IBM Big Data Hub

APRIL 9, 2024

.” Watsonx.data uses machine learning (ML) applications to simulate data that represents ball positioning projections. ” Training and testing models The Masters digital team used watsonx.ai “We used few-shot learning to help guide the models,” says Baughman.

Management 87

Management IT Machine Learning Metrics 87

Analytics Vidhya

JANUARY 20, 2022

This article was published as a part of the Data Science Blogathon.

Modeling 328

Modeling Testing Data Science Publishing 328

O'Reilly on Data

NOVEMBER 29, 2022

While it’s unlikely that the bill will become law, merely raising the possibility of criminal prosecution and jail time has upped the ante for “ commercial entities that operate high-risk information systems or automated-decision systems, such as those that use artificial intelligence or machine learning.”. is worth reading.

Machine Learning 278

Machine Learning Recreation/Entertainment Risk Testing 278

Jen Stirrup

SEPTEMBER 30, 2021

Although CRISP-DM is not perfect , the CRISP-DM framework offers a pathway for machine learning using AzureML for Microsoft Data Platform professionals. They may also learn from evidence, but the data and the modelling fundamentally comes from humans in some way. Once the model has been trained, it will need to be tested.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

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. What is MLOps?

Data Science 58

Data Science Machine Learning Cost-Benefit Deep Learning 58

CIO Business Intelligence

APRIL 11, 2022

In December 2021, Broadcom Software published our blog: Predictions for 2022. We’re already seeing a range of activities, such as AI-driven services and development tools with AI bots being used in software testing. It must go through various stages from ideation and product definition to strategic designing, coding and testing.

Software 96

Software Data-driven Machine Learning Testing 96

O'Reilly on Data

JULY 15, 2019

As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. What cultural and organizational changes will be needed to accommodate the rise of machine and learning and AI?

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

O'Reilly on Data

MAY 18, 2020

And last is the probabilistic nature of statistics and machine learning (ML). Most AI models decay overtime: This phenomenon, known more widely as model decay , refers to the declining quality of AI system results over time, as patterns in new data drift away from patterns learned in training data.

Risk 359

Risk Modeling Data Processing Software 359

Insight

MAY 14, 2020

Machine Learning - ML algorithms that use frequency counts of n-grams of bases as an embedding have shown good accuracy in sorting metagenomic reads, but do not work well for sequences shorter than about 500 bases due to poor statistics (for example, see the TETRA algorithm). Interested in seeing more like this?

Deep Learning 98

Deep Learning Statistics Modeling Machine Learning 98

DataRobot

JULY 6, 2021

The Behavioural Insights Team ran an experiment, testing four variations to the language used in reminder letters on a trial group. Recently published research shows that AIs can match the performance of top human sales staff, achieving four times more sales than your inexperienced sales staff.

Marketing 105

Marketing Sales Data-driven Statistics 105

Analytics Vidhya

JUNE 16, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Greetings, I am Mustafa Sidhpuri a Computer Science and Engineering student. The post Understanding The Concept Of Hypothesis In Data Science! appeared first on Analytics Vidhya.

Data Science 191

Data Science Publishing Analytics Testing 191

Cloudera

JUNE 28, 2022

This blog will be published in two parts. For each transaction, NiFi makes a call to a production model in Cloudera Machine Learning (CML) to score the fraud potential of the transaction. A dashboard feeds from the Kudu database to show fraud summary statistics. This is what we call the first-mile problem.

Dashboards 86

Dashboards Machine Learning Statistics KPI 86

Smart Data Collective

SEPTEMBER 15, 2019

Countless financial professionals are looking towards machine learning and other new tools to improve the quality of the services that they offer to their customers. Hussain of Atos Spain published a white paper on the growing relevance of big data in the finance and insurance verticals.

Big Data 86

Big Data Machine Learning Risk Insurance 86

Cloudera

DECEMBER 3, 2021

In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI. Tracking key metrics and statistical distributions over time and alerting humans when either of these significantly drift can ensure that systems remain performant and fair. We need to get to the root of the problem.

Uncertainty 103

Uncertainty Measurement Metrics Risk 103

Insight

AUGUST 14, 2019

Photo by Kaleidico on Unsplash You’ve studied your share of computer science algorithms/statistics/machine learning/system design for the on-site interview at your favorite tech company. Talk out loud as you solve LeetCode problems and design machine learning models by yourself?—?it The reasons may surprise you.

Testing 54

Testing Statistics Machine Learning Data Science 54

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Galbraith eventually opted to publish Cuckoo’s Calling through an acquaintance of sorts. but the publishing industry failed to see it. often without even looking at it. In other words, Galbraith had chops?—?but

Modeling 59

Modeling Metadata Publishing Sales 59

Insight

APRIL 28, 2020

You’ll often see the name “data challenge” used when the take-home assignment involves machine learning or statistics or “coding challenge” when the focus is on evaluating a candidate’s software engineering skills. Length: Highly Variable.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

Domino Data Lab

AUGUST 9, 2021

With the right tools, your data science teams can focus on what they do best – testing, developing and deploying new models while driving forward-thinking innovation. Before selecting a tool, you should first know your end goal – machine learning or deep learning. Machine Learning Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). billion, with the market growing by 31.1%

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

AWS Big Data

APRIL 12, 2023

Also, you can run other types of business applications, such as web applications and machine learning (ML) TensorFlow workloads, on the same EKS cluster. We also share a Spark benchmark solution that suits all Amazon EMR deployment options, so you can replicate the process in your environment for your own performance test cases.

