GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.
github.com
GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.
ata Collection Experimentation Evaluation and Deployment Monitoring and Response Metadata Data catalogs, Amundsen, AWS Glue, Hive metas-tores Weights & Biases, MLFlow, train/test set parameter configs, A/B test tracking tools Dashboards, SQL, metric functions and window sizes Unit Data cleaning tools Tensorflow, ML-lib, PyTorch, Scikit-learn, X... See more
Shreya Shankar • "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning.
Throughout this post, I’ve shared patterns I’ve observed across dozens of AI implementations. The most successful teams aren’t the ones with the most sophisticated tools or the most advanced models – they’re the ones that master the fundamentals of measurement, iteration, and learning.
The core principles are surprisingly simple:
The core principles are surprisingly simple:
- Look at your data.
Hamel Husain • A Field Guide to Rapidly Improving AI Products – Hamel’s Blog
Reverse Engineering Success
One of the most valuable aspects of template creation is the ability to reverse-engineer high-performing content.
Start by identifying successful content pieces—social posts, blog intros, or email sequences—that resonate with your target audience. Analyze these pieces’ style, structure, and tone, and look for common eleme
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