Artificial Intelligence Decision Tree

Posted on February 15, 2019 by Ivan Kosyakov

Let’s discuss decision points for selecting right components for Artificial Intelligence (AI) solutions. This is also an update to Machine Learning Decision Tree (v1). Keep in mind here that AI is a broader term compared to Machine Learning.

Artificial Intelligence functionality in the decision tree is divided between following groups:

  • AI in Business Applications: Dynamics 365 AI (AI for Customer Service, Market Insights, Sales), Microsoft 365 AI (Office 365 Workplace Analytics, ML in Power BI, O365 Search).
  • Knowledge Mining: O365 Search, Azure Search.
  • AI Apps and Agents: Azure Bot Service (Framework), Cognitive Services (Vision APIs, Speech APIs, Language APIs, Search APIs, Custom APIs).
  • Machine Learning Tools: Azure Notebooks, Jupiter Notebooks, Code, PyCharm, Visual Studio, Azure ML Studio.
  • Cloud-based Machine Learning: Azure ML Service, Azure ML Studio, Data Preparation (Azure Data Factory, Azure Databricks), Model Training/Testing (Azure Databricks, Azure HDInsight, Data Science VM), Container Registry, Model Deployment (Azure Container Instances, Azure Kubernetes Service, Azure Batch, Azure IoT Edge), Azure Infrastructure (CPUs, GPUs, FPGAs).
  • On-premises Machine Learning: Edge Devices, Cognitive Services Containers, SQL Server ML Services, On-prem Hadoop.
  • Machine Learning Frameworks: Deep Learning (ONNX, PyTorch, TensorFlow), General ML (Spark MLllib, SparkR, SparklyR, MML Spark).

The text description of the decision points will be available in a few days…

Reference materials

Other Decision Trees/Maps

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