Testing 71

Testing Big Data Metadata Optimization 71

Domino Data Lab

MAY 8, 2019

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. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 8, 2019

Rather than publish an agenda as most technology conferences do, why not let people mingle, discuss, and propose topics and possible sessions? Latest in machine learning research centers rapidly expanding in Africa? then building machine learning models to recommend methods and potential collaborators to scientists.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Smart Data Collective

JULY 15, 2022

Here are some statistics on the changes AI is creating : A report by Gartner shows that companies are projected to spend over $62 billion on AI this year alone. Launch the Perfect Software Publishing Business by Creating Stellar AI Applications. It would allow you to test all the features and get customer feedback.

Software 96

Software Key Performance Indicator Marketing Modeling 96

AWS Big Data

SEPTEMBER 13, 2023

AWS has invested in native service integration with Apache Hudi and published technical contents to enable you to use Apache Hudi with AWS Glue (for example, refer to Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started ). This concludes the demo.

Data Lake 87

Data Lake Data Processing Metadata Snapshot 87

AWS Big Data

APRIL 28, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. For example, you can configure an Amazon EventBridge rule to invoke an AWS Lambda function to publish CloudWatch metrics every time AWS Glue jobs finish.

Optimization 90

Optimization Metrics Interactive Data Integration 90

Domino Data Lab

AUGUST 10, 2021

At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. There are various types of machine learning approaches, including: Supervised learning: used when you know what the model needs to learn, typically in prediction, regression or classification.

Modeling 64

Modeling IT Risk Management Deep Learning 64

datapine

SEPTEMBER 6, 2022

Story turnaround time : This indicator is not exclusive to online publications, it has been used for decades in the publishing industry to measure the time since an article is approved to the time it is actually published. Flesch reading ease: The Flesch reading ease is a readability test developed in the U.S

Dashboards 179

Dashboards Reporting ROI Metrics 179

Cloudera

NOVEMBER 3, 2021

Today, deep learning and GPUs are practically synonymous. While deep learning is an excellent use of the processing power of a graphics card, it is not the only use. Cloudera Machine Learning (CML) is one of many Data Services available in the Cloudera Data Platform. In fact only 43.2% Checkmate reason 2.

Deep Learning 120

Deep Learning Machine Learning Data Science Cost-Benefit 120

IBM Big Data Hub

JULY 28, 2022

To take full advantage metadata enrichments, you’ll want to publish data assets, enriched with metadata‚ to a catalog where users can quickly find and access the right information. The workflow and user experience for discovering, enriching and publishing a large number of data assets from a source to a catalog has been entirely revisited.

Metadata 57

Metadata Data Quality Machine Learning Statistics 57

DataRobot

NOVEMBER 9, 2021

“One look is worth a Thousand Words” This phrase was used in 1913 to convey that graphics had a place in newspaper publishing. The dataset also provides centralized and windowed statistics features. More than a hundred years later, this phrase still rings true, especially for data scientists.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

Domino Data Lab

JULY 21, 2019

I’m at NYU, as you can tell by my strong New York accent, and I’ve spent most of my career working with federal statistical agencies, the agencies that bring you the Decennial Census, the unemployment rate, GDP, and so on. A statistical agency collected it, curated it, documented it, sent it out. Let me tell you the story.

Metadata 65

Metadata Statistics Risk Machine Learning 65

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. I’ve also dabbled in deep learning , marine surveys , causality , and other things that I haven’t had the chance to write about.

Data Science 71

Data Science Deep Learning Machine Learning Data-driven 71

Domino Data Lab

JULY 15, 2021

IDEs (also sometimes referred to as notebooks) is a coding tool that makes it easier to develop and test code. RStudio is an IDE for the R language used primarily for statistical analysis as well as data visualization. Launchpad: Allows you to publish your deployed models in different formats.

Data Science 145

Data Science Visualization Modeling Enterprise 145

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

In conferences and research publications, there is a lot of excitement these days about machine learning methods and forecast automation that can scale across many time series. Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Cloudera

APRIL 9, 2021

Conversely, modern data warehouses accommodate multiple versions and environments that support both innovation (fast interaction with the latest versions) and operational stability with tried and tested versions. . Exploratory Data Analysis (EDA) typically consists of both statistical and visual exploration of data. Conclusion.

Data Warehouse 59

Data Warehouse Cost-Benefit Statistics Data-driven 59

Smarten

AUGUST 9, 2017

You may also have read the recent Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics’ , Published 27 July 2017, by Rita L. Users can perform advanced analytics in an easy-to-use, drag and drop interface without knowledge of statistical analysis or algorithms.

Analytics 40

Analytics IT Statistics ROI 40