Machine Learning (ML) is a key component of Artificial Intelligence (AI). ML makes computer systems learn and get better from experience without being expressly programmed. You wouldn’t have to write code to describe and give explicit instructions to the computer on what action it should take. Machine learning makes computers adapt and learn from the experience with the help of datasets.
The quality of data, therefore, plays an integral part when training a reliable AI model. That’s why data scientists and engineers ensure that datasets used for training an AI model have top-notch quality.
An indispensable part of ensuring data quality is data annotation, a method used to record and label a dataset’s critical features that an ML system can learn to identify. A tool that does this, such as Kili Technology’s annotation tool, is essential to any ML project.
Best practices to improve machine learning workflows
ML workflows determine which stages are carried out on an ML project. These stages can include the following:
Data collection
Data pre-processing
Dataset creation
Model training/refinement
Evaluation
Deployment/Production
While these phases are generally considered standards, they aren’t rigidly followed. The stages can be flexible. Fitting a project into a rigid framework isn’t really advisable. ‘Be like water,’ as Bruce Lee once said. A flexible workflow allows you to adapt quickly to any changing situation during the project.
However, there are a few best practices you can use to improve your workflow and be more efficient. A few of these practices are discussed below:
Define your goals
A clear definition of your goals before starting your project can result in an efficient workflow. Efficiency can help you avoid unnecessary actions.
Use the following factors to guide you when defining your goals:
The process you’re currently using: Understand how the process you’re presently using works. Note its goals, what it counts as success and the people that drive the process. New models generally replace the current process. Analyzing these elements may give insights into the roles your project will eventually fill, the criteria your model is required to meet or exceed, and the restrictions and compliance during implementation.
Your expectations: A clear, unequivocal definition of what you expect your model to predict is essential. It’s a key factor in understanding the datasets you’ll need. Knowing the specifics of what you expect can give you an insight into how the ML model will be trained. For example, if the model is for predicting potential heart failure, the dataset focuses on patients’ cardiovascular health and related factors.
Your data sources: Analyze the current process’s data source, the amount of data gathered, and how it’s collected. This information can help you find out which data points and types are needed to make predictions.
Find the right strategy
A vital objective of an ML workflow is to increase the accuracy and efficiency of the process. To find the right strategy to reach this objective, you’ll need to do the following:
Research: It sometimes pays to check how other teams fared with a project similar to yours. You can learn from their approach, what they did right, steps that didn’t work out, etc. Learning from their historical data may save you and your team time and effort.
Test: If you’ve found an approach you’d think would work, whether an existing one or one designed by you, take some time testing it first. You can incorporate this strategy into your model’s testing and training phase.
Design a solution
Designing an approach or a solution usually leads to a proof of concept. But for your proof-of-concept to be any good, you need testing and experimentation to ensure that it works. For a successful transition from a concept to an actionable solution, the following can help you:
Machine learning API (Application Programming Interface): APIs help ML models to integrate with any app. This accessibility and ease of integration are essential for any ML model, especially for Machine Learning as a Service (MLaaS) models.
A/B testing: This test, also known as a randomized control trial or controlled experiment, helps businesses make data-driven decisions. It’s essentially a comparison between two versions of something to ascertain which is better. Data scientists use A/B testing to determine which ML model iteration performs better in the real world.
Documentation: Documentation means keeping records of methods, codes, and how the model is used. Show users how to use the model advantageously. Clearly state the results it can produce and how to get them. Also, ensure the documentation is user-friendly if you want your model to be marketable.
Conclusion
ML workflow helps you keep track of the stages of a machine learning project. However, these stages aren’t rigid, and you can adjust your style to adapt to the situation. A typical ML workflow seeks to improve the efficiency of any ML project.
This article discussed a few methods to make an ML workflow more efficient, such as defining your goals, finding the right strategy through research and testing, and designing a solution.
