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How companies around the world apply machine learning

O'Reilly Media - Data

The growing role of data and machine learning cuts across domains and industries. Companies continue to use data to improve decision-making (business intelligence and analytics) and for automation (machine learning and AI). Here are some examples: Data Case Studies (12 presentations). Privacy and security. Telecom sessions.

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AI meets operations

O'Reilly Media - Ideas

First, the behavior of an AI application depends on a model , which is built from source code and training data. A model isn’t source code, and it isn’t data; it’s an artifact built from the two. You need a repository for models and for the training data. Second, the behavior of AI systems changes over time.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

This structure worked well for production training and deployment of many models but left a lot to be desired in terms of overhead, flexibility, and ease of use, especially during early prototyping and experimentation [where Notebooks and Python shine]. 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

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. My goal was to remind the data community about the many interesting opportunities and challenges in data itself. But if data is precious, how do we go about estimating its value?

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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. In many instances, “lack of data” is literally the state of affairs: companies have yet to collect and store the data needed to train the ML models they desire.

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9 Tech Conferences Not to Be Missed in October

Apiumhub

In this article, we´ll be your guide to the must-attend tech conferences set to unfold in October. From software architecture to artificial intelligence and machine learning, ​​these conferences offer unparalleled insights, networking opportunities, and a glimpse into the future of technology.

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Data Innovation Summit with Gema Parreño – lead data scientist at Apiumhub

Apiumhub

Data Innovation Summit topics. Same as last year, the event offers six workshops (crash-course) themes, each dedicated to a unique domain area: Data-driven Strategy, Analytics & Visualisation, Machine Learning, IoT Analytics & Data Management, Data Management and Data Engineering.