Autonomous Solutions

The Five Ways To Build Machine Learning Models

Machine learning is powering most of the recent advancements in , including computer vision, , , autonomous systems, and a wide range of applications. Machine learning systems are core to enabling each of these seven patterns of .

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningIn order to move up the data value chain from the information level  to the knowledge level, we need to apply 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 , but it is a big part of it.

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

Five Key Platforms for Building Machine Learning Models

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

  1. Machine Learning toolkits
  2. Machine Learning Platforms
  3. Analytics Solutions
  4. Data Science Notebooks
  5. -native Machine Learning as a Service (MLaaS) offerings.

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

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