Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

O'Reilly on Data

JULY 28, 2020

In this article, we turn our attention to the process itself: how do you bring a product to market? You must detect when the model has become stale, and retrain it as necessary. Product managers for AI must satisfy these same responsibilities, tuned for the AI lifecycle. Identifying the problem.

Marketing 362

Marketing Experimentation Metrics Testing 362

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task.

Enterprise 127

Enterprise Modeling Testing Data-driven 127

Rocket-Powered Data Science

JULY 6, 2023

This is not entirely due to or related to ChatGPT and LLMs (large language models), though those have been the main drivers. For a short introduction to generative AI, see my article “ Generative AI – Chapter 1, Page 1 ”.

Strategy 152

Strategy Enterprise Software Marketing 152

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. We’ll actually do this later in this article. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. Modeling in R and Python. R libraries.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Ontotext

DECEMBER 15, 2023

It includes only ML papers and related entities; this SPARQL query shows some statistics: papers tasks models datasets methods evaluations repos 376557 4267 24598 8322 2101 52519 153476 We can start with these repositories (most of them are on Github) and get all their topics. We use Categories as a way of finding relevant articles.

Machine Learning 69

Machine Learning Interactive Deep Learning Statistics 69

Analytics Vidhya

MAY 23, 2023

But have you ever wondered what it takes to become an artificial intelligence engineer This article will equip you with the essential information to take the first steps […] The post How to Become an AI Engineer in 2023? AI is revolutionizing industries and transforming our daily lives, from self-driving cars to virtual assistants.

Analytics 302

Analytics Deep Learning IT Machine Learning 302

Smart Data Collective

SEPTEMBER 25, 2023

Have you ever wondered what it would be like if machines could learn to speak every language in the world? In this article, how does AI translation work ? Additionally, the introduction of neural machine translation models has revolutionized the field. Brace yourself, because the future is here.

Deep Learning 98

Deep Learning Statistics Technology Machine Learning 98

Domino Data Lab

DECEMBER 1, 2019

This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production. Introduction: preventing silent model degradation in production. Model monitoring: the approach. Run the example in a complementary Domino project.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

Smart Data Collective

NOVEMBER 1, 2020

With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning? If you’ve ever come across deep learning, you might have heard about two methods to teach machines: supervised and unsupervised. Unsupervised ML: The Basics.

Machine Learning 133

Machine Learning Data mining Visualization Statistics 133

Smart Data Collective

MAY 8, 2022

Brands are closely working to solve this as they dive deep into the world of big data analytics. Well, don’t go anywhere because, in this article, we will show you how you can use big data analytics combined with AI to achieve the best performance possible. What is the relationship between big data analytics and AI?

Big Data 106

Big Data Data Analytics Analytics Cost-Benefit 106

Sisense

APRIL 15, 2021

Augmented analytics (according to Gartner, which would know), uses technologies “such as machine learning [ML] and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms.” Every analytics user can benefit from this type of augmentation.

Analytics 98

Analytics Data Science Modeling Machine Learning 98

Occam's Razor

MARCH 30, 2017

People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Most Deep Learning methods involve artificial neural networks, modeling how our bran works.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

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. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

Cost-Benefit 97

Cost-Benefit Modeling Manufacturing Interactive 97

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. In this article, take a deep dive into data science and how Domino’s Enterprise MLOps platform allows you to scale data science in your business. Models could be consumer-centric, like identifying key consumer groups.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

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

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

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

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

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. He was saying this doesn’t belong just in statistics. Key highlights from the session include.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Smarten

APRIL 19, 2024

In this article, we will discuss the current state of AI in analytics, as well as the future of this burgeoning industry and how it can be applied to analytics to simplify and clarify results and to make analytics easier for businesses and business users to leverage. GPT – Is based on a Large Language Model (LLM).

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

DECEMBER 17, 2019

This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. and webinar provide more depth than this blog post as they cover an implementation of a basic GAN model and demonstrate how adversarial networks can be used to generate training samples. Introduction.

Deep Learning 64

Deep Learning Machine Learning Modeling Statistics 64

O'Reilly on Data

FEBRUARY 18, 2020

Usage specific to Python as a programming language grew by just 4% in 2019; by contrast, usage that had to do with Python and ML—be it in the context of AI, deep learning, and natural language processing, or in combination with any of several popular ML/AI frameworks—grew by 9%. We’re appropriating them differently.

Data-driven 335

Data-driven Software Statistics Marketing 335

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

MARCH 3, 2019

Seriously, this entire article merely skims the surface of those reports. Check the end of this article for key guidance synthesized from the practices of the leaders in the field. Ben and I also wrote articles for each of the surveys, summarizing the highlights. Who builds their models?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Data Science and Beyond

OCTOBER 15, 2017

Here are my thoughts from 2014 on defining data science as the intersection of software engineering and statistics , and a more recent post on defining data science in 2018. The hardest parts of data science are problem definition and solution measurement, not model fitting and data cleaning , because counting things is hard.

Data Science 71

Data Science Deep Learning Machine Learning Data-driven 71

Domino Data Lab

JUNE 23, 2019

It used deep learning to build an automated question answering system and a knowledge base based on that information. It’s harder for many folks in this role who don’t have any data or machine learning background and are thrown or thrust into shipping something like this. You really have to just go through it.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238

Domino Data Lab

JULY 9, 2019

Assessing whether a business stakeholder is trying to solve for a problem that is descriptive, predictive, or prescriptive and then re-framing the problem as supervised learning, unsupervised learning, or reinforcement learning, respectively. In 2001, Bill Cleveland writes this article saying, “You are doing it wrong.”

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

JANUARY 25, 2021

Models are neither code nor data, and we don’t have adequate tools for versioning models either (though tools like MLflow are a start). Frequent deployment assumes that the software can be built relatively quickly, but training a model can take days. AI, Machine Learning, and Data.

Machine Learning 363

Machine Learning Software Testing Technology 363