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.
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.
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.
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.
MLOps with Dataiku - Considerations for Model Deployment and Monitoring
Explore key MLOps concepts, deployment readiness, model monitoring strategies, and how Dataiku streamlines MLOps processes for efficient AI industrialization.
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.
Cloud Platform Observability: Streaming Logs from AWS Firehose to Dynatrace
Integrate AWS Firehose logs with Dynatrace Grail for AI-powered observability. Learn setup, analysis, and multi-cloud log management to mitigate issues and enhance security.
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.
Boundless Analytics Powered by DQL and Grail - Live Demo and Production Insights
Explore Grail and DQL's advanced capabilities for data analysis, including schema-on-read, real-time parsing, and joining diverse data types to streamline operations.
Building Self-Service Power Dashboards with RUM, Grail, and External Data - Dynatrace Tips and Tricks
Learn to create dynamic self-service dashboards combining Dynatrace RUM, Grail, and external data. Master dashboard structuring, data querying, and variable usage for effective visualization.
MLOps with Orchestration: Accelerating Deployment of AI - Workshop Sessions
Accelerate AI deployment with MLOps orchestration. Learn to integrate tools, streamline processes, and handle complex use cases for efficient ML application development and continuous delivery in production environments.
How Not to Let Your Data and Model Drift Away Silently
Discover strategies to prevent data and model drift, ensuring ML models maintain performance and value over time in production environments.
From Concept to Production: Building a Production-Ready ML Pipeline - Part 2
Develop a production-ready ML pipeline from concept to deployment using Python, Kubeflow, and Google Cloud Platform. Master data prep, model training, and orchestration techniques.
Multi-armed Bandits in Production at Stitch Fix
Explore scaling challenges and solutions for multi-armed bandits in online experimentation, including a new method for efficient, deterministic Thompson sampling at Stitch Fix.
5 Governance Capabilities You Need in MLOps - Workshop Session
Explore 5 essential MLOps governance capabilities to effectively implement and control machine learning models in production environments. Gain insights on assessing organizational maturity and achieving full MLOps maturity.
Challenges for ML Operations in a Fast Growing Company
Explore ML operations challenges in rapidly growing companies. Learn strategies for scalable platforms, efficient model training, and balancing distributed vs. non-distributed applications. Gain insights on developer ergonomics and best practices.
Machine Learning on Dynamic Graphs - Temporal Graph Networks
Explore Temporal Graph Networks for dynamic graph analysis, unlocking insights in evolving systems like social networks and financial transactions.