Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

Cloudera

JULY 27, 2021

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. We believe the best way to learn what a technology is capable of is to build things with it.

Data Science 114

Data Science Enterprise Experimentation Machine Learning 114

CIO Business Intelligence

JANUARY 19, 2024

The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more. and SAS Text Analytics, Time Series, Experimentation, and Optimization.

Data Science 127

Data Science Machine Learning Predictive Modeling Experimentation 127

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

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 113

Experimentation Enterprise Deep Learning Modeling 113

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

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 102

IT Digital Transformation IoT Cost-Benefit 102

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

Dataiku

MARCH 25, 2024

This scenario is not science fiction but a glimpse into the capabilities of Multimodal Large Language Models (M-LLMs), where the convergence of various modalities extends the landscape of AI. But instead, a machine seamlessly identifies the scene and its location, provides a detailed description, and even suggests nearby attractions.

Visualization 132

Visualization Modeling Experimentation Testing 132

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 94

Unstructured Data Cost-Benefit Experimentation Machine Learning 94

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 332

Management Machine Learning Experimentation Metrics 332

O'Reilly on Data

APRIL 19, 2021

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%).

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

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

Data Science and Beyond

DECEMBER 23, 2018

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. I’ve been interested in the area of causal inference in the past few years.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

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 94

Cost-Benefit Modeling Manufacturing Interactive 94

CIO Business Intelligence

JUNE 12, 2023

The company also lets AI make 3D models to follow a construction or streamline internal training. “We Of course, he says, it’s interesting to try something experimental, but investing requires greater commitment to the business case. But maybe the next step for salespeople will be to learn it too.”

Sales 52

Sales Machine Learning Experimentation Deep Learning 52

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

Cloudera

AUGUST 20, 2020

UOB used deep learning to improve detection of procurement fraud, thereby fighting financial crime. Acceptance that it will be an experiment — ML really requires a lot of experimentation, and often times you don’t know what’s going to be successful. So, the business has to accept and be willing to fail at it.

Enterprise 121

Enterprise Experimentation Uncertainty Deep Learning 121

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

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 Experimentation Reporting 98

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Using ML models to search more effectively brought the search space down to 102—which can run on modest hardware. Model-Driven Data Queries. of relational databases represent early forms of machine learning.

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

Ontotext

NOVEMBER 6, 2019

GraphDB Workbench is the interface for Ontotext’s semantic graph database, which provides the core infrastructure including modelling agility, data integration, relationship exploration and cross-enterprise semantic data publishing and consumption. The Plugins. The RDFRank plugin is one of the most popular plugins for GraphDB.

Experimentation 45

Experimentation Deep Learning Software Modeling 45

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. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

OCTOBER 19, 2021

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 346

IT Testing Experimentation Software 346

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 77

Machine Learning Manufacturing Cost-Benefit Deep Learning 77

CIO Business Intelligence

MAY 23, 2022

The traditional approach for artificial intelligence (AI) and deep learning projects has been to deploy them in the cloud. Because it’s common for enterprise software development to leverage cloud environments, many IT groups assume that this infrastructure approach will succeed as well for AI model training.

Cost-Benefit 92

Cost-Benefit Experimentation Data Lake Deep Learning 92

DataRobot

DECEMBER 16, 2021

and train models with a single click of a button. Advanced users will appreciate tunable parameters and full access to configuring how DataRobot processes data and builds models with composable ML. Explanations around data, models , and blueprints are extensive throughout the platform so you’ll always understand your results.

Experimentation 98

Experimentation Unstructured Data Deep Learning Modeling 98

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. ModelOps and MLOps fall under the umbrella of DataOps,with a specific focus on the automation of data science model development and deployment workflows.

Testing 307

Testing Machine Learning Consulting Data Quality 307

O'Reilly on Data

SEPTEMBER 11, 2019

2 in frequency in proposal topics; a related term, “models,” is No. An ML-related topic, “models,” was No. For example, even though ML and ML-related concepts —a related term, “ML models,” (No. Deep learning,” for example, fell year over year to No. If anything, this focus has shifted to the ML or predictive model.

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. Models trained in DataRobot can also be easily deployed to Azure Machine Learning, allowing users to host models easier in a secure way.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

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?) All three of these courses are free: 1.

Marketing 136

Marketing Analytics Machine Learning Strategy 136