Smart Data Collective

FEBRUARY 21, 2021

Deep learning tech is influencing and enhancing many industries, promising to provide insights into key business operations which were not previously possible to unearth. One of the biggest applications of this technology lies with using deep learning to streamline fleet management. Route adjustments made in real time.

Deep Learning 109

Deep Learning Management Cost-Benefit Manufacturing 109

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. . Collaboration and Sharing.

Testing 304

Testing Machine Learning Consulting Data Science 304

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. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

DataRobot Blog

APRIL 21, 2022

Machine learning, and especially deep learning, has become increasingly more accurate in the past few years. In the graph below, borrowed from the same article, you can see how some of the most cutting-edge algorithms in deep learning have increased in terms of model size over time.

Deep Learning 145

Deep Learning Machine Learning Metrics Measurement 145

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you. A more general approach is to learn a Generalized Additive Model (GAM).

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

Corinium

JUNE 6, 2019

A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI. Build multiple MVPs to test conceptually and learn from early user feedback.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

Cloudera

DECEMBER 4, 2024

Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. System metrics, such as inference latency and throughput, are available as Prometheus metrics. Data teams can use any metrics dashboarding tool to monitor these.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

Rocket-Powered Data Science

DECEMBER 15, 2018

This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning. A related problem also arises in unsupervised machine learning.

Machine Learning 171

Machine Learning Optimization Metrics Deep Learning 171

KDnuggets

SEPTEMBER 27, 2023

10 ChatGPT Projects Cheat Sheet • Introduction to Deep Learning Libraries: PyTorch and Lightning AI • Top 5 Free Alternatives to GPT-4 • Machine Learning Evaluation Metrics: Theory and Overview • Kick Ass Midjourney Prompts with Poe

Deep Learning 126

Deep Learning Machine Learning Metrics 126

Analytics Vidhya

MARCH 7, 2024

While ChatGPT, developed by OpenAI, stands as a titan in conversational AI, “Perplexity” pertains more to a performance metric used in evaluating language models. Introduction In artificial intelligence, particularly in natural language processing, two terms often come up: Perplexity and ChatGPT.

Metrics 317

Metrics Modeling Analytics Deep Learning 317

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. This view actually delivers four out of the five efficiency metrics that we discussed in the previous blog post.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

KDnuggets

DECEMBER 11, 2019

AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments and Key Trends; Down with technical debt! the most relevant Metrics in a Nutshell. Clean #Python for #DataScientists; Calculate Similarity?-?the

Deep Learning 54

Deep Learning Machine Learning Data Science Metrics 54

Business Over Broadway

DECEMBER 20, 2018

The importance of data science and machine learning continues to grow in business and beyond. Favorite Data Science and Machine Learning Blogs, Podcasts and Newsletters – In a worldwide survey, over 16,000 data professionals were asked to indicate their favorite data science blogs, podcasts and newsletters.

Machine Learning 74

Machine Learning Data Science Deep Learning Predictive Analytics 74

DataRobot

FEBRUARY 10, 2021

Machine learning is a glass cannon. The promise and power of AI lead many researchers to gloss over the ways in which things can go wrong when building and operationalizing machine learning models. As a data scientist, one of my passions is to reproduce research papers as a learning exercise.

Modeling 98

Modeling Deep Learning Machine Learning Metrics 98

CIO Business Intelligence

JANUARY 24, 2024

It’s the culmination of a decade of work on deep learning AI. Deep learning AI: A rising workhorse Deep learning AI uses the same neural network architecture as generative AI, but can’t understand context, write poems or create drawings. You probably know that ChatGPT wasn’t built overnight.

Deep Learning 112

Deep Learning Unstructured Data Machine Learning Modeling 112

KDnuggets

OCTOBER 16, 2019

This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.

Metrics 62

Metrics Deep Learning Machine Learning Modeling 62

Domino Data Lab

FEBRUARY 10, 2019

This blog post provides insights into why machine learning teams have challenges with managing machine learning projects. Why are Machine Learning Projects so Hard to Manage? I’ve watched lots of companies attempt to deploy machine learning?—?some Why is this?

Machine Learning 63

Machine Learning Modeling Deep Learning Metrics 63

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

CIO Business Intelligence

AUGUST 13, 2024

Unlike siloed or shallow automation efforts, deep automation architects a perspective that integrates customer experiences, value streams, human-machine collaboration, and synergistic technologies to create intelligent, self-adjusting businesses. John Deere’s precision agriculture exemplifies deep automation.

