End to end AI Workflows 102

In this episode, we conclude key topics from our previous session and dive into understanding technological platforms at both the architectural and algorithmic levels. We'll explore pipeline configurations used in these environments and the differences between static and dynamic design constructs.

Our expert Ramsey, demonstrates the practical applications of these pipelines and showcase how to set up and manage them using various tools like Python libraries, Presto, and cloud services like Azure and Google Cloud's Vertex AI.

Learn how to efficiently configure, deploy, and manage models, whether you’re working on native cloud environments or on-premise servers. This session is packed with insights and practical examples to enhance your understanding of AI pipeline management and deployment.

00:12 Technological Platform and Algorithm Pipelines
00:32 Architectural Configurations and Pipelines
01:28 Real-Time Interactions and Design Constructs
03:00 Network Configuration and Runtime Environment
03:53 Cloud Environment and Machine Learning Pipelines
04:42 Open API Standards and Ecosystem Integration
06:33 Generative Models and Real-Time Execution
08:05 Production Environment and Scaling
09:30 Azure and Cloud-Based Configurations
13:25 Data Ingestion and Enrichment
13:52 Model Serving and Runtime Capabilities
19:38 Pipeline Configuration and Deployment
33:16 Multi-Model Recommender and Pipeline Automation