentation Modeling Data Science Management 137

Domino Data Lab

JULY 15, 2021

Originally created for software development, Python is used in a variety of contexts, including deep learning research and model deployment. PyTorch is primarily used with Python for high level features such as computation on multidimensional data arrays and provides strong GPU support. Contact us today.

Data Science 145

Data Science Visualization Modeling Enterprise 145

Domino Data Lab

JULY 28, 2021

For companies creating models to scale, an enterprise Machine Learning Operation (MLOps) platform not only needs to support enterprise-grade development and production, it needs to follow the same standard process that data scientists use. The sets of information gained from the project are then carried forward to subsequent projects.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

O'Reilly on Data

MARCH 23, 2023

Unlike labels, embeddings are learned from the training data, not produced by humans. They are all “learned” during training, rather than set by the developers. It’s by far the most convincing example of a conversation with a machine; it has certainly passed the Turing test. Other large models have similar numbers.

IT 262

IT Modeling Testing Risk 262

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. We needed an “evolvable architecture” which would work with the next deep learning framework or compute platform.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

IBM Big Data Hub

FEBRUARY 9, 2024

Massive, centrally operated mainframe computers from a few players gave way to smaller, more efficient machines accessible to enterprises and research institutions. Video, in particular, offers great potential for holistic learning. In time, powerful personal computers with intuitive no-code interfaces became ubiquitous.

Cost-Benefit 104

Cost-Benefit Modeling Data-driven Enterprise 104