Conference Data Engineering Machine Learning 
O'Reilly Media - Data
APRIL 3, 2018
Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.
O'Reilly Media - Data
JULY 18, 2018
While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. Economic value of data.
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CIO
MARCH 13, 2025
Gen AI-related job listings were particularly common in roles such as data scientists and data engineers, and in software development. Our VP of engineering said, These guys are interested in doing it, theyre already playing around with it, and had already built some stuff with it.'
O'Reilly Media - Ideas
NOVEMBER 13, 2018
In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Developers have taken notice and are beginning to learn about ML.
InfoQ Culture Methods
JULY 28, 2020
To become a machine learning engineer, you have to interview. You have to gain relevant skills from books, courses, conferences, and projects. Include technologies, frameworks, and projects on your CV. In an interview, expect that you will be asked technical questions, insight questions, and programming questions.
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. The new team needs data engineers and scientists, and will look outside the company to hire them.
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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