DataRobot

FEBRUARY 10, 2021

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. These errors might seem small, but the effects can be disastrous when the model is used to make decisions in the real world.

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

Analytics Vidhya

AUGUST 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction When training a machine learning model, the model can be easily overfitted or under fitted. To avoid this, we use regularization in machine learning to properly fit the model to our test set.

Machine Learning 301

Machine Learning Testing Data Science Publishing 301

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 data science? What is machine learning?

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

IBM Big Data Hub

AUGUST 11, 2023

These insights can help drive decisions in business, and advance the design and testing of applications. Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights.

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

Domino Data Lab

JULY 28, 2021

Data science is an exciting, interdisciplinary field that is revolutionizing the way companies approach every facet of their business. Data Science — A Venn Diagram of Skills. Data science encapsulates both old and new, traditional and cutting-edge. 3 Components of Data Science Skills.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

Domino Data Lab

JANUARY 20, 2019

What if there was a way to quantitatively measure whether your machine learning (ML) model reflects specific domain expertise or potential bias? Testing with Concept Activation Vectors (TCAV): The Zebra. Introduction. with post-training explanations? on a global level instead of a local level ?

Testing 63

Testing Modeling Machine Learning Data Science 63

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Data Science 101

MAY 20, 2019

You can get this information as the Microsoft Azure Data Scientist Checklist. Below is the basic structure of the DP-100: Designing and Implementing a Data Science Solution on Azure. Passing the exam will qualify you for the Azure Data Scientist Associate certification. R Power BI Publishing Azure ML models.

Data Science 111

Data Science Machine Learning Deep Learning Testing 111

Rocket-Powered Data Science

JULY 6, 2023

These AI applications are essentially deep machine learning models that are trained on hundreds of gigabytes of text and that can provide detailed, grammatically correct, and “mostly accurate” text responses to user inputs (questions, requests, or queries, which are called prompts). Guess what? It isn’t.

Statistics 151

Statistics Deep Learning Machine Learning Modeling 151

Insight

SEPTEMBER 20, 2019

Segmentation Since a few patients had multiple images in the dataset, the data were separated, by patient, into three parts: training (80%), validation (10%), and testing (10%). The model was a modified U-Net and trained on GPU hosted by Amazon Web Services (AWS) EC2 instances. Here, we built a model to mimic this process.

Deep Learning 53

Deep Learning Cost-Benefit Testing Modeling 53

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

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 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

DataRobot

JULY 27, 2021

In a global marketplace where decision-making needs to happen with increasing velocity, data science teams often need not only to speed up their modeling deployment but also do it at scale across their entire enterprise. Often, they are doing this with smaller teams in place than they need due to the shortage of data scientists.

Optimization 98

Optimization Modeling Machine Learning Deep Learning 98

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. Data Science Techniques.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources.

Marketing 362

Marketing Experimentation Metrics Testing 362

Domino Data Lab

JULY 17, 2021

The system here will identify, via some meaningful sense, which existing speakers’ model does the utterance match. If the unknown utterance is spoken by a speaker outside the list of existing speakers, the model will nonetheless map it to some speaker from that list. It determines whether the individual is who they claim to be.

Modeling 64

Modeling Testing Machine Learning Measurement 64

CIO Business Intelligence

JUNE 7, 2022

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? Data analytics methods and techniques. It is frequently used for risk analysis.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

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. The hardest parts of data science.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

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.” Demystifying generative AI At the heart of Generative AI lie massive databases of texts, images, code and other data types.

Enterprise 124

Enterprise Modeling Testing Data-driven 124

O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). Stanford Core NLP.

Deep Learning 227

Deep Learning Testing Machine Learning Modeling 227

CIO Business Intelligence

JUNE 1, 2022

The company is applying winning insights from rapid, data-driven, evolutionary models versus relying on engine speed and aerodynamics alone to win races. Like professional basketball, industrial-scale farming, national politics, and global merchandising, auto racing has become a data science. A Competitive Differentiator.

Manufacturing 119

Manufacturing Digital Transformation Predictive Analytics Strategy 119

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 87

Metrics Machine Learning Risk Statistics 87

CIO Business Intelligence

APRIL 25, 2022

But despite all the money flowing into ML projects, most organizations are struggling to get their ML models and applications working on production systems. . And another recent survey has the worst numbers of all, finding that 90% of ML models are not deployed to production. So what’s the problem? The problem with ML.

Machine Learning 134

Machine Learning Enterprise Deep Learning Cost-Benefit 134

CIO Business Intelligence

JUNE 2, 2022

HPC5’s performance level enables sophisticated in-house algorithms to process subsoil data, as well as geophysical and seismic information from around the world. For optimizing existing resources, Eni uses HPC5 to model, study, and ultimately improve refinement operations. .

Informatics 98

Informatics Modeling Testing Technology 98

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 95

Cost-Benefit Modeling Manufacturing Interactive 95

DataRobot

OCTOBER 20, 2021

Data literacy ensures that diverse perspectives are baked into AI governance and leads to the production of AI systems that achieve better and more consistent outcomes. It is a key component of developing responsible AI, and promotes trust not only in AI concepts but also in individual AI models. What Is Data Literacy?

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

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

Insight

OCTOBER 25, 2019

Image credit: [link] As the title suggests, this is a story about a question that may resonate well with many machine learning practitioners trying to build applications in the real world, where clean and annotated data on a specific problem can be sparse— How do we leverage the power of AI when we have very little data?

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

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. In the real word, data is recorded by different systems and is constantly changing.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. Data Has Properties. The accuracy of any predictive model approaches 100%.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Domino Data Lab

AUGUST 9, 2021

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. What Are Modeling Tools? Importance of Modeling Tools. Types of Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Insight

APRIL 9, 2019

Machine learning beyond the tech industry Machine learning (ML) has had an incredible impact across industries with numerous applications such as personalized TV recommendations and dynamic price models in your rideshare app. These models are then used to create software that can control the process toward a desired outcome.

Manufacturing 75

Manufacturing Machine Learning Testing Software 75

Insight

MAY 16, 2019

It is a high-level, multifaceted field that allows machines to iteratively learn and understand complex representations from images and videos to automate human visual tasks. How Deep Learning scales based on the amount of Data [Copyright: Andrew Ng ]. Transfer Learning?—?YOLO.

Deep Learning 84

Deep Learning Machine Learning Metadata Data Science 84

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

DataKitchen

APRIL 13, 2021

Testing and Data Observability. 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. . Genie — Distributed big data orchestration service by Netflix.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Domino Data Lab

JULY 15, 2021

In the multiverse of data science, the tool options continue to expand and evolve. While there are certainly engineers and scientists who may be entrenched in one camp or another (the R camp vs. Python, for example, or SAS vs. MATLAB), there has been a growing trend towards dispersion of data science tools.

Data Science 145

Data Science Visualization Modeling Enterprise 145