Building and Deploying Machine Learning Models with Amazon SageMaker
Learn to build, train, and deploy ML models using Amazon SageMaker, covering data preprocessing, model selection, hyperparameter tuning, and evaluation.
Statistical Analysis in Cosmology: Goodness-of-Fit and Model Selection - Part 2
Explore advanced statistical methods in cosmology, including model selection, MCMC, chi-square, and p-value analysis for robust data interpretation.
AI App of the Week: OpenAI-o1 Investment App with Chain Prompting - Tutorial
Build an AI-powered investment app using chain prompting, frontend design, backend development, and image generation. Learn to integrate OpenAI models for financial analysis.
Understanding LLM Benchmark Quality - Who Watches the Watchmen?
Explore LLM benchmarks' effectiveness in measuring model performance. Gain insights into evaluating language models beyond leaderboards for informed decision-making in AI applications.
Toward Building a Cost-Effective and Robust Machine Learning System
Explore cost-effective ML system development through active learning, continual learning, and transfer learning. Learn efficient model selection techniques for optimal source model transfer to target tasks.
All AI Models Are Not Created Equal: and That's a Good Thing
Explore AI model diversity, ecosystem competition, and optimization strategies for effective implementation across projects. Learn to select models based on price, speed, and quality.
Optimizing Large Language Model Selection for Efficient GenAI Development
Discover practical tools and strategies for selecting optimal LLMs in GenAI development, exploring DSPy and frugalGPT frameworks for cost-effective and efficient implementation.
Predictive Distribution and Model Comparison in Linear Models - Lecture 12
Dive into predictive distribution modeling, exploring linear models, binary classification, and logistic regression while mastering maximum molecular estimation techniques.
Prompt Engineering: Art, Science, or Career Path?
Dive into prompt engineering fundamentals, from tokenization and model selection to advanced techniques, tools, and future trends in AI interaction and development.
Incremental GPU Slicing in Kubernetes Clusters - Dynamic Resource Management
Discover how to implement dynamic GPU slicing in Kubernetes clusters for efficient resource allocation and cost optimization in large language model deployments, using vendor-agnostic approaches.
Pseudo-Labeling for Covariate Shift Adaptation in Machine Learning
Explore advanced covariate shift adaptation techniques using pseudo-labeling methods for regression tasks, focusing on kernel ridge regression and bias-variance optimization for target distributions.