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

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Udemy - Healthcare AI Engineering - 100 Labs HL7 FHIR and Kubernetes (Size: 987.5 MB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  1 - Introduction to Healthcare AI Engineering
  1. Introduction.mp4 77.1 MB
  10 - Module 9 Edge Computing and IoT Health Integration
  100. Lab 90 Validating end-to-end edge data integrity checks.html 23.9 KB
  11 - Module 10 Sovereign Deployment and Final Capstone
  101. Sovereign Deployment and Final Capstone.mp4 95.8 MB
  102. Lab 91 Provisioning multi-node Kubernetes clusters via kubeadm.html 25.8 KB
  103. Lab 92 Deploying ArgoCD for GitOps continuous delivery.html 24.7 KB
  104. Lab 93 Configuring ExternalDNS and cert-manager controllers.html 28.2 KB
  105. Lab 94 Assembling the complete microservices helm chart.html 29.8 KB
  106. Lab 95 Executing end-to-end platform smoke test scripts.html 21 KB
  107. Lab 96 Validating high-availability failover mechanisms.html 26.9 KB
  108. Lab 97 Performing load testing via distributed locust workers.html 22.1 KB
  109. Lab 98 Conducting final regulatory compliance checklist audit.html 24.1 KB
  110. Lab 99 Packaging system documentation and runbooks.html 29 KB
  111. Lab 100 Capstone Project Autonomous Tele-Diagnostic Mesh.html 34.7 KB
  12 - Conclusion
  112. Conclusion.mp4 95.8 MB
  2 - Module 1 Foundations and Interoperability Setup
  10. Lab 8 Establishing secure TLS local certificate authorities.html 43.6 KB
  11. Lab 9 Configuring Nginx reverse proxy with SSL termination.html 40.5 KB
  12. Lab 10 Verifying initial end-to-end HTTP secure health checks.html 33 KB
  2. Foundations and Interoperability Setup.mp4 40.6 MB
  3 - Module 2 Medical Data Pipelines and Ingestion
  13. Medical Data Pipelines and Ingestion.mp4 87.8 MB
  14. Lab 11 Designing ingestion workers for HL7 v2 messages.html 57.2 KB
  15. Lab 12 Translating HL7 v2 ADT events into FHIR resources.html 70.4 KB
  16. Lab 13 Setting up Apache Kafka for clinical event streams.html 38.7 KB
  17. Lab 14 Configuring Kafka topics for high-throughput vitals.html 39.8 KB
  18. Lab 15 Writing Python consumers for real-time telemetry.html 39.5 KB
  19. Lab 16 Implementing schema registry checks for payloads.html 48.7 KB
  20. Lab 17 Storing time-series vital signs in InfluxDB.html 50.3 KB
  21. Lab 18 Creating automated downsampling retention policies.html 38.3 KB
  22. Lab 19 Integrating Grafana dashboards for vital metrics.html 48.2 KB
  23. Lab 20 Validating stream pipeline fault tolerance limits.html 52.2 KB
  4 - Module 3 Secure Telemedicine Infrastructure
  24. Secure Telemedicine Infrastructure.mp4 85.6 MB
  25. Lab 21 Architecting WebRTC signaling servers with Node.js.html 56.4 KB
  26. Lab 22 Deploying TURN and STUN servers via Coturn.html 41.7 KB
  27. Lab 23 Establishing encrypted peer-to-peer media paths.html 49.8 KB
  28. Lab 24 Building secure video session management APIs.html 48.9 KB
  29. Lab 25 Implementing JWT role-based access for rooms.html 35.9 KB
  30. Lab 26 Capturing encrypted session audio and video chunks.html 34.1 KB
  31. Lab 27 Integrating Web Audio API for noise suppression.html 48.4 KB
  32. Lab 28 Setting up live text chat channels over WebSockets.html 45.9 KB
  33. Lab 29 Enforcing strict session timeouts and revocation.html 44.7 KB
  34. Lab 30 Stress testing concurrent telemedicine streams.html 38.5 KB
  5 - Module 4 Clinical Decision Support Systems
  35. Clinical Decision Support Systems.mp4 82.9 MB
  36. Lab 31 Establishing rule engines with Drools or Python.html 37 KB
  37. Lab 32 Writing clinical alert logic for abnormal labs.html 41.9 KB
  38. Lab 33 Triggering FHIR Flag resources upon threshold breach.html 39.6 KB
  39. Lab 34 Building asynchronous notification worker queues.html 38.5 KB
  40. Lab 35 Integrating SMTP and Webhook alert dispatchers.html 43.2 KB
  41. Lab 36 Designing physician review dashboard backends.html 55.4 KB
  42. Lab 37 Implementing audit logging for clinical decisions.html 43.1 KB
  43. Lab 38 Securing decision endpoints with OAuth2 scopes.html 50.2 KB
  44. Lab 39 Testing rule engine latency under heavy load.html 50.1 KB
  45. Lab 40 Validating clinical alert accuracy via test suites.html 37.7 KB
  6 - Module 5 AI Diagnostics Pipeline Engineering
  46. AI Diagnostics Pipeline Engineering.mp4 81 MB
  47. Lab 41 Preparing DICOM imaging datasets for processing.html 41.8 KB
  48. Lab 42 Building FastAPI wrappers for PyTorch models.html 44 KB
