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Managing risk in machine learning

O'Reilly Media - Ideas

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.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

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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Data collection and data markets in the age of privacy and machine learning

O'Reilly Media - Data

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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Cloudera Supercharges the Enterprise Data Cloud with NVIDIA

Cloudera

Cloudera Data Platform Powered by NVIDIA RAPIDS Software Aims to Dramatically Increase Performance of the Data Lifecycle Across Public and Private Clouds. This exciting initiative is built on our shared vision to make data-driven decision-making a reality for every business. Compared to previous CPU-based architectures, CDP 7.1

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Assessing progress in automation technologies

O'Reilly Media - Ideas

In this post, I share slides and notes from a keynote Roger Chen and I gave at the Artificial Intelligence conference in London in October 2018. To assess the state of adoption of machine learning (ML) and AI, we recently conducted a survey that garnered more than 11,000 respondents. is extremely high.

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Core technologies and tools for AI, big data, and cloud computing

O'Reilly Media - Ideas

Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning. In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Deep Learning.

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DataRobot Flies Higher with Zepl Acquisition, Adding Cloud Native Notebook Solution to AI Platform

DataRobot

At DataRobot, we have always known that data science is a team sport. Data Exploration, Visualization, and First-Class Integration. Zepl brings to the party some new ways for users to depict their data with flexible and attractive charts and graphs, supplementing the best-in-class insights DataRobot has long been known for.

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