Remove 2019 Remove Deep Learning Remove Experimentation
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6 trends framing the state of AI and ML

O'Reilly on Data

Our analysis of ML- and AI-related data from the O’Reilly online learning platform indicates: Unsupervised learning surged in 2019, with usage up by 172%. Deep learning cooled slightly in 2019, slipping 10% relative to 2018, but deep learning still accounted for 22% of all AI/ML usage.

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AI adoption in the enterprise 2020

O'Reilly on Data

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. But what kind?

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How to apply machine learning and deep learning methods to audio analysis

KDnuggets

Find out how data scientists and AI practitioners can use a machine learning experimentation platform like Comet.ml to apply machine learning and deep learning to methods in the domain of audio analysis.

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ChatGPT, the rise of generative AI

CIO Business Intelligence

A transformer is a type of AI deep learning model that was first introduced by Google in a research paper in 2017. ChatGPT was trained with 175 billion parameters; for comparison, GPT-2 was 1.5B (2019), Google’s LaMBDA was 137B (2021), and Google’s BERT was 0.3B (2018). What is ChatGPT? ChatGPT is a product of OpenAI.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

It is also important to have a strong test and learn culture to encourage rapid experimentation. A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI.

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The DataOps Vendor Landscape, 2021

DataKitchen

ParallelM — Moves machine learning into production, automates orchestration, and manages the ML pipeline. Acquired by DataRobot June 2019). Metis Machine — Enterprise-scale Machine Learning and Deep Learning deployment and automation platform for rapid deployment of models into existing infrastructure and applications.

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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Managing Machine Learning Projects” (AWS). People + AI Guidebook” (Google).