Machine Learning Data Pre-processing and Data Wrangling Using Python
Master Python data preprocessing techniques for machine learning, including handling missing values, encoding, scaling, outlier detection, and data transformation using popular libraries.
Data Mining for Beginners - Learn Data Mining
This video teaches Data Mining and its key functions and concepts with a variety of demonstrations & examples to help you get started on the right foot.
Curation and Analysis of Annotated Medical Images Across Institutions - Xiaoyuan Guo
Explore techniques for curating and analyzing annotated medical images across institutions, focusing on data sharing challenges, anomaly detection, and OOD-aware image retrieval for improved dataset quality and future analysis.
Stanford Seminar - Virtual & Mixed Reality for Security of Critical City-Scale Cyber-Physical Systems
Explore VVateR, a virtual 3D world for visualizing cyber-physical systems and attacks on critical infrastructure. Learn about its applications in research, training, and security enhancement.
Stanford Seminar - The Altruistic Entrepreneur
Damien Patton discusses how Banjo uses AI to analyze live data signals, delivering real-time information to aid agencies and media, emphasizing technology's potential for social good and ethical decision-making in entrepreneurship.
Autoencoder Architecture and Applications - Lecture 10
Explore autoencoders: architecture, comparison, applications in image retrieval, denoising, and anomaly detection. Gain insights into this powerful neural network technique.
Realtime Streaming for Anomaly Detection - An End-to-End Data Engineering Project
Build a real-time anomaly detection system using streaming data engineering techniques. Learn end-to-end implementation for monitoring and identifying unusual patterns in data streams.
Production Machine Learning Monitoring: Outliers, Drift, Explainers and Statistical Performance
Dive deep into advanced ML model monitoring techniques, including outlier detection, concept drift, explainability, and performance metrics for robust production deployments.
Anomaly Detection on 5 Pillars of Data Observability with Dynatrace Davis AI
Explore Dynatrace Davis AI's anomaly detection for data observability pillars: freshness, distribution, volume, schema, and lineage. Learn automation techniques for alerting and monitoring data integrity.
Elevating Security with Dynatrace Davis Anomaly Detection
Explore Davis AI's anomaly detection strategies for enhanced security. Learn to apply static, auto-adaptive, and seasonal baselining to efficiently detect threats and analyze abnormal authentication requests.
What's New in Dynatrace - Release 1.291
Explore Dynatrace v1.291 updates, including Red Hat automation, enhanced privacy controls, DQL improvements, Grail-based anomaly detection, and Amazon SNS monitoring enhancements.
Hands-On Notifications and Alerting in Dynatrace - Practical Guide
Explore notifications and alerting in Dynatrace with hands-on examples. Learn to set up alerting profiles, configure problem notifications, integrate email alerts, and manage maintenance windows.
Introduction to Anomaly Detection Based on DQL
Explore Dynatrace's Davis AI Analyzer for advanced anomaly detection in high-cardinality data. Learn to reduce alert noise, avoid fatigue, and leverage AI across various contexts through a comprehensive demo.
Getting Started with Dynatrace Terraform Provider for Observability as Code
Explore Dynatrace Terraform Provider for automated observability management. Learn best practices, tips, and see live demos on SLOs, dashboards, and synthetic tests across multiple environments.
Dynatrace Business Events: Capture, Parse, Retention, and Metrics
Explore Dynatrace Business Events ingest process, from capturing to metrics extraction. Learn about event processing, anomaly alerting, and data retention through live demonstrations.
Databases App: DBA Insights Across All Databases - Dynatrace App Spotlight
Explore Dynatrace's new Databases app, offering DBA insights across all databases. Learn about integration, database instance insights, behavior analysis, query performance, and AI-detected anomaly reactions.
Machine Learning, AI and Security - Efficient Security with AI
Explore the intersection of machine learning, AI, and security for efficient cybersecurity practices in cloud environments.
5 Ways Cybersecurity Leaders Can Leverage GenAI in 2024
Discover how to leverage GenAI for enhanced cybersecurity, including team augmentation, anomaly detection, and accelerated insights, while maintaining data security and privacy.
Deploying and Managing Machine Learning Models at Scale - A Hands-On Workshop
Deploy and manage ML models at scale. Hands-on workshop covers creation, deployment, monitoring, and management using real-life scenarios. Gain practical experience with Seldon Deploy, explainers, and outlier detection.
Defending Against Decision Degradation with Full Spectrum Model Monitoring - Case Study and AMA
Explore Lyft's comprehensive ML model monitoring system, preventing degradation in high-stakes decisions for pricing, safety, and fraud detection. Learn about real-time validation and cultural shifts in ML practices.
AIOps in AWS - Best Approach for Predictive Maintenance
Explore AIOps implementation in AWS for predictive maintenance, covering CloudWatch capabilities, data lake approaches, and DevOps Guru for enhanced cloud operations and incident management.