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

CIO

MARCH 13, 2025

Hes seeing the need for professionals who can not only navigate the technology itself, but also manage increasing complexities around its surrounding architectures, data sets, infrastructure, applications, and overall security. The speed of the cyber technology revolution is very fast and attackers are always changing behaviors.

Part-Time VPE 205

Part-Time VPE Weak Development Team Fractional VPE Fractional CTO 205

CIO

DECEMBER 12, 2024

The survey points to a fundamental misunderstanding among many business leaders regarding the data work needed to deploy most AI tools, says John Armstrong, CTO of Worldly, a supply chain sustainability data insights platform. Gen AI uses huge amounts of energy compared to some other AI tools, he notes.

Data 201

Data Survey Artificial Inteligence Education 201

O'Reilly Media - Data

JULY 18, 2018

In this short talk, I describe some interesting trends in how data is valued, collected, and shared. Economic value of data. It’s no secret that companies place a lot of value on data and the data pipelines that produce key features. But if data is precious, how do we go about estimating its value?

Artificial Inteligence 196

Artificial Inteligence Machine Learning Data Marketing 196

CIO

APRIL 23, 2025

Modern AI models, particularly large language models, frequently require real-time data processing capabilities. The machine learning models would target and solve for one use case, but Gen AI has the capability to learn and address multiple use cases at scale.

Insurance 164

Insurance Artificial Inteligence Data Architecture 164

CIO

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. The data engineer role.

Data Engineering 197

Data Engineering Analytics Engineering Data 197

CIO

DECEMBER 20, 2024

In addition to using cloud for storage, many modern data architectures make use of cloud computing to analyze and manage data. Modern data architectures use APIs to make it easy to expose and share data. AI and machine learning models. Data integrity. Choose the right tools and technologies.

Architecture 183

Architecture Data Fractional CTO Technical Review 183

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

DECEMBER 4, 2024

Changing demographics, fast-evolving technologies, and the globalization of job opportunities make recruiting and holding onto skilled professionals much more difficult. As technology continues to change more rapidly than ever, CIOs who want to build and maintain a team with the right skills will need to do these four things.

Recruiting 202

Recruiting Artificial Inteligence Programming Technology 202

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.

Marketing 152

Marketing Software Development Software Development 152

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. It is an interdisciplinary approach that aligns technological innovation with business requirements. Model and data analysis.

Artificial Inteligence 120

Artificial Inteligence Technical Advisors Artificial Intelligence Automotive 120

Altexsoft

JULY 23, 2020

When speaking of machine learning, we typically discuss data preparation or model building. Much less often the technology is mentioned in terms of deployment. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. More time for development of new models.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

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.

Artificial Inteligence 249

Artificial Inteligence Machine Learning Marketing Data Engineering 249

The Crazy Programmer

NOVEMBER 6, 2021

IT or Information technology is the industry that has registered continuous growth. The Indian information Technology has attained about $194B in 2021 and has a 7% share in GDP growth. Because startups like Zerodha, Ola, and Rupay to large organizations like Infosys, HCL Technologies Ltd, all will grow at a mass scale.

Artificial Inteligence 290

Artificial Inteligence Blockchain Software Review Artificial Intelligence 290

Dzone - DevOps

DECEMBER 31, 2024

MLOps, or Machine Learning Operations, is a set of practices that combine machine learning (ML), data engineering, and DevOps to streamline and automate the end-to-end ML model lifecycle. MLOps is an essential aspect of the current data science workflows.

Artificial Inteligence 96

Artificial Inteligence Machine Learning Scalability Data Engineering 96

TechCrunch

OCTOBER 19, 2021

While collaborating with product developers, Dang and Wang saw that while product developers wanted to use AI, they didn’t have the right tools in which to do it without relying on data scientists. “We They didn’t work with machine learning extensively, so we decided to build tools for technical non-experts.

Artificial Inteligence 248

Artificial Inteligence Machine Learning Tools Technical Review 248

CIO

DECEMBER 10, 2024

The sheer number of options and configurations, not to mention the costs associated with these underlying technologies, is multiplying so quickly that its creating some very real challenges for businesses that have been investing heavily to incorporate AI-powered capabilities into their workflows.

Artificial Inteligence 174

Artificial Inteligence Off-The-Shelf Insurance Analytics 174

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

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. But no technology can work efficiently without human experts behind it.

