Blog post

Training versus Inference

By Paul DeBeasi | February 14, 2019 | 6 Comments

Machine LearningIoTInternet of ThingsArchitecture

Few data-driven technologies provide greater opportunity to derive value from Internet of Things (IoT) initiatives as machine learning. The accelerated growth of data captured from the sensors in IoT solutions and the growth of machine learning capabilities will yield unparalleled opportunity for organizations to drive business value and create a competitive advantage.

An important development in machine learning is the emergence of machine learning inference servers (aka inference engines and inference servers). The machine learning inference server executes the model algorithm and returns the inference output. Refer to my blog post for more information about machine learning inference servers.

As the number of IoT endpoints proliferate, the need for organizations to understand how to design systems that integrate machine learning inference with IoT will grow rapidly. Given the fact that IoT solutions are distributed systems, a key design question is “Where should my organization deploy the machine learning inference server in the distributed IoT system?” Refer to my blog post for more information about the four options that form the foundation for creating a system design that integrates machine learning with IoT.

Machine Learning Training Versus Inference

However, before technical professionals can begin to design a system that integrates a machine learning inference server with IoT, they must understand the relationship between how IoT data can be used for machine learning model training versus inference.  Refer to the figure below to compare training versus inference.

Image comparing Machine Learning Training versus Inference
Machine Learning Training versus Inference
  • Training: Training refers to the process of using a machine learning algorithm to build a model. Training involves the use of a deep-learning framework (e.g., TensorFlow) and training dataset (see the left-hand side of Figure). IoT data provides a source of training data that data scientists and engineers can use to train machine learning models for a variety of use cases, from failure detection to consumer intelligence.
  • Inference: Inference refers to the process of using a trained machine learning algorithm to make a prediction. IoT data can be used as the input to a trained machine learning  model, enabling predictions that can guide decision logic on the device, at the edge gateway or elsewhere in the IoT system (see the right-hand side of Figure).

New Gartner Research

New research from Gartner helps technical professionals overcome the challenge of integrating machine learning with IoT.  It analyzes four reference architectures and ML inference server technologies. IoT architects and data scientists can use this research to improve cross-domain collaboration, analyze ML integration trade-offs and accelerate system design. Each reference architecture can be used as the basis of a high-level design or can be combined to form a hybrid design.

You can view the 39 page research report here: Architecting Machine Learning With IoT.

The Gartner Blog Network provides an opportunity for Gartner analysts to test ideas and move research forward. Because the content posted by Gartner analysts on this site does not undergo our standard editorial review, all comments or opinions expressed hereunder are those of the individual contributors and do not represent the views of Gartner, Inc. or its management.

Leave a Comment


  • SaiAnudeep says:

    Hi ! Thanks for sharing this informative post

  • Thanks for sharing such a great blog Keep posting.

  • Hi Paul, this is generally a good post, but you have your definition of “Training” wrong.

    You say: “Training refers to the process of creating an machine learning algorithm.”

    This is incorrect. Training refers to the process of USING a machine learning algorithm to build a model.

  • William P Caine says:

    Thank you for making this digestible.

  • nice article and good explanation about the interfaces of different topics of AI relation.good information.

  • Paul DeBeasi says:

    Thank you for your comment. I updated the text.