
Training a machine learning (ML) model is a process in which a machine learning algorithm is fed with data to learn from it to perform a specific task (e.g. classification) and finally have the ability to make predictions. The term "machine learning model" refers to the model artifact that is produced as a result of the training process.
In this article, you will learn how to estimate the time and cost of training a machine learning model.
The core of the machine learning lifecycle is model training, where the machine learning team strives to fit an algorithm to the data. The aim is to create a trained machine learning model with good performance that can make predictions on new or unseen data.

Machine learning models are beneficial to businesses in a variety of ways, including quickly analyzing vast amounts of data, locating anomalies, and discovering patterns that would be difficult for a human to perform alone.
There are several types of machine learning models, including supervised and unsupervised learning.
Supervised learning is defined by using labeled datasets to teach algorithms how to correctly classify data or predict outcomes. As data is put into the machine learning algorithm, its weights are changed until the model fits correctly. Supervised learning models help companies solve a variety of real-world problems on a large scale, such as detecting fraudulent transactions on an e-commerce platform.
Unsupervised learning involves the process of utilizing algorithms to discover patterns in data sets that do not contain labeled data points. This type of learning is referred to as "learning without supervision". These algorithms uncover previously unknown patterns or data groupings without requiring any involvement from a data scientist. It is ideally suited for customer segmentation, as well as recommendation systems because of its ability to find similarities and differences in information,
Why Is It Important to Estimate the Time and Cost to Train Machine Learning Models?
It is of utmost importance to make an accurate estimation of the time and cost required to train a machine learning model. This is especially true when you are training your model on a massive amount of data in the Cloud environment. If you are working on a machine learning project, being aware of the length of the training period can assist you in making important decisions.
For instance, if the training period will be longer than you anticipated, you have the option of adjusting the parameters of your algorithm or choosing an alternative algorithm to implement in order to shorten the amount of time required to train your model. This is very important when you plan to run various machine learning experiments over a short period of time.
There are a lot of Machine Learning practitioners who are interested in finding out how long it takes to train a machine learning model. As an example, below is the question that was asked in the Stack Exchange forum.
"I’d like to know ahead of time if my training will take 8 hours, 8 days or 8 weeks. (The 8 was an arbitrary number I chose, obviously). Is there a reliable way to estimate the time it will take? Can I extrapolate the time it takes to train 200,000 as roughly double the time it takes to train 100,000? It would be helpful to be able to estimate whether it will take a couple hours or a couple days or even weeks because then I can tweak the parameters ahead of time." – by Chowza
You can also read other questions at the following links:
- How long does it take to train deep neural networks? Would it be feasible for an individual to replicate the performance of deep neural networks on the MNIST dataset?
- How long does an image classification deep learning network take to train?
Cloud instances come with different features and costs. Depending on your workload and your specific data, you might choose to pay more for a high-performance instance or save money by using a low-cost instance. Knowing which cloud instance you can select to train your machine learning model can be challenging for both experienced and inexperienced data scientists.
Sometimes you can choose to use a cloud instance that has a low price, but it may support training your machine learning model over a long period of time and eventually add more costs than what you had planned to spend. On the other hand, a high-performance cloud instance might cost much more, but it helps train your machine learning model faster.
Estimating the cost of training a machine learning model can assist you in planning your budget and purchasing an appropriate cloud instance for your machine learning project.
How to Estimate Machine Learning Model Training Time and Cost
The Aipaca team is currently developing a robust open-source tool called the Training Cost Calculator (TCC) that can assist you in predicting the time necessary to complete the training process for neural networks (Tensorflow and Pytorch) by using:
- Features of the models
- Software environments
- Computing hardware
It also has the ability to predict the cloud computing costs for various machine learning tasks on different cloud instances. TCC is an excellent resource that you can use to budget your computing costs and reduce your spending.
The demo available on the Aipaca website can show you how to find out the time required and the cost of training your machine learning model.
In the demo section, you have to fill in the following features related to your machine learning model that you want to train in the cloud environment.
Note: The demo results are randomly generated for proof of concept purposes only.
1. The Model HubThis is a collection of pre-trained self-contained deep learning models for a wide range of applications. You can select one of the following model hubs:
- Hugging Face
- Pytorch Hub
- Tensorflow Hub
2. Model NameThis is the name of the pre-trained model you want to use for your project. Here is the list of available pre-trained models you can select from the demo.
- BERT
- DistilGPT2
- GPT2
- ROBERTA
3. Data SizeYou have to specify the size of the dataset on which you are going to train your machine learning model. The size must be in Gigabyte format. For example, 2 GB or 3.45 GB.
4. Epochs This is the hyperparameter that defines the number of times the learning algorithm will train through the full training dataset. For example, you can define the number of epochs to be 100.
5. Batch SizeThe batch size is a hyperparameter that specifies how many samples to run through before updating the model parameters. For example, you can specify the batch size to be 64 samples.
Now that you know all the necessary features, you need to specify their values in order to estimate the time and cost of training your machine learning model. The next step is to fill in those values in the demo.
For example:
- Model Hub: Hugging Face
- Model Name: BERT
- Data Size: 5 GB
- Number of Epochs: 100
- Batch Size: 64
Click here to open the demo section and fill in the values.

Click the Calculate button to get the results.
After completing the calculation process, you can view the results, which will include the names of cloud instances, predicted training times and predicted costs.

From the above results, you can see that an AWS cloud instance called c6g.8xlarge.od will take 6.19 hours to train the machine learning model at a total cost of $6.75. The cost may be high compared to other cloud instances, but you will save a lot of time if you plan to run multiple machine learning experiments.
Contributors
The Training Cost Calculator (TCC) is an excellent productivity-enhancing tool for machine learning projects. Instead of depending on your experience to estimate how long it will take to train your model and which cloud instances to utilize, the TCC will enable you to quickly determine the ideal training time and cloud instances appropriate for your project.
The first version of the Training Cost Calculator (TCC) will be released very soon with more features and details on how you can try to estimate the training time and cost of the cloud instances in your own environment.
This is a 100% Open Source project, and we are eager to see more contributors join us in the community.
More details will be added to the Github repository when the first version of TCC is released. Please refer to the repository for future updates or contact us for any questions.
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