{"id":3392,"date":"2023-08-31T10:59:22","date_gmt":"2023-08-31T10:59:22","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/how-to-benchmark-the-performance-of-machine-learning-platforms\/"},"modified":"2025-08-28T15:33:53","modified_gmt":"2025-08-28T13:33:53","slug":"how-to-benchmark-the-performance-of-machine-learning-platforms","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/how-to-benchmark-the-performance-of-machine-learning-platforms\/","title":{"rendered":"How to benchmark the performance of machine learning platforms"},"content":{"rendered":"<section>This post aims to identify the most critical key performance indicators (KPIs) and define a consistent measurement process.\u00a0In machine learning, benchmarking is used to compare tools and identify the best-performing technologies in the industry.<\/section>\n<section>However, comparing different machine learning platforms can be challenging due to the numerous factors that influence a tool&#8217;s performance.<\/p>\n<h3>Contents<\/h3>\n<ul>\n<li><a href=\"#Objectives\">Performance benchmarking<\/a>.<\/li>\n<li><a href=\"#DataCapacityTests\">Data capacity tests<\/a>.<\/li>\n<li><a href=\"#TrainingSpeedTests\">Training speed tests<\/a>.<\/li>\n<li><a href=\"#InferenceSpeedTests\">Inference speed tests<\/a>.<\/li>\n<li><a href=\"#ModelPrecisionTests\">Model precision tests<\/a>.<\/li>\n<li><a href=\"#Conclusions\">Conclusions<\/a>.<\/li>\n<\/ul>\n<\/section>\n<section>\n<h2>Performance benchmarking<\/h2>\n<p>As we know, the volume, variety, and velocity of information stored in organizations are increasing significantly.<\/p>\n<p>Therefore, for machine learning tools to be efficient, they must process large amounts of data in the shortest time possible.<\/p>\n<p>Key performance indicators typically measured here are data capacity, training speed, inference speed, and model precision.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/data-capacity.svg\" \/><\/p>\n<h3 style=\"text-align: center;\"><strong>DATA CAPACITY<\/strong><\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/training-speed.svg\" \/><\/p>\n<h3 style=\"text-align: center;\"><strong>TRAINING SPEED<\/strong><\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/model-precision.svg\" \/><\/p>\n<h3 style=\"text-align: center;\"><strong>MODEL PRECISION<\/strong><\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/inference-speed.svg\" \/><\/p>\n<h3 style=\"text-align: center;\"><strong>INFERENCE SPEED<\/strong><\/h3>\n<p>Benchmarking measures performance using a specific indicator, resulting in a metric that is then compared to others.<\/p>\n<p>This enables organizations to develop plans for implementing improvements or adapting specific best practices, typically to enhance a particular aspect of performance.<\/p>\n<p>In this way, they learn how well the targets perform and, more importantly, the business processes that explain why these firms are successful.<\/p>\n<\/section>\n<section><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/data-capacity.svg\" \/><\/p>\n<h2>Data capacity tests<\/h2>\n<p>Nowadays, common datasets used in machine learning might contain thousands of variables and millions of samples.<\/p>\n<p>However, when building models with big datasets, machine learning platforms may crash due to memory problems.<\/p>\n<p>Therefore, tools capable of processing these volumes of data are necessary.<\/p>\n<p>The data capacity of a machine learning platform can be defined as the biggest dataset that it can process. In this way, the tool should perform all the essential tasks with that dataset.<\/p>\n<p>We can measure data capacity as the number of samples a machine learning platform can process for a given number of variables.<\/p>\n<p>This metric depends on numerous factors:<\/p>\n<ul>\n<li>The programming language in which it is written (C++, Java, Python&#8230;).<\/li>\n<li>The strategies used within the code for the efficient use of memory.<\/li>\n<li>The optimization algorithms it contains (SGD, Adam, LM&#8230;).<\/li>\n<\/ul>\n<p>To compare the data capacity of machine learning platforms, we follow the next steps:<\/p>\n<ol>\n<li>Select a reference computer (e.g., CPU, GPU, RAM).