We’re excited to include guest posts on our blog from interesting people and companies in the industry. This post was written by Semih Yagcioglu, a machine learning mentor at Springboard. Springboard is an online learning platform that prepares students for the tech industry’s most in-demand careers, offering comprehensive programs in software engineering, data science, machine learning, UI/UX design, and more. Check out their article on how to structure a machine learning project.
If you are just breaking into machine learning, there is a good chance you have already experimented with pre-curated datasets and implemented a simple machine learning algorithm using a popular library on Jupyter Notebook.
But what happens when the problem you are trying to solve increases in complexity? In this guide, we will answer a question almost every machine learning professional asks themselves: how to structure a machine learning project?
Step 1: Store your data
Since data is key in machine learning problems, where to store data is the first question you should be asking yourself. The answer might vary a lot based on the setup you have or the problem you are trying to solve, as well as your constraints. In some projects, your data might be stored in a database or data might be coming as a stream. It’s useful to know where you are going to be storing the data, even if you’re in experimentation mode.
Step 2: Manage configurations
This is maybe one of the most costly parts of a machine learning project, but also one of the easiest to implement. Machine learning algorithms usually have tens, sometimes hundreds, of hyperparameters to tune; the rookie mistake most of us make is to store every hyperparameter in the code in a hard-coded way.
To address this, one thing you can do is to use a configuration file. Just update any hyperparameter you want from this file. Instead of making various copies of the same file with only hyperparameters changed, you can now dynamically change hyperparameters as arguments.
Step 3: Use helpers
In any machine learning project, there is a good chance that you will need one piece of code multiple times. This is where functions or some useful scripts come to play. In that regard, using helpers and keeping these utility scripts in a folder named “scripts” might be quite valuable to manage these resources in the long term.
Step 4: Manage experiments
Running experiments and analyses is essential to any machine learning project. You just need to make sure these experiments are structured in the right way—otherwise, things can get messy. Try creating a directory named “notebooks” to monitor and manage your experiments easily.
Step 5: Log everything
Did your model train well? Or has anything gone wrong during the first week of training? You don’t know? Logging is one of the easiest strategies you can include in your machine learning project structure. It helps to ensure you always know how your model is doing. Incorporate logging during training, validating, and inference for many different use cases. Try using a simple log file and storing logs under a relevant directory to give you the power to monitor your system’s performance.
Step 6: Baselines or models
Machine learning engineers spend a lot of time experimenting, especially with different models. Keeping different models in a structured manner under a directory named “models” or “baselines” is valuable in case you want to move forward with one particular model.
Step 7: Save checkpoints
This sounds like a very simple thing to do but is overlooked in many machine learning systems. Consider this example: you are training a model and soon discover the model’s generalization performance is very good. Are you going to miss the chance to use this model for inference? One solution is to save your model regularly in a directory and store different versions; this allows you to create ensembles from different variations of your model.
Step 8: Expose your models
Let’s say you’ve done everything we have discussed so far. There is still something you should consider for your machine learning project: exposing your model to product integration or allowing other services to consume your model’s abilities. To that end, you can create an API and serve this API under a directory. This way, your model or models should be able to interact with other models or systems easily.
Want to learn more?If you’re interested in learning more about machine learning engineering, check out Springboard’s Machine Learning Engineering bootcamp. You’ll design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or web service. (No other bootcamp does this!)
Springboard offers online courses and bootcamps in UI/UX design, data science, data analytics, software engineering, and machine learning engineering. All courses include 1-on-1 mentorship and Springboard’s one-of-a-kind job guarantee: students have a six-month runway to secure a role in their industry or get 100% of their tuition back.
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