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| ApplyingML | https://applyingml.com |
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Started a list of open-source LLMs with commercial licenses so you can fine-tune your own applications. Contributions welcome! 🙏
I wrote about 9 design patterns in machine learning systems, including HITL, Hard Mining, Cascade, Data Flywheel, Rules Layer, etc.
Each pattern lists their pros & cons, with several industry examples.
What other patterns have you seen? Please share! 🙏
Fun, interactive visualization of the data in Google's C4 (Colossal Clean Crawled Corpus) that's heavily used in LLM training.
Broken down by category and the top sites in each category.
https://www.washingtonpost.com/technology/interactive/2023/ai-chatbot-learning/
I had forgotten about this but it looks like my message-in-a-bottle got picked up by the web crawlers.
Also shows how it's not too difficult to add data into a search index that gets blended into an LLM's output. More in Simon's post here: https://simonwillison.net/2023/Apr/14/worst-that-can-happen/
A few more that I'll cover in a write-up:
• Aggregate raw data once: To reduce compute cost
• Evaluate before deploy: For safety and reliability
• Hard mining: To better learn difficult instances
What other design patterns or industry examples are there? Please share!
6. Business Rules: Adding logic or constraints based on domain knowledge and/or business requirements to augment or adjust the output of ML models
Twitter has various hand-tuned weights when predicting engagement probabilities: https://github.com/twitter/the-algorithm-ml/tree/main/projects/home/recap
5. Data flywheel: Positive feedback loop where more data improves ML models, which leads to more users and data.
Tesla collects data via cars, finds and labels errors, retrains models, and then deploys to their cars which gather more data.
“Potentially nitpicky but competitive advantage in AI goes not so much to those with data but those with a data engine: iterated data aquisition, re-training, evaluation, deployment, telemetry. And whoever can spin it fastest. Slide from Tesla to ~illustrate but concept is general”
4. Data Augmentation: Synthetically increase the size and diversity of training data to improve model generalization and reduce overfitting.
DoorDash varied sentence order and randomly removed information such as menu category: https://doordash.engineering/2020/08/28/overcome-the-cold-start-problem-in-menu-item-tagging/
Companies with large digital catalogs often have lots of free text data about their items, but very few actual labels, making it difficult to analyze the data and develop new features. Building a system that can support machine learning (ML)-powered search and discovery features while simultaneously being interpretable enough for business users to develop curated ...
3. Human-in-the-loop: Collecting labeled data from users, annotation services, or domain experts.
Stack Exchange lets users flag posts as spam, and LinkedIn lets users report messages as harassment: https://engineering.linkedin.com/blog/2020/fighting-harassment
Recently, LLMs are applied too: https://twitter.com/eugeneyan/status/1640530851489259522
2. Reframing: Redefine the original problem, target, or input to make the problem easier to solve.
Sequential recsys reframed the paradigm from co-occurrence (matrix factorization) to predict-the-next-event (e.g., transformers).
Alibaba's BST: https://arxiv.org/abs/1905.06874
Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final recommendations. However, most of these works just concatenate different features, ignoring the sequential nature of users' behaviors. In this paper, we propose to use the powerful Transformer model to capture the sequential signals underlying users' behavior sequences for recommendation in Alibaba. Experimental results demonstrate the superiority of the proposed model, which is then deployed online at Taobao and obtain significant improvements in online Click-Through-Rate (CTR) comparing to two baselines.
