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. Machine learning adds uncertainty.

Management 332

Management Machine Learning Experimentation Metrics 332

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. If this sounds fanciful, it’s not hard to find AI systems that took inappropriate actions because they optimized a poorly thought-out metric. Agreeing on metrics.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

JUNE 7, 2022

But only in recent years, with the growth of the web, cloud computing, hyperscale data centers, machine learning, neural networks, deep learning, and powerful servers with blazing fast processors, has it been possible for NLP algorithms to thrive in business environments. Just starting out with analytics?

Unstructured Data 98

Unstructured Data Deep Learning Insurance Interactive 98

CIO Business Intelligence

MARCH 14, 2022

It is also the foundation of predictive analysis, artificial intelligence (AI), and machine learning (ML). Real-time Data Scaling Challenges. Several factors make such scaling difficult: Massive Data Growth: Global data creation is projected to exceed 180 zettabytes by 2025. Just starting out with analytics?

Data Processing 116

Data Processing Optimization Deep Learning Software 116

CIO Business Intelligence

APRIL 6, 2022

By giving leadership at all levels more timely access to critical data about business operations, businesses can see more of their options, make and automate better decisions, and further improve efficiency to drive higher customer satisfaction and profitability. Ready to evolve your analytics strategy or improve your data quality?

Data mining 72

Data mining Deep Learning Insurance Digital Transformation 72

CIO Business Intelligence

APRIL 11, 2022

The smart city solution incorporates video and sound data inputs from the area, integrated with publicly available, historical data sources, such as crime, weather and social media data. And it saves money for the City services as garbage collection rounds can be optimized. when a container is full.

IoT 145

IoT Measurement Cost-Benefit Deep Learning 145

CIO Business Intelligence

JUNE 1, 2022

Racing car design innovation and racing strategy are now dominated by what McLaren engineers call condition-based insights derived from real-time data feeds from hundreds of sensors in cars and the use of digital twins ? and artificial intelligence (AI) and machine learning (ML) technologies. . Just starting out with analytics?

Manufacturing 119

Manufacturing Digital Transformation Predictive Analytics Strategy 119

datapine

AUGUST 14, 2019

Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning. Our Top Data Science Tools.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

CIO Business Intelligence

JANUARY 4, 2023

Of course, data center challenges are also driving demand for these alternatives. Workflow Optimization from Edge to Core/Cloud and Back : Integration with edge devices as well as integration with different HPC systems is currently designed in-house or otherwise customized. Just starting out with analytics?

Cost-Benefit 59

Cost-Benefit Machine Learning Modeling Uncertainty 59

CIO Business Intelligence

APRIL 11, 2022

Using artificial intelligence (AI) and machine learning, more than 1.9 To create a productive, cost-effective analytics strategy that gets results, you need high performance hardware that’s optimized to work with the software you use. Ready to evolve your analytics strategy or improve your data quality?

Deep Learning 59

Deep Learning Strategy Risk Data Quality 59

CIO Business Intelligence

MAY 16, 2022

The data gathered from cameras and sensors as part of a computer vision system, along with machine learning, make it easier to find missing persons and to identify people who are not allowed to be in a venue. Ready to evolve your analytics strategy or improve your data quality? Just starting out with analytics?

Recreation/Entertainment 104

Recreation/Entertainment Broadcasting Cost-Benefit Advertising 104

CIO Business Intelligence

APRIL 4, 2022

Their legacy databases could not analyze the volumes of data fast enough to block fraudulent activities without negatively impacting their responsiveness. . Deep link analytics combined with real-time analysis and machine learning provide a robust platform for detecting and preventing fraud.

Technology 52

Technology Deep Learning Cost-Benefit Data Quality 52

Cloudera

NOVEMBER 15, 2021

Here we briefly describe some of the challenges that data poses to AI. Data annotation. Abundance of data has been one of the main facilitators of the AI boom of the last decade. Deep Learning, a subset of AI algorithms, typically requires large amounts of human annotated data to be useful. Data curation.

Unstructured Data 87

Unstructured Data Recreation/Entertainment Structured Data Reporting 87

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

DataKitchen

APRIL 13, 2021

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. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Testing 307

Testing Machine Learning Consulting Data Quality 307

Smarten

APRIL 19, 2024

Benefits include customized and optimized models, data, parameters and tuning. Price and bundling optimization, demand pricing, and other variables will be included to provide the best options, prices and responses and to personalize the approach and present results for data analysis and for end users.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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 194

Modeling Machine Learning Testing Metrics 194

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

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

DataRobot Blog

DECEMBER 20, 2022

In this context, an augmented intelligence approach around the data will be increasingly more critical for asset managers, investors, and real estate developers to ensure a better understanding of the real estate assets and take better decisions aimed at optimizing both the Net Asset Value and the Net Operating Income.

Management 59

Management Machine Learning Optimization Modeling 59

CIO Business Intelligence

APRIL 12, 2022

Computer vision is helping to reshape the transportation industry at every level from streamlining the passenger experience to preemptive fleet maintenance to fuel optimization. In addition, safety is a key requirement across rail, water, air, and roadways, often requiring split-second decisions that can often be enhanced by machine learning.

IT 72

IT Cost-Benefit IoT Machine Learning 72

CIO Business Intelligence

JANUARY 31, 2023

These changes bring new challenges, but advancements in IT automation, artificial intelligence (AI) and machine learning (ML), and edge-computing capabilities will play a key role. Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI).

Deep Learning 111

Deep Learning Cost-Benefit Data-driven Manufacturing 111

CIO Business Intelligence

MARCH 22, 2022

Business units can bring in their own data, and access the superset of data aggregated from all the other different sources. . Becoming data-driven and automating with AI and machine learning (ML) algorithms can seem overwhelming. Ready to evolve your analytics strategy or improve your data quality?

Analytics 115

Analytics Key Performance Indicator Data Warehouse Data-driven 115

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

CIO Business Intelligence

JANUARY 20, 2023

Blocking the move to a more AI-centric infrastructure, the survey noted, are concerns about cost and strategy plus overly complex existing data environments and infrastructure. Though experts agree on the difficulty of deploying new platforms across an enterprise, there are options for optimizing the value of AI and analytics projects. [2]

Analytics 137

Analytics Key Performance Indicator Unstructured Data IoT 137