![]() The training speed of a machine learning platform depends on numerous factors: This can make it impractical to use some platforms for some applications. Moreover, before deploying a model, it is usually necessary to train many candidate models to select the best-performing one. Training machine learning models with big datasets can take several hours. Indeed, modeling big data sets is very expensive in computational terms. One of the most critical factors in machine learning platforms is the time they need to train the models. Platform A would throw a memory allocation error, while Platform B would train the model. Therefore, we can say that the capacity of Platform B is 1.5 times the capacity of Platform A.Īs a practical case, consider that our computer has 16 Gb RAM and our data set has 500,000 samples. The following figure illustrates the result of a data capacity test with two platforms.Īs we can see, Platform A can analyze up to 400,000 samples, while Platform B can analyze up to 600,000 samples. Note that the selection of a dataset suite is necessary. To compare the data capacity of machine learning platforms, we follow the next steps:Ĭhoose a reference computer (CPU, GPU, RAM.).Ĭhoose a reference benchmark (data set, neural network, training strategy).Ĭhoose a reference model (number of layers, number of neurons.).Ĭhoose a reference training strategy (loss index, optimization algorithm.).Ĭhoose a stopping criterion (loss goal, epochs number, maximum time.). The optimization algoritms it contains (SGD, Adam, LM.). The strategies used within the code for the efficient use of memory. The programming language in which it is written (C++, Java, Python.). ![]() We can measure data capacity as the number of samples that a machine learning platform can process for a given number of variables. In this way, the tool should perform all the essential tasks with that dataset. ![]() The data capacity of a machine learning platform can be defined as the biggest dataset that it can process. Therefore, tools capable of processing these volumes of data are necessary. However, machine learning platforms may crash due to memory problems when building models with big datasets. Nowadays, common datasets used in machine learning might contain thousands of variables and millions of samples. The business processes that explain why these firms are successful. In this way, they learn how well the targets perform and, more importantly, Usually to increase some aspect of performance. This allows organizations to develop plans on making improvements or adapting specific best practices, Key performance indicators typically measured here are data capacity, training speed, inference speed, and model precision.īenchmarking is used to measure performance using a specific indicator resulting in a metric that is then compared to others. Therefore, for machine learning tools to be efficient, they need to process large amounts of data in the shortest time possible. This post aims to identify the most critical key performance indicators (KPIs) and define a consistent measurement process.Īs we know, the volume, variety, and velocity of information stored in organizations are increasing significantly. However, comparing different machine learning platforms can be a difficult task due to the large number of factors involved in the performance of a tool. In machine learning, benchmarking is the practice of comparing tools to identify the best-performing technologies in the industry. How to benchmark the performance of machine learning platforms How to benchmark the performance of machine learning platforms:ĭata capacity, training speed, inference speed and model precision | Neural Designer
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