3 Techniques to Make your Machine Learning more efficient[Technique Tuesdays]
Simple techniques to speed up your processes
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Stanford recently released their AI index report for 2023- Measuring Trends in Artificial Intelligence. In it, they had a few very interesting things to say about AI and how it was headed-
The number of AI-related job postings has increased on average from 1.7% in 2021 to 1.9% in 2022.
Governments are looking at AI and AI-related laws more often.
Organizations that have adopted AI report realizing meaningful cost decreases and revenue increases.
All of these point to very strong job prospects in AI going forward. And since this newsletter is geared towards helping you thrive in Tech, I would have been remiss to not cover it. Here are a few techniques to help you make the AI processes in your organizations more efficient. Whether you’re going to be involved with them directly (manager or AI engineer) or indirectly (as a colleague or someone looking to transition into AI) these will help you a bunch.
PS: If you’d like to discuss this report and its findings, you can check out my LinkedIn post or the Twitter thread I made on this report and its findings.
3 Techniques to keep your Machine Learning more efficient
Reducing your Data Feature Size- With all the noise around LLMs and generative AI, it can be easy to forget- supervised learning and feature engineering are still king. However, people overlook one crucial fact- more features are not always better. You should put some effort into reducing the number of features and engineering away the less useful features to keep the dataset as small and simple as possible. It’ll keep your costs down, performance up, and systems running smoothly.
Active Learning- Active Learning is based on a simple concept. From the perspective of a Machine Learning Model, all data points are not created equal. Some points are easy to handle, while others require more finesse. If you have a lot of data, then chances are that there is a lot of overlap in data points. So you can discard a significant portion with no problems. Think back to your Leetcode prep. Once you get very good at graphs, you no longer benefit from solving more graph problems.
Applying Filters and letting simple models do most of your tasks- People love using big powerful models to solve everything. However, you’re better off working on smaller models for specific tasks. Instead of trying to build ‘AGI’, let simple models/filters do most of your tasks and use your large AI model only when it is absolutely needed. You’ll see better performance and lower costs.
If you’re interested in implementing large language models, I have an article dedicated to how you can build better language models in the article below. It covers the most recent research conducted in this domain, to allow you to build NLP models that are safer, more efficient, and still hit peak performance.
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