Strategies for Efficient LLM Deployments in Any Cluster
Explore strategies to optimize LLM deployments in Kubernetes clusters, focusing on reducing model size, improving resource utilization, and balancing performance with efficiency for cloud-to-edge scenarios.
Data is as Data Does - The Influence of Computation on Inference
Exploring the impact of computational approximations on probabilistic models in machine learning, with focus on Gaussian Process approximations and their applications in neurobiology.
Building LLM-Powered Apps: Best Practices for Vector Databases and LLMOps
Explore best practices for LLM-based app development, covering design considerations, technical aspects, and deployment strategies for optimal performance and scalability.
Towards Good Validation Metrics for Generative Models in Offline Model-Based Optimization
Explore principled evaluation frameworks for generative models in offline model-based optimization. Learn about validation metrics, dataset challenges, and strategies for measuring extrapolation capabilities.
How to Get Started with MLOps in Azure Databricks
Explore the machine learning lifecycle and operationalize models using Databricks Machine Learning, focusing on scaling deployments with MLOps for company-wide implementation.
Scalable AutoML for Time Series Forecasting Using Ray
Explore scalable AutoML for time series forecasting using Ray, covering feature generation, model selection, and distributed hyper-parameter tuning with real-world applications and insights.
Protecting the Protector - Hardening Machine Learning Defenses Against Adversarial Attacks
Strategies for enhancing machine learning model resilience against tampering, comparing cloud and client-based models, and exploring defense techniques for endpoint protection systems.
A Composer's Guide to Creating with Generative Neural Networks
Exploring the challenges and creative potential of using generative neural networks in music composition, from data collection to model selection and ethical considerations.
Machine Learning Made Easy With PyCaret
Discover how PyCaret simplifies machine learning workflows, automates processes, and boosts productivity. Learn to integrate this low-code library for faster, more efficient ML experiments.
Building a Large Music Recommender Leveraging AI, Deep Learning and Human Expertise
Explore Pandora's sophisticated music recommendation system, combining AI, deep learning, and human expertise to deliver personalized listening experiences based on vast user data and musicological insights.
Honey Bee Conservation Using Deep Learning
Explore DeepBee: an innovative tool using deep learning for automatic honeybee colony assessment, streamlining the process of counting and classifying comb cells for beekeepers.
Impact on Business and Society
Exploring AI democratization at Salesforce, focusing on reducing human effort in service centers through automated model building, feature engineering, and actionable results using scalable technologies.
ProxyLM - Predicting Language Model Performance on Multilingual Tasks via Proxy Models
Discover how ProxyLM framework uses proxy models to predict language model performance in multilingual NLP tasks, offering significant computational efficiency and improved accuracy for model selection.
Boosting Data Science Routines with DataArt's AutoML Platform
Discover how DataArt's DS/ML Platform automates data science workflows through feature engineering, model selection, and deployment - suitable for both expert and citizen data scientists.
Rapid Prototyping with Open Source AI Models
Discover and leverage open-source AI models for idea validation, process automation, and innovation without expensive hardware requirements.
VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models
Explore interactive visualization for comparing and selecting predictive models, integrating performance, instance, and feature-level analysis to support informed decision-making in machine learning.
Selecting the Right Large Language Model - A Systematic Approach
Systematic framework for experimenting with and selecting the optimal LLM, considering factors like performance, cost, and specific use case requirements.
Vertex AI Gemini: Model Selection and Prompt Design Principles and Strategies
Master Vertex AI Gemini model selection and prompt design. Learn to map capabilities to use cases, craft effective prompts, and implement robust engineering strategies.
Quality Assurance and Machine Learning Model Validation
Explore key steps in machine learning model validation and quality assurance, from bias detection to data leak prevention, enhancing your MLOps process.
Supervised Learning: The Setup and Hypothesis Space - Lecture 3
Delve into supervised learning concepts, focusing on hypothesis space selection and its crucial role in machine learning model development and performance optimization.
Trade-off Between Optimality and Explainability in Machine Learning Models
Explore the trade-off between model explainability and accuracy in AI/ML, focusing on counterfactual explainability and optimization challenges in high-risk applications.