Remove 2018 Remove Data Engineering Remove Machine Learning
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How companies around the world apply machine learning

O'Reilly Media - Data

Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.

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Are you ready for MLOps? 🫵

Xebia

Universities have been pumping out Data Science grades in rapid pace and the Open Source community made ML technology easy to use and widely available. Both the tech and the skills are there: Machine Learning technology is by now easy to use and widely available. Big part of the reason lies in collaboration between teams.

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Highlights from JupyterCon in New York 2018

O'Reilly Media - Data

Watch keynotes covering Jupyter's role in business, data science, higher education, open source, journalism, and other domains, from JupyterCon in New York 2018. Jupyter trends in 2018. Paco Nathan shares a few unexpected things that emerged in Jupyter in 2018. Watch " Jupyter trends in 2018.". Democratizing data.

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AI Chihuahua! Part I: Why Machine Learning is Dogged by Failure and Delays

d2iq

Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.

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Immunai announces a $215M Series B as its ‘immune cell atlas’ matures

TechCrunch

The company that set out to create an atlas of the human immune system in 2018 had raised about $80 million by February 2021. It combines genetic information, along with other data like epigenetic changes or proteomics (the study of proteins), to map out how the immune system functions. Our approach is the opposite.

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5 key areas for tech leaders to watch in 2020

O'Reilly Media - Ideas

This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%.

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How companies are building sustainable AI and ML initiatives

O'Reilly Media - Ideas

In 2017, we published “ How Companies Are Putting AI to Work Through Deep Learning ,” a report based on a survey we ran aiming to help leaders better understand how organizations are applying AI through deep learning. We found companies were planning to use deep learning over the next 12-18 months.