Whether you’re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there’s a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.
Let’s start by just enumerating the broad categories of AI/ML business models. Most of us agree there are at least these four.
AI/ML Infrastructure
AI-First Full Stack Vertical Platforms
Applied AI – Optimization of the Current Business Model
Platformication – A Radical End Point for AI/ML Strategy
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Tag Archives: Architecture
How to Configure the Number of Layers and Nodes in a Neural Network
Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the number of layers and the number of nodes in each hidden layer.
You must specify values for these parameters when configuring your network.
The most reliable way to configure these hyperparameters for your specific predictive modeling problem is via systematic experimentation with a robust test harness. Read More
Explaining AI from a Life cycle of data
Explaining AI from the perspective of the life-cycle of Data is useful because more people are used to data (than to code). I welcome comments on this approach. Read More
Google AI Chief Jeff Dean’s ML System Architecture Blueprint
ML has revolutionized vision, speech and language understanding and is being applied in many other fields. That’s an extraordinary achievement in the tech’s short history and even more impressive considering there is still no dedicated ML hardware. Read More
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