O'Reilly on Data
MARCH 20, 2019
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.
O'Reilly on Data
DECEMBER 12, 2019
Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]
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O'Reilly on Data
FEBRUARY 4, 2019
In this post, I’ll describe some of the key areas of interest and concern highlighted by respondents from Europe, while describing how some of these topics will be covered at the upcoming Strata Data conference in London (April 29 - May 2, 2019). Data Platforms. Data Integration and Data Pipelines.
erwin
JANUARY 3, 2019
Experts are predicting a surge in GDPR enforcement in 2019 as regulators begin to crackdown on organizations still lagging behind compliance standards. Big Data Batman (@BigDataBatman) January 29, 2019. For anything data management and data governance related, the erwin Experts should be your first point of call.
O'Reilly on Data
MAY 21, 2019
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. A typical data pipeline for machine learning.
DataKitchen
APRIL 13, 2021
DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Acquired by DataRobot June 2019).
Cloudera
DECEMBER 1, 2023
These government-led efforts have had a profound impact on the development and adoption of AI solutions in the public sector, paving the way for a future where data-driven decision-making and automation are the norm. Launched in 2019, this strategy aims to position the US as a leader in AI research, development, and deployment.
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