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How to build a machine learning algorithm

Dr Bernard Kornfeld, 15 July 2022

The application of machine learning algorithms to real-time data within an integrated data, AI + IoT platform can significantly elevate your IoT solution, turning it from nice-to-have real-time monitoring to an ever-growing, ROI achieving optimization engine.

What machine learning enables you to do is find patterns hidden deep within huge data sets to uncover better ways of operating, uncover efficiencies, and predict (and thereby enable you to prevent) problems or catastrophes before they occur.

Unlike traditional programming, which is deterministic, Machine Learning relies on conjecture. The variables are just that, variable, and the outcomes that will result from altering them are not guaranteed – it may take many iterations to reach the desired or better outcome from the status-quo.

Whilst the outcomes are uncertain, the process to getting to them isn’t.

The 9 steps to building a machine learning algorithm:

  1. Hypothesis formulation – it is critical before starting to build a machine learning algorithm that it’s clear what the business problem you’re looking to solve is and what success would look like. Having clear, quantifiable measures of success is critical because without them, it would be impossible to determine if a machine learning algorithm is performing well or poorly. What this step will do is rule out a lot of potential scenarios for machine learning applications, because if it’s not possible to measure success or outcomes are subjective vs. objective, then there is no basis for going forwards.
  2. Data collection – once you know what you’re looking to achieve, it’s necessary to then identify the existing (and new) streams of data needed to assess all the variables that go into achieving the outcome. If you’re looking to do this in real-time within an integrated data, AI + IoT platform, then it’s also critical to also evaluate how you’re going to ensure the platform has ongoing, continuous access to the data in a timely manner.
  3. Data cleansing – standardizing data, and dealing with missing or incorrect data is also essential before creating the algorithm itself. Having lots of data isn’t necessarily enough if it’s inaccurate or not all in the same format and can’t be processed. This is something that the ‘data’ portion of the integrated data, AI + IoT platform is capable of, with features and functionality that enables you to clean, aggregate, and augment all of your data feeds.
  4. Label and transform data – a continuation of the ‘data’ section of the platform, it’s important that it is able to label the data it’s receiving to see if it, for example, passes or fails i.e. is accurate and enhancing the result or metric that you’re looking to improve, and that it is able to transform categorical data to numerical data, normalizing it for analysis alongside other data sets.
  5. Identify or build models – there is a whole host of pre-built machine learning models that you can apply to your data to help solve the type of problem you have. The Rayven integrated data, AI + IoT platform is capable of supporting any Python-based algorithm, but it also enables you to build your own machine learning algorithm using a simple drag-and-drop WYSIWYG interface. What’s critical is to select multiple machine learning that are fit for the real-world problem that you’re looking to solve, so that you can compare the outcomes from them to see what’s working best.
  6. Train models – during this stage, you use a clean, accurate sample of your data set to train the models as to what standard operating procedures looks like in the 1s and 0s to provide it with the opportunity to learn its patterns. From there, you select the features and identify hyperparameters (variables) within the algorithms that you want to alter and test in order to explore how you can improve the metric that’s directly related to the business objective and whether they achieve optimal performance.
  7. Evaluate and select model – once you’ve trained the model, you need to run it and evaluate each model’s quality by comparing its predictions against the actual results it delivers, determining the overall best performer based on the outcome it was created to deliver (predictive ability, speed, ease of use etc.).
  8. Model deployment – after selecting your winning algorithm you then need to deploy it to your real-world, live data. Superior integrated data, AI + IoT platforms will enable you to this simply (Rayven makes it as easy as dropping it into your Workflow Builder), and then visualize the results it’s generating in dashboards, which those on the ground, in the boardroom or anywhere else can interpret and clean insights from. You can even take this a step further in advanced systems such as Rayven’s, for example in the case of a machine learning algorithm that’s designed to predict equipment failure, and create custom workflows that will shut machinery down when an algorithm predicts a catastrophic failure, to save on repair costs or, more importantly, to prevent injury to nearby personnel.
  9. Monitor and reassess – once in place and deployed, you will need to continually review the performance of your machine learning algorithm and data model to reassess performance because, as data and circumstances change, changes to both may need to be made to further improve or sustain positive results.

The Rayven integrated data, AI + IoT platform, Dynamix, makes all of the above very simple. Because the platform is all-in-one, you can perform all of the above tasks using its easy-to-use interface, meaning advanced machine learning and data science task can be completed fast and affordably.

In addition, the solutions built upon the platform and designed for specific purposes (see I4 Mining or Factory One) feature a number of machine learning algorithms that you can use to achieve specific business results out-of-the-box, (including ones for regression, clustering, decision tree, k-means and regression), as well as the ability to build your own using the AI Dynamix drag-and-drop functionality or to deploy a pre-built Python-based algorithm.

To discuss your machine learning, IIoT and Industry 4.0 needs – or to just talk to use about exploring potential use cases for your industrial business – do get in contact, we’d love to help.

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