| 1 - 12-Factor Principles.mp4 | 119.6 MB | ||
| 1 - Getting Started with Vertex AI (GCP).mp4 | 47.7 MB | ||
| 1 - Introduction to Generative AI Systems.mp4 | 70.1 MB | ||
| 1 - Keras Model Building (Sequential & Functional).mp4 | 15.6 MB | ||
| 1 - Object Storage Patterns (S3, Blob, GCS).mp4 | 12.2 MB | ||
| 2 - Containers & Image Packaging (Docker Essentials).mp4 | 49 MB | ||
| 2 - ETLELT Pipelines (Glue, Data Factory, Dataflow).mp4 | 12.2 MB | ||
| 2 - Exploring Azure AI Foundry and Studio.mp4 | 35.5 MB | ||
| 2 - Inside Large Language Models (LLMs).mp4 | 59.2 MB | ||
| 2 - Training, Evaluation, and Checkpointing.mp4 | 20.4 MB | ||
| 3 - From Docker Compose to Kubernetes.mp4 | 51.2 MB | ||
| 3 - Introduction to Amazon Bedrock & SageMaker.mp4 | 16.8 MB | ||
| 3 - The Strengths and Limitations of LLMs.mp4 | 61.2 MB | ||
| 3 - Warehousing Fundamentals (BigQuery, Redshift, Synapse).mp4 | 10.4 MB | ||
| 4 - Cost, Security, and Governance for AI Systems.mp4 | 15.5 MB | ||
| 4 - Families of Modern Language Models.mp4 | 44.8 MB | ||
| 4 - Kubernetes Core Objects (Pods, Deployments, Services).mp4 | 11.1 MB | ||
| 5 - How Foundation Models Are Built and Trained.mp4 | 31 MB | ||
| 5 - Infrastructure as Code with Terraform (Foundations).mp4 | 21.6 MB | ||
| 6 - CICD for Cloud-Native Apps.mp4 | 56.1 MB | ||
| 6 - How Foundation models Work.mp4 | 40.5 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 23 total files | |||
Foundations of Cloud AI: Understanding Modern AI Platforms
https://WebToolTip.com
Published 10/2025
Created by Ing.Seif | Europe Innovation
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 21 Lectures ( 1h 58m ) | Size: 801 MB
Generative AI, data pipelines, and model lifecycle concepts across major cloud platforms
What you'll learn
how Generative AI and Large Language Models integrate within cloud ecosystems.
Differentiate between major cloud AI platforms
Understand the role of data pipelines, object storage, and warehousing in AI infrastructure.
escribe key concepts in model lifecycle management : training, evaluation, and export.
Requirements
No technical or programming background required. A general understanding of cloud computing is helpful but not mandatory.
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