3 - Machine Learning

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3 - Machine Learning da Mind Map: 3 - Machine Learning

1. Data Preparation & Training

1.1. Cloud Strategy

1.1.1. PaaS Strategy

1.1.1.1. Development

1.1.1.1.1. Exploration (Undefined Tools, languages and Libraries)

1.1.1.1.2. Defined Strategy (Defined language and libraries)

1.1.1.2. Production Ready

1.1.1.2.1. Coding Expertise

1.1.1.2.2. No Coding Expertise

1.1.2. Infrastructure control needed

1.1.2.1. Development

1.1.2.1.1. Deep Learning

1.1.2.1.2. Machine Learning

1.1.2.2. Production Ready

1.1.2.2.1. SQL Server (IaaS) Data Source

1.1.2.2.2. Storage Based Data Source

1.2. On-Premises Strategy

1.2.1. R/Python Coding Expertise

1.2.1.1. No Data Movement Strategy

1.2.1.1.1. SQL Server Data Source

1.2.1.1.2. Storage Data Source

1.2.1.2. Flexible Strategy

1.2.1.2.1. Notebook Strategy

1.2.1.2.2. T-SQL Expertise

2. Operationalization

2.1. Cloud Strategy

2.1.1. PaaS Strategy

2.1.1.1. No Data Movement Strategy

2.1.1.1.1. SQL Server (IaaS) Machine Learning Services

2.1.1.1.2. SQL Database Machine Learning Services

2.1.1.1.3. Legacy Hadoop Ecosystem

2.1.1.1.4. No Hadoop Ecosystem Based Model

2.1.1.2. Real-time or request/response

2.1.1.2.1. Azure Machine Learning Studio Model

2.1.1.2.2. Modular deployment (Containers)

2.1.1.2.3. Stream Analytics Integration

2.1.1.3. Batch Processing

2.1.1.3.1. Modular deployment (Containers)

2.1.1.3.2. Very Large Volume

2.1.1.4. Azure Machine Learning Studio Model

2.1.1.4.1. Azure Machine Learning Web Service Plan

2.1.1.5. Azure Machine Learning Service

2.1.1.5.1. Modular deployment (Containers)

2.1.2. Infrastructure Control Needed

2.1.2.1. Azure Machine Learning Studio Model

2.1.2.1.1. PaaS Strategy

2.1.2.2. Modular deployment (Containers)

2.1.2.2.1. Dev/Test Environment

2.1.2.2.2. Production Ready

2.1.2.3. Legacy Distributed Processing

2.1.2.3.1. SQL Server (IaaS) Machine Learning Services

2.1.2.3.2. Legacy Hadoop Ecosystem

2.1.2.4. Azure Machine Learning Service

2.1.2.4.1. Modular deployment (Containers)

2.2. On-Premises Strategy

2.2.1. Azure Machine Learning Studio Model

2.2.1.1. Cloud Strategy

2.2.2. Modular deployment (Containers)

2.2.2.1. Dev/Test Environment

2.2.2.1.1. Defined Request volume

2.2.2.1.2. Auto Scale Needed

2.2.2.2. Production Ready

2.2.2.2.1. Kubernetes Cluster

2.2.3. SQL Server Machine Learning Services Based Models

2.2.3.1. SQL Server (IaaS) Machine Learning Services

2.2.4. R/Python Based Models

2.2.4.1. Distributed Processing Needed

2.2.4.1.1. Microsoft Machine Learning Server on Hadoop

2.2.4.1.2. Cloudera Cluster

2.2.4.1.3. Hortonworks Cluster

2.2.4.2. Single Node

2.2.4.2.1. Microsoft Machine Learning Server