5 Ways To Optimize Your Machine Learning Workflow
Over the past years, machine learning (ML) has become increasingly popular across different industries such as gaming, healthcare, entertainment, and commerce, among others. The potential benefits of machine learning’s prominence are immense. For one, it allows companies to identify trends and patterns using data. It’s also easy to apply, especially now with all the platforms available on the internet.
However, although these platforms are indeed helpful, using an ML platform doesn’t necessarily mean you can achieve the optimal performance for your machine learning workflow.
Some organizations were able to succeed in machine learning despite not using any platform, and such achievement is only possible with their strategy. On that note, here are some of the most effective ways companies can optimize their machine learning workflow:
- Adopt A Set Of Practices For The Team
During the development of machine learning models, the team will encounter siloes every once in a while. A silo is a state where a group of individuals is isolated from the rest of the team, hindering communication and cooperation. As a result, the workflow of the entire team would fall into chaos. So, apart from being knowledgeable about machine learning development, encouraging collaboration among your team members is equally important.
That’s precisely why frameworks like DevOps and MLOps came to be. These are sets of rules promoting collaboration and eliminating siloes within the team. If you want to optimize your machine learning workflow, you might want to consider using these practices.
- Apply Feature Engineering
When working with machine learning models, you’ll often deal with bias—a phenomenon where the algorithm produces prejudiced results because of an error in the development process. If your model gets riddled with machine learning bias, its workflow will get disrupted, which would affect the model’s overall performance. On the bright side, you can reduce bias with various strategies, and one approach is to create new features.
Features are individual characteristics within a model that serves as the building block of an ML model. One way to create this is through feature engineering.
Feature engineering is the process of constructing features from raw data based on your existing knowledge. What makes feature engineering an incredibly powerful technique is it mainly uses your existing knowledge, which minimizes the possibility of bias.
- Test Multiple Models
Which model would make the best use of the data? This is, perhaps, the most crucial question data analysts ask themselves when working with machine learning.
For that, you need to consider a lot of things. More specifically, you need to determine if your features are compatible with the ML model. Unfortunately, it’s extremely difficult to know whether a model will bring you satisfactory results or not. That’s precisely why instead of focusing on one machine learning model, you’re better off testing multiple models at once. Not only will it allow you to determine the best one among your options, but it also allows you to identify issues on your chosen model through comparison.
- Hyperparameter Tuning
It’s no exaggeration to say that the performance of a machine learning model largely relies on its hyperparameters. Hyperparameters are variables determining the overall structure of an ML model. They’re also responsible for controlling the learning rate of the model, whether it’s large or small. So, if one wants to optimize the model’s workflow, they must first optimize the hyperparameters, and that’s where its tuning comes in.
Hyperparameter tuning is the process of choosing the variables most suitable for the learning rate of a model. This process usually involves steps such as comparing the output, evaluating the accuracy, and modifying hyperparameters.
- Obtain More Data
When dealing with data, you have what people call a training set size, which is how much data you’re going to use for the entire project. Usually, the training set size is pre-determined, but if you feel like you’re not doing the best for the ML model, consider increasing it by obtaining more raw data. This would also translate to new features, which are yet another essential component of the machine learning workflow.
The good news is there are several ways to obtain more data sets. For instance, you can add more similar data by searching from the web or looking for resources from platforms like Google APIs. It’s also best to look for open-source databases.
When developing a machine learning model, you can’t expect to get it right the first time as it’d go through countless tests before you can get a satisfactory outcome. On the bright side, there are numerous techniques to use if you want to optimize your machine learning workflow, and this guide should provide you with enough insights to get you going.