Artificial Inteligence 105

Artificial Inteligence Machine Learning Engineering Data Engineering 105

InfoQ Culture Methods

JULY 28, 2020

To become a machine learning engineer, you have to interview. Include technologies, frameworks, and projects on your CV. You have to gain relevant skills from books, courses, conferences, and projects. In an interview, expect that you will be asked technical questions, insight questions, and programming questions.

Artificial Inteligence 119

Artificial Inteligence Machine Learning Engineering How To 119

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

Altexsoft

JUNE 25, 2019

Being at the top of data science capabilities, machine learning and artificial intelligence are buzzing technologies many organizations are eager to adopt. If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Cloudera

APRIL 13, 2021

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. We recently published a Cloudera Special Edition of Production Machine Learning For Dummies eBook. Let your teams experiment rapidly, fail early and often, continuously learn, and try new things.

Artificial Inteligence 98

Artificial Inteligence Machine Learning Enterprise eBook 98

Xebia

MARCH 13, 2025

This blog focuses on the principles of technology and the most important problems a feature store solves. This becomes more important when a company scales and runs more machine learning models in production. Please have a look at this blog post on machine learning serving architectures if you do not know the difference.

Training 130

Training Artificial Inteligence Machine Learning Data 130

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.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

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

Cloudera

FEBRUARY 14, 2022

When we introduced Cloudera Data Engineering (CDE) in the Public Cloud in 2020 it was a culmination of many years of working alongside companies as they deployed Apache Spark based ETL workloads at scale. We are paving the path for our enterprise customers that are adapting to the critical shifts in technology and expectations.

Data Engineering 113

Data Engineering Engineering Data Storage 113

O'Reilly Media - Ideas

APRIL 9, 2019

A few months ago, I wrote about the differences between data engineers and data scientists. An interesting thing happened: the data scientists started pushing back, arguing that they are, in fact, as skilled as data engineers at data engineering. I agree; learn as much as you can.

Data Engineering 112

Data Engineering Engineering Data Technical Review 112

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. There is a commitment to scale and accelerate development of generative AI technology to meet the growing needs of the enterprise.

Generative AI 113

Generative AI AWS Groups Artificial Inteligence 113

CIO

JULY 28, 2022

They’ve also created a relationship with universities, setting up a pipeline of emerging technology-focused interns, who work at the company, gain experience in data science, and then can potentially be hired after they graduate. . And machine learning engineers are being hired to design and build automated predictive models.

Data 215

Data Artificial Inteligence Machine Learning Fractional CTO 215

CIO

JUNE 10, 2024

Moreover, many need deeper AI-related skills, too, such as for building machine learning models to serve niche business requirements. He wants data scientists who can build, train, and validate models for use cases, and who can perform exploratory analysis and hypothesis testing. Here’s how IT leaders are coping.

Artificial Inteligence 204

Artificial Inteligence Strategy Machine Learning Training 204

TechCrunch

NOVEMBER 14, 2022

The complexity of streaming data technologies – not just streaming video but any kind of streaming data – has created a headache around dealing with that high speed data processing. Accordingly, companies like Spark, Flink have spring up to address this ksqlDB. It’s now raised a £11m / $12.9m

Data 208

Data B2B Artificial Inteligence Machine Learning 208

CIO

JUNE 7, 2022

But the success of their AI initiatives depends on more than just data and technology — it’s also about having the right people on board. An effective enterprise AI team is a diverse group that encompasses far more than a handful of data scientists and engineers. Data scientists are the core of any AI team.

Artificial Inteligence 218

Artificial Inteligence Technical Review Fractional CTO Data Engineering 218

CIO

APRIL 12, 2023

After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current data architecture and technology stack. It isn’t easy.

Artificial Inteligence 218

Artificial Inteligence Artificial Intelligence Machine Learning Agile 218

CIO

SEPTEMBER 29, 2023

If any technology has captured the collective imagination in 2023, it’s generative AI — and businesses are beginning to ramp up hiring for what in some cases are very nascent gen AI skills, turning at times to contract workers to fill gaps, pursue pilots, and round out in-house AI project teams.

Generative AI 214

Generative AI Artificial Inteligence Machine Learning ChatGPT 214

CIO

NOVEMBER 27, 2024

For AI, there’s no universal standard for when data is ‘clean enough.’ A golden dataset of questions paired with a gold standard response can help you quickly benchmark new models as the technology improves. In the generative AI world, the notion of accuracy is much more nebulous.”

Data 211

Data Enterprise Weak Development Team Software Review 211

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. Given the current shortage of data science talent, many organizations are building out programs to develop internal data science talent.

Data 210

Data Artificial Inteligence Machine Learning Analytics 210