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How companies are building sustainable AI and ML initiatives

O'Reilly on Data

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.

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

O'Reilly on Data

Recent improvements in tools and technologies has meant that techniques like deep learning are now being used to solve common problems, including forecasting, text mining and language understanding, and personalization. AI and machine learning in the enterprise. Deep Learning. Foundational data technologies.

Big Data 209
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Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

Here are some typical ways organizations begin using machine learning: Build upon existing analytics use cases: e.g., one can use existing data sources for business intelligence and analytics, and use them in an ML application. Use ML to unlock new data types—e.g., A typical data pipeline for machine learning.

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Your 5-Step Journey from Analytics to AI

CIO Business Intelligence

Then virtualize your data to allow business users to conduct aggregated searches and analyses using the business intelligence or data analytics tools of their choice. . Set up unified data governance rules and processes. Ready to evolve your analytics strategy or improve your data quality?

Analytics 112
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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Paco Nathan ‘s latest column dives into data governance. This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form.

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How to choose the best AI platform

IBM Big Data Hub

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.