Why a data scientist is not a data engineer
Or, why science and engineering are still different disciplines.
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Insights, practical guidance, and announcements from O'Reilly
Specialized tools for machine learning development and model governance are becoming essential
Why companies are turning to specialized machine learning tools like MLflow.
Sustaining machine learning in the enterprise
Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning.
Velocity 2019 will focus on the rise of cloud native infrastructure
Organizations that want all of the speed, agility, and savings the cloud provides are embracing a cloud native approach.
Proposals for model vulnerability and security
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors.
International Women’s Day at O’Reilly
At O’Reilly, we seek to foster a culture that creates opportunity, rewards and recognizes accomplishments, and treats everyone with respect.
Lessons learned building natural language processing systems in health care
NLP systems in health care are hard—they require broad general and medical knowledge, must handle a large variety of inputs, and need to understand context.
You created a machine learning application. Now make sure it’s secure.
The software industry has demonstrated, all too clearly, what happens when you don’t pay attention to security.
3 reasons to add deep learning to your time series toolkit
The most promising area in the application of deep learning methods to time series forecasting is in the use of CNNs, LSTMs, and hybrid models.
Prepare for the rise of farm bots
To meet the challenge of producing more food with less everything, farm bots are going to be an essential part of the mix.
Core technologies and tools for AI, big data, and cloud computing
Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning.
The trinity of errors in financial models: An introductory analysis using TensorFlow Probability
An exploration of three types of errors inherent in all financial models.