Data-driven 130

Data-driven Metrics Optimization Machine Learning 130

IBM Big Data Hub

DECEMBER 19, 2023

Anomaly detection simply means defining “normal” patterns and metrics—based on business functions and goals—and identifying data points that fall outside of an operation’s normal behavior. A machine learning model trained with labeled data will be able to detect outliers based on the examples it is given.

Machine Learning 64

Machine Learning Optimization Unstructured Data Sales 64

Sisense

APRIL 10, 2020

These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. When we say “modeling” in data science, we mean teaching a program to learn from training data using machine learning algorithms. Again, the library you use will be based on your use case.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

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

Cloudera

FEBRUARY 25, 2021

Cloudera has a front-row seat to organizational challenges as those enterprises make Machine Learning a core part of their strategies and businesses. The work of a machine learning model developer is highly complex. Here’s a preview of what you can leverage with one click in CML: Deep Learning for Anomaly Detection.

Deep Learning 84

Deep Learning Machine Learning Visualization Forecasting 84

CIO Business Intelligence

AUGUST 15, 2024

A virtual assistant may save employees time when searching for old documents or composing emails, but most organizations have no idea how much time those tasks have taken historically, having never tracked such metrics before, she says. There are a lot of cool AI solutions that are cheaper than generative AI,” Stephenson says.

ROI 143

ROI Cost-Benefit Experimentation Deep Learning 143

DataRobot

DECEMBER 20, 2021

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. With frameworks like Tensorflow , Keras , Pytorch, etc.,

Visualization 97

Visualization Deep Learning Machine Learning Modeling 97

Smart Data Collective

JULY 1, 2023

Image annotation is the act of labeling images for AI and machine learning models. The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning.

Machine Learning 93

Machine Learning Metrics Uncertainty Deep Learning 93

CIO Business Intelligence

JUNE 21, 2022

In addition to quantitative ROI metrics, HPC research was also shown to save lives, lead to important public/private partnerships, and spur innovations. . Real-time big data analytics, deep learning, and modeling and simulation are newer uses of HPC that governments are embracing for a variety of applications. HPC Growth in U.S.

Deep Learning 111

Deep Learning Big Data ROI Software 111

DataRobot Blog

FEBRUARY 14, 2022

Because our dataset contains image data, DataRobot used models that contain deep learning based image featurizers. Typically this means finding out how many predictions have been made, how many requests have been made to the deployment, and other performance-related metrics. Learn More About Explainable AI. Learn more.

Machine Learning 52

Machine Learning Metrics Sales Modeling 52

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029.

Machine Learning 48

Machine Learning Marketing Forecasting Modeling 48

Cloudera

OCTOBER 5, 2020

Cloudera announced today a new collaboration with NVIDIA that will help Cloudera customers accelerate data engineering, analytics, machine learning and deep learning performance with the power of NVIDIA GPU computing across public and private clouds. CDP enables enterprise customers to leverage Apache Spark 3.0

Enterprise 93

Enterprise Machine Learning Deep Learning Data Science 93

Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. You could cluster (k=2) on NPS scores (a customer evaluation metric) then replace the Democrat/Republican dimension with the top two components from the clustering. for a, b in pairs:?. print(a, b, lic[a].similarity(lic[b])).

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 80

Metrics Machine Learning Statistics Risk 80

Sisense

AUGUST 6, 2020

AI and machine learning (ML) are not just catchy buzzwords; they’re vital to the future of our planet and your business. So what are the high-level steps to incorporate AI and machine learning into new and existing products? An obvious mechanical answer is: use relevance as a metric.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

CIO Business Intelligence

JUNE 1, 2022

and artificial intelligence (AI) and machine learning (ML) technologies. . Aside from monitoring components over time, sensors also capture aerodynamics, tire pressure, handling in different types of terrain, and many other metrics. which are virtual models of objects, systems, or processes ? Just starting out with analytics?

Manufacturing 119

Manufacturing Digital Transformation Predictive Analytics Strategy 119

DataRobot Blog

DECEMBER 15, 2022

AI-powered Time Series Forecasting may be the most powerful aspect of machine learning available today. The machine learning life cycle always starts with the dataset. The order of the models will be based on the project’s metric—and can be changed based on your configuration. Setting up a Time Series Project.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81