  49. Lab 43 Containerizing GPU-enabled diagnostic inference apps.html 45.2 KB
  50. Lab 44 Implementing asynchronous job queues with Celery.html 44.8 KB
  51. Lab 45 Managing model weights using versioned storage.html 53.1 KB
  52. Lab 46 Optimizing ONNX runtimes for faster inference.html 45.9 KB
  53. Lab 47 Creating REST endpoints for image classification.html 56.7 KB
  54. Lab 48 Storing diagnostic results as DiagnosticReports.html 41.4 KB
  55. Lab 49 Benchmarking inference latency and memory usage.html 43.7 KB
  56. Lab 50 Verifying output consistency across test batches.html 37.8 KB
  7 - Module 6 Advanced Analytics and Data Governance
  57. Advanced Analytics and Data Governance.mp4 87.2 MB
  58. Lab 51 Deploying Apache Spark for population analytics.html 39.6 KB
  59. Lab 52 Extracting FHIR data into analytical Parquet files.html 57.2 KB
  60. Lab 53 Anonymizing patient identifiers for research queries.html 54.3 KB
  61. Lab 54 Implementing differential privacy algorithms in code.html 36.1 KB
  62. Lab 55 Setting up data lineage tracking with OpenLineage.html 23.4 KB
  63. Lab 56 Building automated compliance validation checks.html 24 KB
  64. Lab 57 Configuring role-based column masking policies.html 23.7 KB
  65. Lab 58 Executing secure multi-tenant analytical queries.html 24.5 KB
  66. Lab 59 Exporting aggregated public health metrics safely.html 31.3 KB
  67. Lab 60 Auditing data access logs for regulatory review.html 23.1 KB
  8 - Module 7 Security, Hardening, and Compliance
  68. Security, Hardening, and Compliance.mp4 95.6 MB
  69. Lab 61 Hardening Linux kernel parameters for security.html 20.5 KB
  70. Lab 62 Implementing Keycloak for unified IAM and SSO.html 22.9 KB
  71. Lab 63 Configuring fine-grained SMART on FHIR scopes.html 26.5 KB
  72. Lab 64 Enforcing network microsegmentation with Cilium.html 33.9 KB
  73. Lab 65 Scanning container images for vulnerabilities via Trivy.html 24 KB
  74. Lab 66 Establishing automated secrets management with Vault.html 25 KB
  75. Lab 67 Implementing immutable audit trails for database ops.html 31.7 KB
  76. Lab 68 Conducting automated penetration tests on APIs.html 25.8 KB
  77. Lab 69 Configuring disaster recovery database backups.html 26.3 KB
  78. Lab 70 Executing simulated ransomware recovery drills.html 25.5 KB
  9 - Module 8 Observability and Site Reliability Engineering
  79. Observability and Site Reliability Engineering.mp4 79.4 MB
  80. Lab 71 Deploying Prometheus for cluster metric collection.html 25.2 KB
  81. Lab 72 Instrumenting microservices with OpenTelemetry.html 31.1 KB
  82. Lab 73 Configuring Grafana Loki for centralized logging.html 22.1 KB
  83. Lab 74 Setting up distributed tracing across API gateways.html 23.8 KB
  84. Lab 75 Defining SLIs and SLOs for clinical response times.html 25.8 KB
  85. Lab 76 Creating intelligent alert routing rules in Alertmanager.html 26 KB
  86. Lab 77 Simulating network partitions in chaos engineering tests.html 27.9 KB
  87. Lab 78 Optimizing database query performance indices.html 24 KB
  88. Lab 79 Implementing auto-scaling rules based on queue depth.html 26.6 KB
  89. Lab 80 Reviewing post-incident analysis reports and fixes.html 23.6 KB
  3. Lab 1 Setting up Linux container runtime environments.html 46.1 KB
  4. Lab 2 Initializing secure local network namespaces.html 33.2 KB
  5. Lab 3 Configuring PostgreSQL for clinical data persistence.html 39.3 KB
  6. Lab 4 Deploying a standalone HAPI FHIR JPA server instance.html 37.2 KB
  7. Lab 5 Validating Patient resource schemas via curl requests.html 38.5 KB
  8. Lab 6 Constructing JSON payloads for clinical observations.html 44.8 KB
  9. Lab 7 Building automated schema validation test scripts.html 39.6 KB
  90. Edge Computing and IoT Health Integration.mp4 75.2 MB
  91. Lab 81 Configuring MQTT brokers for medical IoT devices.html 25.1 KB
  92. Lab 82 Programming ESP32 firmware for sensor telemetry.html 22.7 KB
  93. Lab 83 Implementing TLS client certificates on edge nodes.html 26.4 KB
  94. Lab 84 Building lightweight edge data filtering daemons.html 22.5 KB
  95. Lab 85 Handling intermittent network connectivity offline.html 23.4 KB
  96. Lab 86 Synchronizing edge buffers upon reconnection.html 24.8 KB
  97. Lab 87 Deploying K3s lightweight Kubernetes on edge hosts.html 27.7 KB
  98. Lab 88 Managing remote device updates securely over-the-air.html 25.7 KB
  99. Lab 89 Monitoring edge hardware vitals and temperature.html 27 KB

Description


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

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