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.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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.” The result will be unusable if a user prompts the model to write a factual news article.

Enterprise 119

Enterprise Modeling Testing Data-driven 119

Domino Data Lab

MARCH 11, 2021

Companies can choose to build their own custom machine learning framework, but most organizations choose an existing framework that fits their needs. In this article, we’ll show key considerations for selecting the right machine learning framework for your project and briefly review four popular ML frameworks. Tensorflow 2.0,

Machine Learning 143

Machine Learning Deep Learning Insurance Modeling 143

Smart Data Collective

FEBRUARY 20, 2022

In this article we will list 10 things AWS can do for your SaaS company. This article finally gets to the core question we started with: what can AWS do for your SaaS business? AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms. Let’s just dive right in.

Cost-Benefit 116

Cost-Benefit Data Lake Software Machine Learning 116

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

OCTOBER 14, 2020

This article focuses on accelerating model development. We needed an “evolvable architecture” which would work with the next deep learning framework or compute platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Hyperparameter Tuning.

Experimentation 99

Experimentation Deep Learning Modeling Testing 99

BizAcuity

MARCH 31, 2022

There have been so many articles published about AI and its applications, you can find millions of articles from broad concepts to deep technical literature on the internet. This article (like thousands of other articles), is aimed at presenting consolidated information about AI for business in simple language.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

IBM Big Data Hub

JULY 6, 2023

The evolution of machine learning The start of machine learning, and the name itself, came about in the 1950s. In 1950, data scientist Alan Turing proposed what we now call the Turing Test , which asked the question, “Can machines think?” Python is the most common programming language used in machine learning.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

IBM Big Data Hub

APRIL 18, 2024

Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. ” AGI analyzes relevant code, generates a draft function with comments explaining its logic and allows the programmer to review, optimize and integrate it.

Cost-Benefit 89

Cost-Benefit Modeling Manufacturing Interactive 89

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. SQL optimization provides helpful analogies, given how SQL queries get translated into query graphs internally , then the real smarts of a SQL engine work over that graph. Introduction. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

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. In the context of machine learning, we consider data drift 1 to be the change in model input data that leads to a degradation of model performance. Detecting image drift. References.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

Domino Data Lab

JULY 28, 2021

In this article, we will provide an overview of the three overlapping components of data science, the importance of communication and collaboration, and how the Domino Data Lab MLOps platform can help improve the speed and efficiency of your team. These changes often personify themselves as square roots, lambdas or inverted matrices.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

CIO Business Intelligence

APRIL 24, 2023

How we got here The most notable enabling technologies in generative AI are deep learning, embeddings, transfer learning (all of which emerged in the early to mid-2000s), and neural net transformers (invented in 2017). Deep learning emerged in academia in the early 2000s, with broader industry adoption starting around 2010.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

O'Reilly on Data

JANUARY 25, 2021

Both SRE and DevOps emphasize similar practices: version control (62% growth for GitHub, and 48% for Git), testing (high usage, though no year-over-year growth), continuous deployment (down 20%), monitoring (up 9%), and observability (up 128%). It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 363

Machine Learning Software Testing Technology 363

Cloudera

FEBRUARY 16, 2022

In this article, we explore model governance, a function of ML Operations (MLOps). 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. Figure 04: Applied Machine Learning Prototypes (AMPs).

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

IBM Big Data Hub

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

Deep Learning 57

Deep Learning Machine Learning Unstructured Data Modeling 57

Domino Data Lab

JULY 28, 2021

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. Reinforcement learning focuses on optimizing a specific decision. The Data Science Toolkit.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

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. The data types used in deep learning are interesting.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

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. deep learning) there is no guaranteed explainability. This is to prevent any information leakage into our test set.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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. Introduction. Welcome back to our monthly burst of themes and conferences.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Insight

JANUARY 14, 2020

Keras is an open source deep learning API that was written in Python and runs on top of Tensorflow, so it’s a little more user-friendly and high-level than Tensorflow. Check out this great Medium article that discusses the difference between tensors and multidimensional arrays. Check out the Keras documentation here.

Metrics 58

Metrics Optimization Modeling Testing 58

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

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

O'Reilly on Data

OCTOBER 19, 2021

This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. Not only is data larger, but models—deep learning models in particular—are much larger than before. An Overarching Concern: Correctness and Testing.

IT 351

IT Testing Experimentation Software 351

Occam's Razor

OCTOBER 12, 2017

When you go to the interview, the hiring company will proceed to ask questions that test your competency in the listed job requirements. Test for analytics experience AND explore the level of analytical thinking the job candidate possesses. Optimal Starting SCOTUS Starting Points. I read articles they write.

Marketing 136

Marketing Analytics Machine Learning Strategy 136

O'Reilly on Data

JUNE 7, 2022

We have a lot of vague notions about the Turing test, but in the final analysis, Turing wasn’t offering a definition of machine intelligence; he was probing the question of what human intelligence means. And granted, a lot can be done to optimize training (and DeepMind has done a lot of work on models that require less energy).

Modeling 363

Modeling Interactive Optimization Deep Learning 363

IBM Big Data Hub

JANUARY 10, 2024

In this article, we’ll discuss how AI technology functions and lay out the advantages and disadvantages of artificial intelligence as they compare to traditional computing methods. For optimal performance, AI models should receive data from a diverse datasets (e.g., What is artificial intelligence and how does it work?

Unstructured Data 75

Unstructured Data Deep Learning Cost-Benefit Machine Learning 75

DataRobot Blog

DECEMBER 20, 2022

In this article, we’ll showcase the ability of AI to improve the quality of the potential investment’s future performance, with a specific example from the real estate segment. In this article, we’ll first take a closer look at the concept of Real Estate Data Intelligence and the potential of AI to become a game changer in this niche.

Management 59

Management Machine Learning Optimization Modeling 59