Thanks to recent developments and new releases, machine learning is everywhere. With the growing popularity of AI and ML and the growing demand for production-grade ML models, discovering ML problems and configuring solutions for them has become critically important. Design patterns are the best way to narrow down solutions to ML-related problems. Ideas for patterns help define a problem and find detailed solutions to that problem. This can be reused many times for similar problems.
Design patterns organize knowledge into instructions that practitioners around the world can follow. Different ML design patterns are used at different stages of the ML lifecycle. Some of them are used for problem framing, feasibility assessment, or addressing the ML model development or deployment stage. Recently, a Twitter user of his named Eugene Yan discussed design patterns for machine learning systems in a thread. He listed some of them in his tweet.
- Cascade: Cascade decomposes a complex problem into simpler problems and uses subsequent models to tackle more difficult or specific problems. A shared example is about Stack Exchange, an online community platform, and how it uses a cascade of defenses against spam. It consists of multiple layers of protection to detect and prevent spam from being posted on the platform, each layer focusing on a different aspect of spam detection. The first line of defense is if someone posts too fast (HTTP 429 error), second if someone is caught by regular expressions and rules (heuristic) (heuristic), third is shadow testing (ML) very accurate based on Cascading is an effective approach because it works systematically and hierarchically. See resources here.
- Reframing – Reframing involves redefining the original problem to make it easier to solve. The example given in the tweet is about Alibaba, a large e-commerce platform, which reframes the sequential recommendation paradigm to predict which items users are likely to interact with next. Helpful. See resources here.
- Human Participation – This involves collecting labels or annotations from users, annotation services, or domain experts to improve the performance of ML models. Examples mentioned in tweets are Stack Exchange and LinkedIn, where users can flag spam posts. This allows users to provide feedback on spam content. Using this he can train ML models to better detect spam and filter out offensive messages in the future. See resources here.
- Data Augmentation – Create synthetic variations of your training data to increase its size and diversity, improve your ML model’s ability to generalize, and reduce the risk of overfitting. An example of DoorDash, a food delivery platform, where data augmentation is used to address the challenge of accurately classifying and tagging new menu items with limited or no data available to train the model. is mentioned. See resources here.
- Data Flywheel – A positive feedback loop where collecting more data improves the ML model, leading to more users and data. Tesla’s example is shared in collecting data from cars, including sensor data, performance metrics, and usage patterns. This data is used to identify and label errors to help improve models used for tasks such as autonomous driving. See resources here.
- Business rules: These include adding logic or constraints to enhance or adjust the ML model’s output based on domain knowledge and business requirements. Twitter uses ML models to predict engagement. This will adjust the tweet’s visibility in your timeline. It also uses manually tuned weights or rules as constraints on the output of ML models to incorporate knowledge into the decision-making process. See resources here.
As a result, design patterns for machine learning systems can improve model performance, reliability, and interpretability, helping to solve challenges in this field.
This article is this tweet. don’t forget to join Our 19k+ ML SubReddit, cacophony channeland email newsletterWe share the latest AI research news, cool AI projects, and more. If you have any questions about the article above or missed something, feel free to email me. Asif@marktechpost.com
🚀 Check out 100 AI Tools in the AI Tools Club
Tanya Malhotra is a final year student at the University of Petroleum and Energy Research, Dehradun, with a Bachelor of Science degree in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
A data science enthusiast with good analytical and critical thinking, she has a keen interest in learning new skills, leading groups, and managing work in an organized manner.
