O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Why aren’t traditional software tools sufficient?

Modeling 257

Modeling Machine Learning Testing Metrics 257

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 363

Management Machine Learning Experimentation Metrics 363

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. data platform, metrics, ML/AI research, and applied ML). This is both an advantage and a disadvantage!

Management 364

Management Experimentation B2B Machine Learning 364

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”. require not only disclosure, but also monitored testing.

Management 308

Management Machine Learning Metrics Modeling 308

Cloudera

NOVEMBER 13, 2024

Build and test training and inference prompts. Fine Tuning Studio ships with powerful prompt templating features, so users can build and test the performance of different prompts to feed into different models and model adapters during training. We can then test the prompt against the dataset to make sure everything is working properly.

Cost-Benefit 87

Cost-Benefit Data Processing Machine Learning Testing 87

DataRobot Blog

JULY 21, 2022

Download the Machine Learning Project Checklist. Planning Machine Learning Projects. Machine learning and AI empower organizations to analyze data, discover insights, and drive decision making from troves of data. More organizations are investing in machine learning than ever before.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

Cloudera

NOVEMBER 17, 2021

We are very excited to announce the release of five, yes FIVE new AMPs, now available in Cloudera Machine Learning (CML). In addition to the UI interface, Cloudera Machine Learning exposes a REST API that can be used to programmatically perform operations related to Projects, Jobs, Models, and Applications.

Machine Learning 124

Machine Learning Visualization Data Science Dashboards 124

AWS Big Data

DECEMBER 5, 2024

This type of structure is foundational at REA for building microservices and timely data processing for real-time and batch use cases like time-sensitive outbound messaging, personalization, and machine learning (ML). These metrics help us determine the attributes of the cluster usage effectively.

Metrics 78

Metrics Dashboards Testing Optimization 78

DataKitchen

APRIL 26, 2021

GSK had been pursuing DataOps capabilities such as automation, containerization, automated testing and monitoring, and reusability, for several years. Workiva also prioritized improving the data lifecycle of machine learning models, which otherwise can be very time consuming for the team to monitor and deploy.

Measurement 246

Measurement Metrics Data-driven Dashboards 246

DataKitchen

NOVEMBER 29, 2021

Model developers will test for AI bias as part of their pre-deployment testing. Quality test suites will enforce “equity,” like any other performance metric. Continuous testing, monitoring and observability will prevent biased models from deploying or continuing to operate. Companies Commit to Remote.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

O'Reilly on Data

JANUARY 17, 2023

” If none of your models performed well, that tells you that your dataset–your choice of raw data, feature selection, and feature engineering–is not amenable to machine learning. All of this leads us to automated machine learning, or autoML. Perhaps you need a different raw dataset from which to start.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

Corinium

JUNE 6, 2019

Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. There is usually a steep learning curve in terms of “doing AI right”, which is invaluable. What is the most common mistake people make around data?

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

Cloudera

DECEMBER 4, 2024

Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. System metrics, such as inference latency and throughput, are available as Prometheus metrics. Data teams can use any metrics dashboarding tool to monitor these.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Predictive Analytics – AI & machine learning. So let’s introduce Cloudera Machine Learning (CML) and discuss how it addresses the aforementioned silo issues.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

DataKitchen

OCTOBER 28, 2021

Machine learning (ML) models are computer programs that draw inferences from data — usually lots of data. Data teams should formulate equity metrics in partnership with stakeholders. Once targets are defined, data professionals can iterate on eliminating bias from machine learning models. What Is AI Bias?

Testing 130

Testing IT Manufacturing Machine Learning 130

Rocket-Powered Data Science

JULY 21, 2023

Here are four specific metrics from the report, highlighting the potentially huge enterprise system benefits coming from implementing Splunk’s observability and monitoring products and services: Four times as many leaders who implement observability strategies resolve unplanned downtime in just minutes, not hours or days.

Machine Learning 190

Machine Learning Metrics Internet of Things Reporting 190

CIO Business Intelligence

OCTOBER 25, 2022

To remain resilient to change and deliver innovative experiences and offerings fast, organizations have introduced DevOps testing into their infrastructures. However, introducing DevOps to mainframe infrastructure can be nearly impossible for companies that do not adequately standardize and automate testing processes before implementation.

Testing 105

Testing Software Optimization Marketing 105

AWS Big Data

NOVEMBER 27, 2024

Within seconds of transactional data being written into Amazon Aurora (a fully managed modern relational database service offering performance and high availability at scale), the data is seamlessly made available in Amazon Redshift for analytics and machine learning. Choose Test Connection. Choose Next if the test succeeded.

Data Warehouse 93

Data Warehouse Analytics Testing Sales 93

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. 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.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. testing every possible combination) Hyperparameter tuning is beneficial to some extent, but the real efficiency gains are in finding the right data.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you. A more general approach is to learn a Generalized Additive Model (GAM).

