The Five Ways To Build Machine Learning Models

 Machine learning is powering most of the recent advancements in AI, including computer vision, natural language processing, predictive analytics, autonomous systems, and a wide range of applications. Machine learning systems are core to enabling each of these seven patterns of AI. In order to move up the data value chain from the information level  to the knowledge level, we need to apply machine learning that will enable systems to identify patterns in data and learn from those patterns to apply to new, never before seen data. Machine learning is not all of AI, but it is a big part of it.

While building machine learning models is fundamental to today’s narrow applications of AI, there are a variety of different ways to go about realizing the same ends. So-called machine learning platforms facilitate and accelerate the development of machine learning models by providing functionality that combines many necessary activities for model development and deployment. Since the fields of machine learning and data science are not new, there are a large number of tools that help with different aspects of machine learning development.
Five Key Platforms for Building Machine Learning Models

There are five major categories of solutions that provide machine learning development capabilities:

    Machine Learning toolkits
    Machine Learning Platforms
    Analytics Solutions
    Data Science Notebooks
    Cloud-native Machine Learning as a Service (MLaaS) offerings.

There are seven primary patterns in the way that AI is implemented for applications. At the high level those seven patterns are shown below:

The Seven Patterns of AI
 
 

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