Tip 6: Focus on Recent Technologies Mention your proficiency with LLMs, reinforcement learning, or other generative AI technologies. Highlight any recent work or projects involving these technologies.
MACHINE LEARNING
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Production Deployment Skills for ML Engineers
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Tip 4: Emphasize Deployment Experience Highlight your experience with deploying models into production environments using tools like Docker, Kubernetes, or cloud platforms such as AWS, GCP, and Azure. Include specific examples and the impact they had.
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End-to-End AI Project Lifecycle: From Concept to Deployment
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Tip 1: Showcase End-to-End Project Work Describe projects where you took an idea from concept to deployment. Outline the problem, data collection, model development, validation, and deployment. Demonstrate your ability to handle the entire lifecycle of an AI project.
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7 Resume Tips to Stand Out for Machine Learning Roles
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I’ve reviewed 2000+ resumes for machine learning roles in the last 5 years. Here are 7 tips to make your resume stand out:
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Awesome Post on Devising ML Metrics by Dan
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Just read, this is an awesome post Dan! (Originally thought it was just an image lol, here is the post: https://
safe.ai/blog/devising-
ml-metrics
…) -
LLMs Understanding World vs N-gram Models: Insufficient Argument
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"LLMs understand the world because that's needed to predict the next word" is an insufficient argument: you could say the same about n-gram models.
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Programmatic Data Annotation for Model Alignment and Curation
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Instead of manually doing thumbs up and thumbs down, we can programmatically annotate what makes something acceptable vs. not, and that data can be used to align a model or curate data for fine-tuning. More details in the Snorkel Blog:
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Backpropagation: The Most Reinvented Algorithm in History
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Backpropagation is the most reinvented algorithm in history.
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Seven Sins of Language Model Evaluation: What Makes Evals Successful
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New blog post where I discuss what makes an language model evaluation successful, and the "seven sins" that make hinder an eval from gaining traction in the community: https://
jasonwei.net/blog/evals Had fun presenting this at Stanford's NLP Seminar yesterday! -
Transformer blocks progressively refine information through attention mechanism
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(More likely though, each block refines the information over time in the Transformer forward pass, enriching it with the information gathered from previous tokens during Attention.)