Category: Data Engineer
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One simple chart: Demand for Machine Learning Engineers
I started hearing the job role of “machine learning engineer” a few years ago. In mid 2017 we wrote a post describing the role that we were observing in a few companies mainly in the technology…
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What Is a Lakehouse?
by Ben Lorica, Michael Armbrust, Ali Ghodsi, Reynold Xin and Matei Zaharia. [This post originally appeared on the Databricks blog.] Over the past few years at Databricks, we’ve seen a new data management paradigm that emerged…
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Machine learning for operational analytics and business intelligence
The O’Reilly Data Show Podcast: Peter Bailis on data management, ML benchmarks, and building next-gen tools for analysts. In this episode of the Data Show, I speak with Peter Bailis, founder and CEO of Sisu, a…
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One simple chart: Who is interested in Apache Pulsar?
Multi-layer architecture, scalability, multitenancy, and durability are just some of the reasons companies have been using Pulsar. By Ben Lorica and Jesse Anderson. With companies producing data from an increasing number of systems and devices, messaging and…
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Labeling, transforming, and structuring training data sets for machine learning
The O’Reilly Data Show Podcast: Alex Ratner on how to build and manage training data with Snorkel. In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project;…
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You’ll want Nexar’s newly released Live Map for your city
Extracting and exposing valuable insights to enable smart cities and many other applications. I recently had the privilege of getting a preview of Nexar’s Live Map, from my friend, Nexar’s CTO and co-founder Bruno Fernandez-Ruiz. Nexar…
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Managing machine learning in the enterprise: Lessons from banking and health care
A look at how guidelines from regulated industries can help shape your ML strategy. By Ben Lorica, Harish Doddi, David Talby. As companies use machine learning (ML) and AI technologies across a broader suite of products…
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Tools for machine learning development
The O’Reilly Data Show: Ben Lorica chats with Jeff Meyerson of Software Engineering Daily about data engineering, data architecture and infrastructure, and machine learning. By Jenn Webb. In this week’s episode of the Data Show, we’re…
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Enabling end-to-end machine learning pipelines in real-world applications
The O’Reilly Data Show Podcast: Nick Pentreath on overcoming challenges in productionizing machine learning models. In this episode of the Data Show, I spoke with Nick Pentreath, principal engineer at IBM. Pentreath was an early and…
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What are model governance and model operations?
A look at the landscape of tools for building and deploying robust, production-ready machine learning models. By Ben Lorica, Harish Doddi, David Talby. Our surveys over the past couple of years have shown growing interest in…