O'Reilly Media - Data

JULY 18, 2018

It’s no secret that companies place a lot of value on data and the data pipelines that produce key features. In the early phases of adopting machine learning (ML), companies focus on making sure they have sufficient amount of labeled (training) data for the applications they want to tackle.

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Artificial Inteligence Machine Learning Data Marketing 196

Xebia

FEBRUARY 28, 2025

Universities have been pumping out Data Science grades in rapid pace and the Open Source community made ML technology easy to use and widely available. Both the tech and the skills are there: Machine Learning technology is by now easy to use and widely available. Big part of the reason lies in collaboration between teams.

Technical Review 182

Technical Review Weak Development Team Artificial Inteligence Machine Learning 182

TechCrunch

NOVEMBER 18, 2021

DevOps fueled this shift to the cloud, as it gave decision-makers a sense of control over business-critical applications hosted outside their own data centers. Data engineers play with tools like ETL/ELT, data warehouses and data lakes, and are well versed in handling static and streaming data sets.

Enterprise 344

Enterprise Artificial Inteligence Data Engineering Data Center 344

CIO

APRIL 23, 2025

Delta Lake: Fueling insurance AI Centralizing data and creating a Delta Lakehouse architecture significantly enhances AI model training and performance, yielding more accurate insights and predictive capabilities. Modern AI models, particularly large language models, frequently require real-time data processing capabilities.

Insurance 164

Insurance Artificial Inteligence Data Architecture 164

CIO

APRIL 1, 2025

Job titles like data engineer, machine learning engineer, and AI product manager have supplanted traditional software developers near the top of the heap as companies rush to adopt AI and cybersecurity professionals remain in high demand. The job will evolve as most jobs have evolved.

Marketing 152

Marketing Software Development Software Development 152

CIO

DECEMBER 11, 2024

It was not alive because the business knowledge required to turn data into value was confined to individuals minds, Excel sheets or lost in analog signals. We are now deciphering rules from patterns in data, embedding business knowledge into ML models, and soon, AI agents will leverage this data to make decisions on behalf of companies.

Data 167

Data Technical Review Software Review Weak Development Team 167

Altexsoft

JULY 23, 2020

When speaking of machine learning, we typically discuss data preparation or model building. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. MLOps lies at the confluence of ML, data engineering, and DevOps. More time for development of new models.

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Artificial Inteligence Machine Learning DevOps Tools 145

Xebia

MARCH 13, 2025

Unfortunately, the blog post only focuses on train-serve skew. Feature stores solve more than just train-serve skew. This becomes more important when a company scales and runs more machine learning models in production. In a naive setup features are (re-)computed each time you train a new model.

Training 130

Training Artificial Inteligence Machine Learning Data 130

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. RAPIDS on the Cloudera Data Platform comes pre-configured with all the necessary libraries and dependencies to bring the power of RAPIDS to your projects. to_arrow().to_pylist().

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CIO

DECEMBER 10, 2024

Our LLM was built on EXLs 25 years of experience in the insurance industry and was trained on more than a decade of proprietary claims-related data. Our EXL Insurance LLM is consistently achieving a 30% improvement in accuracy on insurance-related tasks over the top pre-trained models, such as GPT4, Claude, and Gemini.

Artificial Inteligence 174

Artificial Inteligence Off-The-Shelf Insurance Analytics 174

TechCrunch

NOVEMBER 28, 2022

It’s only as good as the models and data used to train it, so there is a need for sourcing and ingesting ever-larger data troves. But annotating and manipulating that training data takes a lot of time and money, slowing down the work or overall effectiveness, and maybe both.

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Training Data Technical Review Artificial Inteligence 240

TechCrunch

JULY 12, 2022

Machine learning can provide companies with a competitive advantage by using the data they’re collecting — for example, purchasing patterns — to generate predictions that power revenue-generating products (e.g. At a high level, Tecton automates the process of building features using real-time data sources.

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Artificial Inteligence Machine Learning Marketing Data Engineering 249

CIO

SEPTEMBER 12, 2022

The chief information and digital officer for the transportation agency moved the stack in his data centers to a best-of-breed multicloud platform approach and has been on a mission to squeeze as much data out of that platform as possible to create the best possible business outcomes. Data engine on wheels’.

Data Engineering 174

Data Engineering Engineering Data Transportation 174

CIO

NOVEMBER 27, 2024

For AI, there’s no universal standard for when data is ‘clean enough.’ But that’s exactly the kind of data you want to include when training an AI to give photography tips. Data quality is extremely important, but it leads to very sequential thinking that can lead you astray,” Carlsson says.

Data 211

Data Enterprise Weak Development Team Software Review 211

Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

Artificial Inteligence 107

Artificial Inteligence Machine Learning Scalability Data Engineering 107

CIO

APRIL 2, 2025

The spectrum is broad, ranging from process automation using machine learning models to setting up chatbots and performing complex analyses using deep learning methods. They examine existing data sources and select, train and evaluate suitable AI models and algorithms. Implementation and integration.

