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.
Building Your ML Ops Strategy for Generative AI
Adapt ML Ops for generative AI with tuning pipelines, model management, evaluation, and safety filters. Learn to operationalize and customize models effectively.
Managing Machine Learning Projects - From Design to Deployment
Explore ML project management strategies, from setup to production integration. Learn to navigate challenges, assess models, and align with business processes for successful implementation.
MLOps vs ML Orchestration - Exploring Challenges and Community-Driven Development
Explore MLOps vs ML Orchestration with Ketan Umare, discussing Union.AI, Flyte, community-driven development, real-time data handling, and democratizing machine learning for broader accessibility and efficiency.
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.
The Materials Simulation Toolkit for Machine Learning (MAST-ML): Automating Development and Evaluation of Machine Learning Models for Materials Property Prediction
Automate machine learning model development for materials property prediction using MAST-ML. Learn data import, feature engineering, model evaluation, and uncertainty quantification through hands-on activities.
Evaluating Large-Scale Learning Systems
Explore challenges and solutions in evaluating large-scale machine learning systems, including federated networks and large language models. Gain insights into reliable assessment methods.
Fine-tuning LLMs to Reduce Hallucination - Leveraging Out-of-Domain Data
Explore techniques to enhance LLM factual consistency using out-of-domain data and two-step fine-tuning. Learn to leverage Weights & Biases for model evaluation and improve hallucination detection.
Challenges in Making Large Language Models Safe and Robust
Explore root causes of safety concerns and attacks on LLMs, evaluate defense strategies, and examine broader implications for machine learning safety and robustness.
Predicting Classifier Accuracy in the Wild - Lecture 39
Dive into advanced machine learning concepts with Prof. Sebastiani as he explores methods for predicting classifier accuracy in real-world applications and deployment scenarios.
Hierarchical Prompting Taxonomy - A Universal Evaluation Framework for Large Language Models
Explore a universal evaluation framework for large language models using Hierarchical Prompting Taxonomy. Gain insights into assessing dataset complexity and model capabilities.
Aligning LLM-Assisted Evaluation with Human Preferences
Explore the critical relationship between LLM-assisted evaluation methods and human preferences in validating AI outputs, examining key alignment challenges and solutions.
Evaluating Unlearning and Memorization via Compression
Explore cutting-edge research on machine learning compression techniques, focusing on model unlearning and memorization evaluation methods presented by CMU researcher Zhili Feng.
Mastering Confusion Matrix in Machine Learning - From Confusion to Clarity
Master the confusion matrix to evaluate and enhance machine learning model performance through accuracy, precision, and recall metrics for better classification results.
Choosing the Right LLM - A Framework for Model Evaluation and Selection
Discover a systematic approach to evaluating and selecting Large Language Models for specific tasks, focusing on practical frameworks and custom testing methods for optimal results.
MLOps with MLFlow: Evaluating and Quantizing Fine-Tuned LLM Models
Discover how to evaluate fine-tuned LLMs using MLflow, convert models to ONNX format, and implement INT8 quantization for optimized performance and deployment.
Fine-tuning AI Models for Healthcare Policy Analysis with AWS and Snorkel Flow
Master AWS and Snorkel Flow integration to enhance AI models through structured fine-tuning workflows, focusing on improving accuracy in analyzing health insurance policy documents and generating reliable responses.
Evaluating World Models in Generative AI - A Critique of AI Intelligence Assessment
Explore groundbreaking research from top universities challenging AI evaluation methods, revealing limitations in language models' structural understanding and proposing new assessment frameworks.
VIME - Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning
Explore an interactive visual tool for analyzing machine learning model capabilities and limitations through a systematic exploration interface.
Evaluating LLM Responses with MLflow
Master MLflow's evaluation features to assess and analyze Large Language Model responses, enhancing your ability to measure and improve AI model performance.