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

BizAcuity

APRIL 19, 2019

The answer lies in revolutionary machine learning and business analytics. Adaptive machine and business analytics, applying cutting-edge machine learning and other technologies are proving helpful in spotting anomalies among users in real-time and fighting this issue. ML and Business Analytics to the rescue.

Machine Learning 75

Machine Learning Metrics Data mining Business Analytics 75

DataKitchen

SEPTEMBER 21, 2023

In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. Moreover, advanced metrics like Percentage Regional Sales Growth can provide nuanced insights into business performance.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

CIO Business Intelligence

NOVEMBER 12, 2024

He did not get to the point of 100% specificity and confidence about exactly how this makes him happier and more productive through a quick one-and-done test of a use case or two. Make ‘soft metrics’ matter Imagine an experienced manager with an “open door policy.” Each workflow is aimed at a problem or opportunity to be solved.

ROI 72

ROI Uncertainty Metrics Testing 72

datapine

NOVEMBER 27, 2019

Often seen as the highest foe-friend of the human race in movies ( Skynet in Terminator, The Machines of Matrix or the Master Control Program of Tron), AI is not yet on the verge to destroy us, in spite the legit warnings of some reputed scientists and tech-entrepreneurs. 1 for data analytics trends in 2020.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. genetic counseling, genetic testing). underspecified) due to omitted metrics. Machine Learning and Predictive Modeling of Customer Churn.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. Below we will explain how to virtually eliminate data errors using DataOps automation and the simple building blocks of data and analytics testing and monitoring. .

Testing 214

Testing Manufacturing Data Quality Statistics 214

Smart Data Collective

FEBRUARY 9, 2022

Learn how DirectX visualization can improve your study and assessment of different trading instruments for maximum productivity and profitability. A growing number of traders are using increasingly sophisticated data mining and machine learning tools to develop a competitive edge. But first, What is DirectX Anyway?

Visualization 145

Visualization Optimization Analytics Testing 145

DataRobot

FEBRUARY 10, 2021

Machine learning is a glass cannon. The promise and power of AI lead many researchers to gloss over the ways in which things can go wrong when building and operationalizing machine learning models. As a data scientist, one of my passions is to reproduce research papers as a learning exercise.

Modeling 98

Modeling Deep Learning Machine Learning Metrics 98

CIO Business Intelligence

FEBRUARY 20, 2025

In addition, they can use statistical methods, algorithms and machine learning to more easily establish correlations and patterns, and thus make predictions about future developments and scenarios. If a database already exists, the available data must be tested and corrected.

Big Data 84

Big Data Measurement Visualization Machine Learning 84

Smart Data Collective

AUGUST 11, 2022

Sustaining the responsible use of machines. Human labeling and data labeling are however important aspects of the AI function as they help to identify and convert raw data into a more meaningful form for AI and machine learning to learn. Therefore, algorithm testing and training on data quality are necessary.

Data Quality 139

Data Quality Machine Learning Digital Transformation Big Data 139

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

CIO Business Intelligence

APRIL 8, 2025

Invest in AI-powered quality tooling AI and machine learning are transforming data quality from profiling and anomaly detection to automated enrichment and impact tracing. Use machine learning models to detect schema drift, anomalies and duplication patterns and provide real-time recommended resolutions. Synthetic data.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

CIO Business Intelligence

AUGUST 9, 2024

At Atlanta’s Hartsfield-Jackson International Airport, an IT pilot has led to a wholesale data journey destined to transform operations at the world’s busiest airport, fueled by machine learning and generative AI. Applying AI to elevate ROI Pruitt and Databricks recently finished a pilot test with Microsoft called Smart Flow.

Data Transformation 126

Data Transformation Machine Learning Data Lake Dashboards 126

CIO Business Intelligence

NOVEMBER 27, 2024

LLMs can pass the bar exam or the medical board because those tests are too clean to be useful benchmarks, explains Swaminathan. Common data management practices are too slow, structured, and rigid for AI where data cleaning needs to be context-specific and tailored to the particular use case.

Enterprise 141

Enterprise Data Quality Structured Data Modeling 141

Domino Data Lab

JANUARY 11, 2021

PyCaret is a convenient entree into machine learning and a productivity tool for experienced practitioners. handling missing values with various imputation methods available), splitting into train and test sets, as well as some aspects of feature engineering and training. Image from github.com/pycaret. AutoML ( Optional ).

Modeling 145

Modeling Metrics Data Science Testing 145

Occam's Razor

OCTOBER 24, 2016

Step 1: Optimal Metrics. You'll find it here: Digital Metrics Ladder of Awesomeness. The metrics ladder lays out a path that will get you there, step by step while ensure your org is coming along with you. Step 1: Optimal Metrics. Tough metrics. Smart metrics. Wait, Wait, What the Heck is Attribution?

Metrics 147

Metrics Marketing Optimization Modeling 147