Best Practices Rules of Machine Learning Engineering

Abide to these rules to get the highest value from your machine learning

Photo by Maxim Hopman on Unsplash

Machine learning has become increasingly popular in recent years, with applications ranging from image recognition to natural language processing. However, building a successful machine-learning system requires more than just knowledge of algorithms and models.

Part 1: Before Machine Learning

Rule #1: Don’t be afraid to launch without machine learning.

One common mistake many companies make is delaying launching a product or solving a business problem until they have a perfect machine-learning model.

However, this can lead to missed opportunities and wasted resources. Instead, launching with a simple heuristic or rule-based system is better and then iterating based on the feedback or the data.

Rule #2: Make metrics design and implementation a priority.

Metrics are essential for evaluating the performance of your machine learning system. Choosing metrics that align with your business goals and implementing them correctly from the beginning is important. This will help you avoid costly mistakes down the line.

Rule #3: Choose machine learning over a complex heuristic.

While heuristics can be useful in some cases, they are often difficult to maintain and can become outdated quickly. In contrast, machine learning models can adapt to changing data and improve over time. Therefore, it is generally better to choose machine learning over a complex heuristic when possible.

Part 2: Building Your First Pipeline

Once you have decided to build a machine learning system, the next step is to create your first pipeline. This involves collecting data, preprocessing it, training models, and deploying them in production.