October 2017
Intermediate to advanced
91 pages
1h 48m
English
book
Data Warehousing in the Age of Artificial Intelligence
by Gary Orenstein, Conor Doherty, Mike Boyarski, Eric Boutin
Content preview from Data Warehousing in the Age of Artificial Intelligence
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Chapter 2. Framing Data Processing with ML and AI
A mix of applications and data science combined with a broad data corpus delivers powerful capabilities for a business to act on data. With a wide-open field of machine learning (ML) and artificial intelligence (AI), it helps to set the stage with a common taxonomy.
In this chapter, we explore foundational ML and AI concepts that are used throughout this book.
Foundations of ML and AI for Data Warehousing
The world has become enchanted with the resurgence in AI and ML to solve business problems. And all of these processes need places to store and process data.
The ML and AI renaissance is largely credited to a confluence of forces:
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The availability of new distributed processing techniques to crunch and store data, including Hadoop and Spark, as well as new distributed, relational datastores
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The proliferation of compute and storage resources, such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others
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The awareness and sharing of the latest algorithms, including everything from ML frameworks such as TensorFlow to vectorized queries
AI
For our purpose, we consider AI as a broad endeavor to mimic rational thought. Humans are masters of pattern recognition, possessing the ability to apply historical events with current situational awareness to make rapid, informed decisions. The same outcomes of data-driven decisions combined with live inputs are part of the push in modern AI.
ML
ML follows ...
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