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Artificial Inteligence Technical Advisors Artificial Intelligence Automotive 120

CIO

DECEMBER 4, 2024

We’ve had folks working with machine learning and AI algorithms for decades,” says Sam Gobrail, the company’s senior director for product and technology. But for practical learning of the same technologies, we rely on the internal learning academy we’ve established.”

Recruiting 202

Recruiting Artificial Inteligence Programming Technology 202

Cloudera

NOVEMBER 17, 2021

You know the one, the mathematician / statistician / computer scientist / data engineer / industry expert. Some companies are starting to segregate the responsibilities of the unicorn data scientist into multiple roles (data engineer, ML engineer, ML architect, visualization developer, etc.),

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Artificial Inteligence Machine Learning Hotels Data Engineering 124

O'Reilly Media - Ideas

NOVEMBER 13, 2018

As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. We recently conducted a survey which garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. Privacy and security.

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TechCrunch

MAY 10, 2022

“The major challenges we see today in the industry are that machine learning projects tend to have elongated time-to-value and very low access across an organization. “Given these challenges, organizations today need to choose between two flawed approaches when it comes to developing machine learning. .

Artificial Inteligence 235

Artificial Inteligence Machine Learning Off-The-Shelf Training 235

CIO

APRIL 30, 2025

The complexity could be customer distress, a storm, an airport slowdown, or any other situation with a lot of data and urgency to empower employees and customers with relevant, in-the-moment information. Much of this work has been in organizing our data and building a secure platform for machine learning and other AI modeling.

Airlines 103

Airlines Strategy ChatGPT Software Review 103

Altexsoft

JUNE 29, 2021

In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. Google, in turn, uses the Google Neural Machine Translation (GNMT) system, powered by ML, reducing error rates by up to 60 percent. The role of a machine learning engineer in the data science team.

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Artificial Inteligence Machine Learning Engineering Data Engineering 105

CIO

JUNE 11, 2024

The company is offering eight free courses , leading up to this certification, including Fundamentals of Machine Learning and Artificial Intelligence, Exploring Artificial Intelligence Use Cases and Application, and Essentials of Prompt Engineering. AWS has been adding new certifications to its offering.

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Artificial Inteligence AWS Artificial Intelligence Machine Learning 173

Cloudera

APRIL 9, 2021

The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Here are the key stages: .

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CIO

JUNE 10, 2024

Now, they’re racing to train workers fast enough to keep up with business demand. Moreover, many need deeper AI-related skills, too, such as for building machine learning models to serve niche business requirements. Everyone is learning,” Daly says. Case in point: Training data workers on AI bias.

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Artificial Inteligence Strategy Machine Learning Training 204

CIO

JULY 28, 2022

Synchrony isn’t the only company dealing with a dearth of data scientists to perform increasingly critical work in the enterprise. Companies are struggling to hire true data scientists — the ones trained and experienced enough to work on complex and difficult problems that might have never been solved before. Getting creative.

Data 215

Data Artificial Inteligence Machine Learning Fractional CTO 215

Xebia

DECEMBER 23, 2024

Diverse User Roles and Decentralized Teams: Amplifying the Cost Challenge One of the greatest strengths of modern data platforms is their ability to support a wide variety of usersdata engineers, analysts, scientists, and even business stakeholders. Upskilling Teams: Go beyond awareness by providing targeted training and resources.

Data 130

Data Storage Culture Resources 130

Xebia

DECEMBER 23, 2024

Diverse User Roles and Decentralized Teams: Amplifying the Cost Challenge One of the greatest strengths of modern data platforms is their ability to support a wide variety of usersdata engineers, analysts, scientists, and even business stakeholders. Upskilling Teams: Go beyond awareness by providing targeted training and resources.

Data 130

Data Storage Culture Resources 130

O'Reilly Media - Ideas

APRIL 2, 2019

Why companies are turning to specialized machine learning tools like MLflow. A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. The upcoming 0.9.0

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Artificial Inteligence Machine Learning Government Tools 108

Altexsoft

OCTOBER 30, 2021

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering.

Data Engineering 98

Data Engineering Engineering Data Artificial Inteligence 98

CIO

APRIL 22, 2022

What is data science? Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For further information about data scientist skills, see “ What is a data scientist?

Data 210

Data Artificial Inteligence Machine Learning Analytics 210

d2iq

FEBRUARY 19, 2021

Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.

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Artificial Inteligence Machine Learning Technical Review Software Review 103

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.

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AWS Machine Learning - AI

NOVEMBER 15, 2024

The flexible, scalable nature of AWS services makes it straightforward to continually refine the platform through improvements to the machine learning models and addition of new features. The first round of testers needed more training on fine-tuning the prompts to improve returned results.

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Cloudera

JANUARY 20, 2021

to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. Machine learning is now being used to solve many real-time problems. One big use case is with sensor data. Training Data in HBase and HDFS.

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CIO

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

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CIO

SEPTEMBER 29, 2023

Most relevant roles for making use of NLP include data scientist , machine learning engineer, software engineer, data analyst , and software developer. PyTorch is known in the deep learning and AI community as being a flexible, fast, and easy-to-use framework for building deep neural networks.

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