<\/li>\n<li>Choose a reference benchmark (data set, neural network, training strategy).<\/li>\n<li>Choose a reference model (number of layers, number of neurons&#8230;).<\/li>\n<li>Choose a reference training strategy (loss index, optimization algorithm&#8230;).<\/li>\n<li>Choose a stopping criterion (loss goal, epochs number, maximum time&#8230;).<\/li>\n<\/ol>\n<p>Note that selecting a dataset suite is necessary.<\/p>\n<p>The following figure illustrates the result of a data capacity test with two platforms.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/data-capacity-test.webp\" alt=\"Result of a data capacity test with two platforms. We can observe the data capacity of Platform B is higher than that of Platform A.\" width=\"800\" height=\"400\" \/><\/p>\n<p>As we can see, Platform A can analyze up to 400,000 samples, while Platform B can analyze up to 600,000 samples.<br \/>\nTherefore, we can say that the capacity of Platform B is 1.5 times the capacity of Platform A.<\/p>\n<p>As a practical example, consider that our computer has 16 GB of RAM and our dataset has 500,000 samples. Platform A would throw a memory allocation error, while Platform B would train the model.<\/p>\n<\/section>\n<section><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/training-speed.svg\" \/><\/p>\n<h2>Training speed tests<\/h2>\n<p>One of the most critical factors in machine learning platforms is the time they need to train the models.<br \/>\nIndeed, modeling big data sets is very expensive in computational terms.<\/p>\n<p>Training machine learning models with big datasets can take several hours.<br \/>\nMoreover, before deploying a model, it is usually necessary to train many candidate models to select the best-performing one.<br \/>\nThis can make it impractical to use some platforms for some applications.<\/p>\n<p>The training speed of a machine learning platform depends on numerous factors:<\/p>\n<ul>\n<li>The programming language in which it is written (C++, Java, Python&#8230;).<\/li>\n<li>The high-performance computing (HPC) techniques that it implements (CPU parallelization, GPU acceleration&#8230;).<\/li>\n<li>The optimization algorithms it contains (SGD, Adam, LM&#8230;).<\/li>\n<\/ul>\n<p>Training speed is usually measured as the number of samples per second that the platform processes during training.<\/p>\n<p>To compare the training speed of machine learning platforms, we follow the next steps:<\/p>\n<ol>\n<li><b>Choose a reference benchmark<\/b> (data set, neural network, training strategy&#8230;).<\/li>\n<li><strong>Select a reference computer<\/strong> (e.g., CPU, GPU, RAM).<\/li>\n<li><b>Compare the training speed<\/b>.<\/li>\n<\/ol>\n<p>The following figure illustrates the results of a training speed test conducted on two platforms.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/training-speed-test.webp\" \/><\/p>\n<p>As we can see, the training speed of Platform 1 is 200,000 samples\/second, while that of Platform 2 is 350,000 samples\/second.<br \/>\nTherefore, we can say that the training speed of Platform B is 1.75 times the capacity of Platform A.<\/p>\n<p>To illustrate this, consider a dataset with 1,000,000 training samples and an optimization algorithm that runs for 1,000 epochs. The training time for Platform A is 1 hour, 23 minutes, and 20 seconds, and the training time for Platform B is 00 hours, 47 minutes, and 37 seconds.<\/p>\n<\/section>\n<section><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/model-precision.svg\" \/><\/p>\n<h2>Model precision tests<\/h2>\n<p>The primary objective of machine learning is to develop models that achieve high accuracy.<\/p>\n<p>We can define precision as the mean error of a model against a testing data set.<\/p>\n<p>The precision of a machine learning platform depends on numerous factors:<\/p>\n<ul>\n<li>The optimization algorithm with which it has been trained (HPC).<\/li>\n<li>The programming language in which it is written (C++, Java, Python&#8230;).<\/li>\n<li>The high-performance computing (HPC) techniques that it implements (CPU parallelization, GPU acceleration&#8230;).<\/li>\n<\/ul>\n<p>We follow the next steps to compare the precision of different machine learning platforms:<\/p>\n<ol>\n<li><strong>Select a reference computer<\/strong> (e.g., CPU, GPU, RAM).