O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Because ML models can react in very surprising ways to data they’ve never seen before, it’s safest to test all of your ML models with sensitivity analysis. [9]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. MLOps and IBM Watsonx.ai

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

Risk 52

Risk Modeling Metrics Business Objectives 52

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

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. In fact, deep learning was first described theoretically in 1943.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

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

NOVEMBER 9, 2021

Signal classification models are typically built using time series principles; traditionally used features that include central, windowed, lag, and lead statistics can do the job but sometimes there might be scenarios where we want to eke out more performance out of the data. Image courtesy towardsAI.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

Smarten

APRIL 19, 2024

The Current State of AI in Analytics By combining machine learning with AI, analytics vendors can and have made strides in providing user-friendly, self-serve augmented analytics products that include the newest versions of these technologies: Machine Learning (ML) Generative Artificial Intelligence (GenAI) Deep Learning Natural Language Processing (..)

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns. Other deep learning models can also be written in this form.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

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. IDEs (also sometimes referred to as notebooks) is a coding tool that makes it easier to develop and test code. Some common IDEs are RStudio and Jupyter Notebook.

Data Science 145

Data Science Visualization Modeling Enterprise 145

Domino Data Lab

MAY 8, 2019

On the other hand, as Lipton emphasized, while the tooling produces interesting visualizations, visualizations do not imply interpretation. ML model interpretability and data visualization. From my experiences leading data teams, when a business is facing difficult challenges, data visualizations can help or hurt.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. As long as two modules both conform to the same set of standards, you can swap them out, and due to the shared characteristics of the modules, this aspect of Kubernetes can shorten your integration testing process.

Data Science 69

Data Science Machine Learning Big Data Interactive 69

Domino Data Lab

JUNE 23, 2019

Be aware that machine learning often involves working on something that isn’t guaranteed to work. As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. Testing is critical. It is similar to R&D.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MARCH 3, 2019

I’m here mostly to provide McLuhan quotes and test the patience of our copy editors with hella Californian colloquialisms. The data types used in deep learning are interesting. The data types used in deep learning are interesting. One-fifth use reinforcement learning. Or something. Technologies.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Smart Data Collective

NOVEMBER 9, 2022

There are a number of great applications of machine learning. One of the biggest benefits is testing processes for optimal effectiveness. The main purpose of machine learning is to partially or completely replace manual testing. One example is using machine learning tools like Selenium to test web development processes.

Machine Learning 99

Machine Learning Testing Cost-Benefit Data-driven 99

Domino Data Lab

JULY 9, 2019

A “data scientist” might build a multistage processing pipeline in Python, design a hypothesis test, perform a regression analysis over data samples with R, design and implement an algorithm in Hadoop, or communicate the results of our analyses to other members of the organization in a clear and concise fashion. .”

Data Science 112

Data Science Machine Learning Advertising Modeling 112