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?

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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. An example of the new reality comes from Salesforce.

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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

O'Reilly Media - Data

MAY 24, 2018

In particular, we examined the evolution of key topics covered in this podcast: data science and machine learning, data engineering and architecture, AI, and the impact of each of these areas on businesses and companies. Continue reading The evolution of data science, data engineering, and AI.

Data Engineering 179

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CIO

DECEMBER 12, 2024

The legacy problem Legacy systems that collect and store limited data are part of the problem, says Rupert Brown, CTO and founder of Evidology Systems, a compliance solutions provider. Data quality is a problem that is going to limit the usefulness of AI technologies for the foreseeable future, Brown adds.

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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. “It also enables companies to generate more accurate predictions.

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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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CIO

SEPTEMBER 12, 2022

Data engine on wheels’. To mine more data out of a dated infrastructure, Fazal first had to modernize NJ Transit’s stack from the ground up to be geared for business benefit. Data from that surfeit of applications was distributed in multiple repositories, mostly traditional databases. That’s how we measure success.”.

Data Engineering 174

Data Engineering Engineering Data Transportation 174

CIO

DECEMBER 4, 2024

The company pushes all its employees, even down to the most junior levels, to read up on emerging trends and experiment. And if they find things that are valuable, they should share them with the rest of the company. Organizations like Pariveda and Neudesic understand the importance of encouraging continuous learning.

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TechCrunch

JUNE 2, 2021

Iterative , an open-source startup that is building an enterprise AI platform to help companies operationalize their models, today announced that it has raised a $20 million Series A round led by 468 Capital and Mesosphere co-founder Florian Leibert. He noted that the industry has changed quite a bit since then. Image Credits: Iterative.

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CIO

DECEMBER 10, 2024

As many companies that have already adopted off-the-shelf GenAI models have found, getting these generic LLMs to work for highly specialized workflows requires a great deal of customization and integration of company-specific data. In 2023 alone, Gartner found companies that deployed AI spent between $300,000 and $2.9

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Artificial Inteligence Off-The-Shelf Insurance Analytics 174

Xebia

MARCH 30, 2025

“The fine art of data engineering lies in maintaining the balance between data availability and system performance.” ” Ted Malaska At Melexis, a global leader in advanced semiconductor solutions, the fusion of artificial intelligence (AI) and machine learning (ML) is driving a manufacturing revolution.

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Testing Artificial Inteligence Comparison Software Review 130

TechCrunch

OCTOBER 19, 2021

Founder Tommy Dang started the company at the end of 2020 after working together to build internal low-code tools at Airbnb. 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.

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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. Data Platforms.

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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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The Crazy Programmer

NOVEMBER 6, 2021

So you can also acquire such skills and get placed in renowned companies. Currently, the demand for data scientists has increased 344% compared to 2013. hence, if you want to interpret and analyze big data using a fundamental understanding of machine learning and data structure. Big Data Engineer.

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CIO

DECEMBER 20, 2024

Data architecture principles According to David Mariani , founder and CTO of semantic layer platform AtScale, six principles form the foundation of modern data architecture: View data as a shared asset. Provide user interfaces for consuming data. AI and machine learning models.

Architecture 183

Architecture Data Fractional CTO Technical Review 183

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.

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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. The role of a machine learning engineer in the data science team.

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TechCrunch

MAY 10, 2022

Data science teams are stymied by disorganization at their companies, impacting efforts to deploy timely AI and analytics projects. In a recent survey of “data executives” at U.S.-based ” The market for synthetic data is bigger than you think.

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Artificial Inteligence Machine Learning Off-The-Shelf Training 235

Xebia

MARCH 13, 2025

The implementation was a over-engineered custom Feast implementation using unsupported backend data stores. The engineer that implemented it had left the company by the time I joined. This becomes more important when a company scales and runs more machine learning models in production.

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Cloudera

NOVEMBER 19, 2021

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. This is why companies spend so much time and energy figuring out how to address this so-called “last mile” problem.

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Machine Learning Artificial Inteligence eBook Data Engineering 122

CIO

APRIL 2, 2025

Artificial intelligence (AI) has long since arrived in companies. Whether in process automation, data analysis or the development of new services AI holds enormous potential. But how does a company find out which AI applications really fit its own goals? Model and data analysis. Strategy development and consulting.

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Cloudera

APRIL 9, 2021

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection.

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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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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.

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Data Engineering Engineering Data Artificial Inteligence 106

TechCrunch

APRIL 12, 2022

While companies find AI’s predictive power alluring, particularly on the data analytics side of the organization, achieving meaningful results with AI often proves to be a challenge. . “We [founded Union] because we believe that machine learning and data workflows are fundamentally different from software deployments.

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TechCrunch

DECEMBER 10, 2020

As remote work continues to solidify its place as a critical aspect of how businesses exist these days, a startup that has built a platform to help companies source and bring on one specific category of remote employees — engineers — is taking on some more funding to meet demand. Turing is not disclosing its valuation.

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TechCrunch

MAY 3, 2022

“There were no purpose-built machine learning data tools in the market, so [we] started Galileo to build the machine learning data tooling stack, beginning with a [specialization in] unstructured data,” Chatterji told TechCrunch via email. ” To date, Galileo has raised $5.1

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AWS Machine Learning - AI

NOVEMBER 15, 2024

Principal is a global financial company with nearly 20,000 employees passionate about improving the wealth and well-being of people and businesses. It empowers employees to be more creative, data-driven, efficient, prepared, and productive. The initial proof of concept was deployed in a preproduction environment within 3 months.

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TechCrunch

OCTOBER 14, 2020

The Exchange regularly covers companies as they approach and crest the $100 million revenue mark. Today we’re digging into a company that is a little bit bigger than that. Namely Databricks , a data analytics company that was most recently valued at around $6.2 He’s also a co-founder. What does Databricks do?

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CIO

NOVEMBER 27, 2024

For AI, there’s no universal standard for when data is ‘clean enough.’ Take the data quality of employee records you might use for both salary processing and an internal mailing campaign with company news. You could, in theory, be cleaning forever, depending on the size of your data,” he says.

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Data Enterprise Weak Development Team Software Review 211

CIO

JULY 28, 2022

For the past few years, IT leaders at a US financial services company have been struggling to hire data scientists to harness the increasing flood of incoming data that, if used properly, could improve customer experience and drive new products. These people are making up a data science support system.

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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.

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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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TechCrunch

AUGUST 17, 2021

Less than a year after its $3 million seed round, San Francisco- and Africa-based fintech Pngme has snapped up another $15 million for its financial data infrastructure play. The company is also describing itself as a machine learning-as-a-service platform. But why has it become important for the company all of a sudden?

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TechCrunch

FEBRUARY 2, 2022

RudderStack , a platform that focuses on helping businesses build their customer data platforms to improve their analytics and marketing efforts, today announced that it has raised a $56 million Series B round led by Insight Partners, with previous investors Kleiner Perkins and S28 Capital also participating. Image Credits: RudderStack.

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