Udemy - Healthcare AI Engineering - 100 Labs HL7 FHIR and Kubernetes torrent

Healthcare AI Engineering: 100 Labs | HL7 FHIR & Kubernetes
https://WebToolTip.com
Published 8/2026

Created by Dar Al Taqniya

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Level: All Levels | Genre: eLearning | Language: English | Duration: 112 Lectures ( 19h 49m ) | Size: 987.6 MB
From isolated coding to production-grade Healthcare AI, FHIR, Telemedicine & Kubernetes engineering.
What you'll learn

⚡ Architect enterprise-grade HL7 FHIR healthcare platforms from the ground up using entirely open-source technologies.

⚡ Deploy secure, encrypted telemedicine infrastructure with WebRTC, TURN/STUN, TLS, OAuth2, JWT, and API gateways.

⚡ Design real-time healthcare data pipelines using Kafka, PostgreSQL, InfluxDB, Grafana, and Python.

⚡ Build AI-powered medical inference services using FastAPI, PyTorch, ONNX Runtime, and containerized microservices.

⚡ Implement production-level security including Keycloak, Vault, SMART on FHIR, Cilium, Trivy, immutable audit logging, and disaster recovery.

⚡ Master healthcare observability through Prometheus, Grafana, Loki, OpenTelemetry, distributed tracing, and SRE practices.

⚡ Engineer resilient IoT healthcare systems using MQTT, ESP32, Kubernetes Edge (K3s), secure OTA updates, and offline synchronization.

⚡ Automate Kubernetes deployments using GitOps, ArgoCD, Helm, cert-manager, ExternalDNS, and production CI/CD principles.

⚡ Validate healthcare systems against interoperability, security, compliance, resilience, and performance requirements used by modern enterprises.

⚡ Complete a production-scale Autonomous Tele-Diagnostic Mesh integrating Healthcare AI, Telemedicine, Edge Computing, Kubernetes, Security, and FHIR interoperabi
Requirements

❗ Students should have

❗ 1. Basic computer literacy

❗ 2. Basic understanding of networking concepts is helpful but not required

❗ 3. Basic familiarity with Linux command line is recommended

❗ 4. Curiosity to build production systems

❗ Software

❗ 1. Ubuntu Linux 24.04 LTS (recommended)

❗ 2. Python 3.12+

❗ 3. Docker Engine

❗ 4. Kubernetes (kubeadm or K3s during later labs)

❗ 5. Visual Studio Code

❗ Minimum Hardware Requirement

❗ 1. Quad-Core CPU

❗ 2. 16 GB RAM

❗ 3. 100 GB free SSD storage