The Rapid Evolution of the Canonical Stack for Machine Learning
Explore the evolution of AI/ML tech stacks and the emergence of a canonical stack for machine learning, democratizing AI for companies of all sizes.
MLOps at Acerta - Automating the Machine Learning Lifecycle for Manufacturing
Automate the machine learning lifecycle for manufacturing, enhancing efficiency, scalability, and reproducibility. Learn to build a robust MLOps platform for production-grade systems.
Iterative Development Workflows for Building AI Applications
Explore iterative development workflows for AI applications using Snorkel framework. Learn guided error analysis, quality improvement techniques, and collaboration with domain experts.
MLOps with PyCaret - Practical Demo for Data Science Workflows
Supercharge your data science team's productivity with PyCaret, an open-source ML library for efficient model preparation and deployment in Python.
Shopping Recommendations at Pinterest
Explore Pinterest's shopping recommendation systems, including engagement-based and embedding-based candidate generation, indexing methods, and deep neural network ranking models.
Machine Learning Tools: Skyline and RL-Scope for Performance Optimization
Explore Skyline and RL-Scope, two innovative tools for optimizing machine learning performance. Learn about interactive profiling, visualization, and debugging for PyTorch networks and cross-stack profiling for deep reinforcement learning.
Creating Machine Learning Retraining Pipelines with Kubeflow and Kubernetes
Develop ML retraining pipelines using Kubernetes and Kubeflow. Master advanced techniques for efficient model updates and deployment in production environments.
How to Iterate and Productionize Machine Learning Models with Speed
Explore strategies to overcome deployment challenges and accelerate the productionization of machine learning models for faster impact.
Machine Learning Monitoring in Production - Lessons Learned from 30+ Use Cases
Discover practical strategies for effective machine learning monitoring in production, drawing insights from over 30 real-world use cases.
Build High Performance MLOps With ML Monitoring and AI Explainability
Discover techniques for enhancing MLOps through ML monitoring and AI explainability. Learn to identify model failures, address operational issues, and streamline model deployment using advanced analytics.
The Ops in MLOps - Cost Predictive, Security Compliant, and Data Governance Techniques
Explore techniques for creating ML environments with cost predictability, security compliance, and proper data governance. Focus on the often-overlooked Ops components of MLOps.
MLSecOps and Shift-Left Security in Machine Learning Model Lifecycle
Learn to integrate security into machine learning model lifecycles using MLSecOps. Explore shift-left security practices, address vulnerabilities early, and implement effective risk management in ML projects.
From Concept to Production: Template for the Entire ML Journey - Part 1
Develop a production-ready ML pipeline from concept to deployment using Python. Gain hands-on experience with data prep, model training, and deployment on Google Cloud Platform.
Good, Fast, Cheap - How to Do Data Science with Missing Data
Explore strategies for handling missing data in data science projects, including visualization, identification, and practical implementation techniques for effective workflow integration.
Unlock the Power of MLFlow with Multi-Cloud Compute Infrastructure
Harness MLFlow's potential using multi-cloud compute infrastructure. Learn integration techniques, architectural considerations, and scaling strategies for effective ML training and deployment across clouds.
Essential Workshop to Exploratory Data Analysis and Feature Engineering
Explore and preprocess data, analyze correlations and distributions, engineer features, and train ML models using a Google Play Store dataset. Improve model accuracy and efficiency.
What's New in Dynatrace - Release 1.290
Explore Dynatrace v1.290 updates: DQL enhancements, platform dashboard improvements, .NET support, new Vulnerabilities app, and K8s monitoring advancements for cloud-native full-stack mode.
What's New in Dynatrace - Release 1.289
Explore Dynatrace v1.289 updates: breaking changes, platform enhancements, AppSec features, infrastructure improvements, and application observability advancements through live demos and detailed explanations.
How to Effectively Upgrade from Dynatrace Managed to SaaS
Learn to upgrade from Dynatrace Managed to SaaS using the SaaS Upgrade Assistant and Remote Configuration Management. Explore configuration export, import, and OneAgent migration in a live demo.
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.
AI-Driven Analytics and Automation for Unified Observability and Security
Unify observability, security, and business data with AI-powered insights and automation for cloud-native and multi-cloud environments. Explore platform features through a live demo.