Introduction to Text Analytics with R - Model Metrics
Explore advanced model evaluation techniques, including sensitivity and specificity, feature engineering, and improving model performance beyond accuracy in text analytics using R.
Feature Engineering for Web Bot Detection - Feature Engineering Tutorial
Learn to detect automated web traffic through feature engineering, exploring bot types, their impact on businesses, and extracting useful features for effective bot detection.
Building Robust Machine Learning Models
Explore the fundamentals of robust machine learning, focusing on data quality, feature engineering, and model evaluation rather than specific tools or trendy techniques.
Deep Learning and Software Engineering - A Retrospective and Paths Forward
Explore deep learning's impact on software engineering, including code suggestion and program repair. Discover current research, challenges, and future directions in this emerging field.
Effective Transfer Learning for NLP - Madison May - ODSC East 2018
Explore effective transfer learning techniques for NLP, including parameter-efficient methods using sequence representations. Learn practical improvements for real-world tasks and discover the Enso library for benchmarking.
Technical Overview of Machine Learning Life Cycle
Comprehensive technical overview of the machine learning lifecycle, covering data preparation, model training, deployment, and inference. Ideal for AI engineers seeking in-depth understanding of ML processes.
Build Your Machine Learning Pipeline in These 4 Simple Steps - Loan Prediction Scikit-Learn Example
Aprenda a construir um pipeline completo de machine learning usando scikit-learn para previsão de empréstimos, desde a ingestão de dados até o uso do modelo salvo para previsões.
Dataset Management for Computer Vision - Important Component to Delivering Computer Vision Solutions
Explore dataset management's crucial role in computer vision solutions. Learn to align business goals, express assumptions, and maintain datasets for successful real-world implementations.
Scalable AutoML for Time Series Forecasting Using Ray
Explore scalable AutoML for time series forecasting using Ray, covering feature generation, model selection, and hyper-parameter tuning. Learn from real-world applications and experiences in various industries.
The Killer Feature Store - Orchestrating Spark ML Pipelines and MLflow for Production
Explore feature store implementation as an orchestration engine for ML pipeline stages using Spark and MLflow, focusing on deployment management, A/B testing, discovery, and governance.
Productionizing Machine Learning with Microservices Architecture
Learn to deploy ML models as microservices using Spark, enabling auto-scaling, versioning, and security. Discover how to aggregate real-time and historical data for inferencing and create actionable insights.
Testing ML Models in Production - Detecting Data and Concept Drift
Explore robust ML model testing in production using MLflow, SciPy, and statsmodels. Learn key statistical tests, metrics for drift detection, and gain practical insights for maintaining model effectiveness.
Consolidating MLOps at Schiphol Airport Using MLFlow
Explore how Schiphol Airport leverages MLFlow for rapid iteration, deployment, and monitoring of mission-critical machine learning models, enabling multiple weekly releases and efficient adaptation to changing dynamics.
Cloud-Based Convolution Neural Network Ensembles for Computer Vision Counting
Explore ensemble CNN techniques for computer vision counting, featuring cloud computing evaluation and strategies for achieving low RMSE scores in competitive settings.
Multi-Variate Feature Engineering Presentation for Machine Learning
Learn advanced feature engineering techniques for machine learning, including feature importance, target coding, neural networks, and embedding layers to improve model performance and extrapolation.
End to End ML Project With Deployment - Project Problem Statement, EDA and Model Training
Comprehensive guide to building and deploying a machine learning project, covering problem definition, exploratory data analysis, feature engineering, and model training.
Lessons and Outlook for ML Parameterization of Sub Grid Atmospheric Physics From the Vantage of Emulating Cloud Superparameterization - Mike Pritchard
Exploring machine learning for atmospheric physics parameterization, focusing on cloud superparameterization emulation. Discusses challenges, strategies, and lessons learned in applying ML to climate modeling.
Quick to Production: Integrating Spark and TensorFlow for Efficient MLOps
Learn to quickly deploy deep learning models using Spark and TensorFlow, simplifying MLOps for data scientists. Explore distributed computing, feature engineering, and leveraging TensorFlow ecosystem libraries for efficient model production.
Integrating High Performance Feature Stores with KServe Model Serving
Explore high-performance feature stores integration with KServe for efficient ML model serving, focusing on Feast concepts, KServe challenges, and scalable architectures.
Angel 3.0 - A Full Stack Machine Learning Platform
Explore Angel 3.0, a comprehensive machine learning platform featuring auto feature engineering, hyperparameter tuning, model serving, and PyTorch integration for enhanced graph embedding and GNN algorithms.
Breakdown New Data Silos in Generative AI
Explore strategies to overcome data silos in generative AI, enhancing model performance through improved data access, discovery, and integration across departments and cloud platforms.