<\/li>\n<li><b>Choose a reference dataset<\/b> (variables and sample number).<\/li>\n<li><b>Choose a reference model<\/b> (number of layers, number of neurons&#8230;).<\/li>\n<li><b>Choose a reference training strategy<\/b> (loss index, optimization algorithm&#8230;).<\/li>\n<li><b>Choose a stopping criterion<\/b> (loss goal, epochs number, maximum time&#8230;).<\/li>\n<\/ol>\n<p>A dataset here should allow us to reach an error of 0.<\/p>\n<p>The following table illustrates the result of an accuracy test with two platforms:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/model-precision-test.webp\" \/><\/p>\n<p>As we can see, Platform A can build a model with a correlation of 0.8. On the other hand, Platform B can build a model with a correlation of 0.9. Therefore, we can say that the precision of Platform B is 1.12 times bigger than that of Platform A.<\/p>\n<\/section>\n<section><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/inference-speed.svg\" \/><\/p>\n<h2>Inference speed tests<\/h2>\n<p>In many applications, particularly those requiring real-time performance, the model&#8217;s response time is a critical factor. Indeed, an inference time of a few milliseconds can make the model impractical.<\/p>\n<p>The inference speed can be defined as the time required to calculate the model&#8217;s outputs as a function of its inputs. To measure this metric, we use the number of samples per second.<\/p>\n<p>The inference speed of a machine learning platform depends on numerous factors.<\/p>\n<ul>\n<li>The programming language in which it is written (C++, Java, Python&#8230;).<\/li>\n<li>The high-performance computing (HPC) techniques that it implements (CPU parallelization, GPU acceleration&#8230;).<\/li>\n<\/ul>\n<p>To compare the inference speed of machine learning platforms, we follow the next steps:<\/p>\n<ol>\n<li><strong>Select a reference computer<\/strong> (e.g., CPU, GPU, RAM).<\/li>\n<li><b>Choose a reference input set<\/b> (variables and samples number).<\/li>\n<li><b>Choose a reference model<\/b> (number of layers, number of neurons&#8230;).<\/li>\n<\/ol>\n<p>The following figure illustrates the results of an inference speed test conducted on two platforms.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/inference-speed-test.webp\" \/><\/p>\n<p>As we can see, in one second, Platforms A and B can calculate the outputs for 700,000 and 900,000 inputs, respectively.<br \/>\nTherefore, we can say that the inference speed of Platform B is 1.28 times bigger than that of Platform A.<\/p>\n<\/section>\n<section>\n<h2>Conclusions<\/h2>\n<p>This post aims to define the most important KPIs (critical key performance indicators\u00a0)\u00a0in machine learning platforms for benchmarking in the industry.<\/p>\n<p>It also describes the most relevant factors affecting those key performance indicators.<\/p>\n<p>Finally, it describes how to design and measure performance tests for data capacity, training speed, model precision, and inference speed.<\/p>\n<p>The data science and machine learning platform <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a> utilizes high-performance techniques to maximize productivity.<\/p>\n<p>You can <a href=\"https:\/\/www.neuraldesigner.com\/free-trial\">download<\/a> Neural Designer now and try it for free.<\/p>\n<\/section>\n<section>\n<h2>Related posts<\/h2>\n<\/section>\n<style><![CDATA[ @media all and (max-width: 1000000px) { .content .x700 { display: block; } .content .x490 { display: none; } .content .x290 { display: none; } } @media all and (max-width: 800px) { .content .x700 { display: none; } .content .x490 { display: block; } .content .x290 { display: none; } } ]]><\/style>\n","protected":false},"author":13,"featured_media":2566,"template":"","categories":[],"tags":[37],"class_list":["post-3392","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-platforms"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to benchmark the performance of machine learning platforms<\/title>\n<meta name=\"description\" content=\"Identify performance KPIs for benchmarking machine learning platforms: data capacity, training speed, inference speed, and model precision.\" 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