Machine Learning with DataRobot AI Cloud Platform
Comprehensive tutorial on DataRobot AI Cloud Platform, covering machine learning model building, evaluation, and deployment. Explores key features like AutoML, bias detection, and model optimization.
Predict Housing Prices in Austin TX with Tidymodels and XGBoost
Explore feature engineering for boosted tree models using Austin housing data. Learn to incorporate text information as indicator variables and tune models for accurate price predictions.
Partial Dependence Plots for Mario Kart World Records
Explore decision tree modeling and partial dependence profiles for Mario Kart world records using tidymodels and DALEX, with a focus on predicting shortcut usage.
EvalML AutoML Library to Automate Feature Engineering, Feature Selection, Model Creation and Tuning
Explore EvalML, an AutoML library automating feature engineering, selection, model creation, and tuning. Learn to streamline your data science workflow and improve project efficiency.
Building Our First Model - Introduction to Text Analytics with R Part 4
Learn to build and evaluate a text classification model using R, covering tokenization, cross-validation, parallel processing, and decision tree training for optimal accuracy in text analytics.
Tuning Gemma for a Spoken Language AI Assistant - Build with Google AI
Explore creating a multilingual AI assistant using Gemma. Learn to tune models for specific tasks and languages, with insights from developers and practical extension tips.
A Visual Introduction to Modern and Scalable Machine Learning Pipeline
Visual guide to modern, scalable machine learning pipelines: from data prep to deployment. Covers key stages like training, validation, inference, feature stores, and model registry. Ideal for understanding ML infrastructure evolution.
Feature Engineering & Interpretability for XGBoost with Board Game Ratings
Explore advanced modeling with board game ratings data: feature engineering, XGBoost tuning, and explainability methods for data-driven insights and predictive analysis.
Tune and Evaluate a Multiclass Lasso Model for NBER Working Papers
Explore multiclass lasso modeling for economic research papers, covering data analysis, feature engineering, model tuning, and evaluation using #TidyTuesday data.
Tune and Evaluate a Decision Tree Model with Scooby Doo Episodes
Learn to predict Scooby Doo monster status using decision trees. Explore model tuning, visualization, and simplification techniques with Julia Silge's practical demonstration.
An Improved and Functional Design of End-to-End Machine Learning Life Cycle
Comprehensive overview of the machine learning lifecycle, covering data preparation, model training, deployment, and inference, with practical insights for effective implementation and monitoring.
Introducing MLflow for End-to-End Machine Learning on Databricks
Explore end-to-end machine learning with MLflow on Databricks: from data engineering to model deployment, using health data to predict life expectancy through Spark, hyperopt, and interactive dashboards.
Automated Machine Learning and Explainability
Explore Oracle AutoML's innovative approach to simplifying machine learning, integrating explainability techniques for enhanced user understanding and trust in complex ML pipelines and models.
Data Science for Business - Repeatable and Objective Data Science Process
Explore the Team Data Science Process (TDSP) for efficient predictive analytics and AI integration in business processes, focusing on agile methodology and practical implementation strategies.
An Introduction to Apache TVM
Explore Apache TVM's Python API for deep learning model compilation. Learn installation, features, and practical applications for CPUs and microcontrollers through hands-on demos and examples.
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.
Scaling Language Model Tuning with Snorkel AI - A Case Study
Explore Snorkel AI's innovative approach to scaling language model tuning using programmatic labeling and guiding models for efficient data curation and improved performance.
Introduction to Generative AI Studio - Prototyping and Customizing Models on Vertex AI
Prototype and customize generative AI models using Vertex AI's Generative AI Studio. Learn about language, vision, speech, prompt design, and model tuning.
MLOps vs ModelOps - What's the Difference and Why You Should Care
Explore the differences between MLOps and ModelOps, focusing on continuous monitoring, automated remediation, and feedback loops for AI and analytical models.
Deep Dive on How Google Cloud Built Duet AI
Explore Google Cloud's process for building Duet AI, including model selection, tuning, middleware, guardrails, and evaluation techniques for generative AI products.
Tuning Foundation Models: A Comprehensive Guide
Explore techniques for customizing foundation models with your data, improving performance across tasks while understanding limitations and future prospects.