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 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

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 100

Testing Software Optimization Marketing 100

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

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 Metrics 130

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

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. The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded.

Marketing 361

Marketing Experimentation Metrics Testing 361

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 Reporting Internet of Things 190

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

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 193

Modeling Machine Learning Testing Metrics 193

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 108

Machine Learning Deep Learning Modeling Interactive 108

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

IBM Big Data Hub

APRIL 9, 2024

” Watsonx.data uses machine learning (ML) applications to simulate data that represents ball positioning projections. “We can keep track of the model version we use, promote it to validation, and eventually deploy it to production once we feel confident that all the metrics are passing our quality estimates. .

Management 82

Management IT Machine Learning Metrics 82

DataKitchen

JULY 27, 2023

You can use it for big data analytics and machine learning workloads. Machine learning workflows with Azure Machine Learning and Azure Databricks: Azure Databricks can be used to preprocess and clean data, then the transformed data can be stored in Azure Blob Storage. Azure Machine Learning).

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

CIO Business Intelligence

MAY 1, 2024

Gen AI takes us from single-use models of machine learning (ML) to AI tools that promise to be a platform with uses in many areas, but you still need to validate they’re appropriate for the problems you want solved, and that your users know how to use gen AI effectively. Now nearly half of code suggestions are accepted.

Cost-Benefit 142

Cost-Benefit Metrics Insurance Measurement 142

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 327

Management Machine Learning Experimentation Metrics 327

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 83

Metrics Machine Learning Risk Statistics 83

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 Testing 246

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 363

Management Experimentation B2B Machine Learning 363

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 240

Management Metrics Machine Learning Modeling 240

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 Testing 98

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

AWS Big Data

MARCH 11, 2024

Managed Service for Apache Flink manages the underlying infrastructure and Apache Flink components that provide durable application state, metrics, logs, and more. However, it’s crucial to recognize that this reduction could adversely affect performance if not thoroughly assessed and tested.

Management 99

Management Snapshot Metrics Cost-Benefit 99

Smart Data Collective

JULY 9, 2023

Understanding E-commerce Conversion Rates There are a number of metrics that data-driven e-commerce companies need to focus on. It is a crucial metric that provides priceless information about your website’s ability to transform visitors into paying customers. Some of the most important is conversion rates.

Optimization 74

Optimization Data Analytics Analytics Data-driven 74

AWS Big Data

MARCH 27, 2024

By testing the solution against key metrics, a POC provides insights that allow you to make an informed decision on the suitability of the technology for the intended use case. Complete the implementation tasks such as data ingestion and performance testing. Collect data metrics and statistics on the completed tasks.

Testing 101

Testing Data Warehouse Metrics Cost-Benefit 101

IBM Big Data Hub

APRIL 13, 2023

After developing a machine learning model, you need a place to run your model and serve predictions. Someone with the knowledge of SQL and access to a Db2 instance, where the in-database ML feature is enabled, can easily learn to build and use a machine learning model in the database. Create a TRAIN partition.

Modeling 85

Modeling Statistics Machine Learning Testing 85

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 129

Data Quality Machine Learning Digital Transformation Big Data 129

DataKitchen

JANUARY 19, 2022

Imagine a large enterprise yielding significant value from their Matillion-Snowflake integration, but wishing to expand the scope of data pipeline deployment, testing, and monitoring. DataKitchen triggers a Matillion job, then retrieves execution parameters that can be used in DataKitchen tests. Parameterizing Matillion JSON Files.

Testing 130

Testing Data Integration Data Warehouse Enterprise 130

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 133

Visualization Optimization Analytics Testing 133

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

JUNE 10, 2022

For data scientists, we continue to provide new Applied Machine Learning Prototypes (AMPs), which are open source and available on GitHub. In Cloudera Machine Learning (CML), you can deploy them with the single click of a button, bringing data scientists that much closer to providing value. Continuous Model Monitoring.

Machine Learning 99

Machine Learning Data Science Modeling Metrics 99

AWS Big Data

JANUARY 16, 2024

The need for Amazon MWAA disaster recovery Amazon MWAA, a fully managed service for Apache Airflow , brings immense value to organizations by automating workflow orchestration for extract, transform, and load (ETL), DevOps, and machine learning (ML) workloads. If the metric is deemed healthy, no action is taken.

Strategy 102

Strategy Metadata Metrics Dashboards 102

Cloudera

AUGUST 25, 2020

Introducing Model Monitoring & Metrics Store In Cloudera Data Science Workbench. With only about 35% of machine learning models making into production in the enterprise ( IDC ), it’s no wonder that production machine learning has become one of the most important focus areas for data scientists and ML engineers alike.

Machine Learning 76

Machine Learning Metrics Modeling Enterprise 76

Smart Data Collective

JANUARY 4, 2023

These insights can help hospitalists track claim rejections, accounts receivable aging, and other metrics to create measurable improvement goals. This data helps identify patterns and trends that indicate a need for further testing or treatment, allowing for more accurate diagnoses. Improving Diagnostics Through Wearables.

Technology 111

Technology Big Data Metrics Data-driven 111

AWS Big Data

AUGUST 9, 2023

The advent of rapid adoption of serverless data lake architectures—with ever-growing datasets that need to be ingested from a variety of sources, followed by complex data transformation and machine learning (ML) pipelines—can present a challenge. Athena database – The database where the monitoring metrics are persisted for analysis.

Data Lake 90

Data Lake Metrics Testing Cost-Benefit 90

CIO Business Intelligence

JULY 5, 2022

It uses data mining, data modeling, and machine learning to answer “why” something happened and predict what might happen in the future. Predictive analytics is the use of techniques such as statistical modeling, forecasting, and machine learning to make predictions about future outcomes. Business analytics techniques.

Business Analytics 131

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 131

Cloudera

JULY 24, 2023

As a result, the platform development team needs to test many different combinations to ultimately identify the right major / minor version of each project that properly integrates with the rest of the custom distribution. data engineering pipelines, machine learning models).

Analytics 66

Analytics Testing Metrics Management 66