کتاب Google Cloud Platform: Real-time Data Analysis Application Development with Advanced AI and Machine Learning Techniques با فرمت AZW3 برای کتابخوان کیندل و فرمت EPUB

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره Google Cloud Platform: Real-time Data Analysis Application Development with Advanced AI and Machine Learning Techniques

موضوع کلی: برنامه نویسی

موضوع میانی: Google Cloud Platform (GCP)

📋 سرفصل‌های دوره

• 1. Introduction to Cloud Computing and GCP
• 2. Navigating the Google Cloud Console
• 3. GCP Resource Hierarchy and Organization
• 4. Identity and Access Management (IAM) Fundamentals
• 5. Billing and Cost Management in GCP
• 6. Core Networking Concepts in GCP (VPC, Subnets)
• 7. Introduction to Cloud Storage: Buckets and Objects
• 8. Managing Data with Cloud Storage Best Practices
• 9. Compute Engine: Virtual Machines for Data Processing
• 10. Serverless Compute with Cloud Functions
• 11. Introduction to BigQuery: The Serverless Data Warehouse
• 12. BigQuery Data Organization: Datasets, Tables, Views
• 13. Querying Data with Standard SQL in BigQuery
• 14. Optimizing BigQuery Performance: Partitioning and Clustering
• 15. Ingesting Data into BigQuery: Batch and Streaming Inserts
• 16. Introduction to Google Cloud Pub/Sub
• 17. Publishing Messages to Pub/Sub Topics
• 18. Subscribing to Pub/Sub Messages: Pull vs. Push
• 19. Real-time Data Ingestion Patterns with Pub/Sub
• 20. Introduction to Google Cloud Dataflow and Apache Beam
• 21. Dataflow Programming Model: PCollections, Transforms
• 22. Developing Batch Data Pipelines with Dataflow
• 23. Developing Streaming Data Pipelines with Dataflow
• 24. Windowing and Watermarks in Streaming Dataflow
• 25. Error Handling and Resilience in Dataflow Pipelines
• 26. Monitoring and Troubleshooting Dataflow Jobs
• 27. Introduction to Google Cloud Dataproc for Apache Spark/Hadoop
• 28. Using Dataproc for Large-scale Batch Data Processing
• 29. Real-time Stream Processing with Spark Streaming on Dataproc
• 30. Cloud SQL: Managed Relational Databases
• 31. Firestore and Datastore: NoSQL Document Databases
• 32. Designing Architectures for Real-time Data Analytics
• 33. Building Event-Driven Systems on GCP
• 34. Introduction to Cloud Run: Serverless Containers
• 35. Deploying Microservices with Cloud Run
• 36. Building RESTful APIs with Cloud Functions
• 37. Containerization with Docker for Application Development
• 38. Introduction to Google Kubernetes Engine (GKE)
• 39. Deploying Scalable Applications on GKE
• 40. API Management with Cloud Endpoints and API Gateway
• 41. Real-time Data Visualization with Looker Studio (formerly Data Studio)
• 42. Connecting BigQuery to Looker Studio for Dashboards
• 43. Building Custom Dashboards and Reports
• 44. Developing User Interfaces for Real-time Applications
• 45. Implementing WebSockets for Bidirectional Communication
• 46. Push Notifications and Messaging with Firebase Cloud Messaging
• 47. Monitoring Application Health and Performance with Cloud Monitoring
• 48. Logging and Auditing with Cloud Logging
• 49. Setting up Alerts and Notifications for Real-time Systems
• 50. Continuous Integration and Continuous Delivery (CI/CD) with Cloud Build
• 51. Introduction to Artificial Intelligence and Machine Learning
• 52. The Machine Learning Lifecycle on GCP
• 53. Data Preprocessing and Feature Engineering for ML
• 54. Overview of Supervised Learning Models
• 55. Overview of Unsupervised Learning Models
• 56. Introduction to Google Cloud Vertex AI
• 57. Vertex AI Workbench: Managed Notebooks for ML Development
• 58. Managing Datasets in Vertex AI
• 59. Custom Model Training with Vertex AI Training
• 60. Hyperparameter Tuning with Vertex AI Vizier
• 61. Model Evaluation and Metrics in Vertex AI
• 62. Model Versioning and Registration in Vertex AI Model Registry
• 63. Deploying Models for Real-time Prediction with Vertex AI Endpoints
• 64. Performing Batch Predictions with Vertex AI
• 65. Monitoring Deployed Models for Drift and Performance
• 66. Introduction to Explainable AI (XAI) on Vertex AI
• 67. Vertex AI Feature Store: Managing Features for ML
• 68. Real-time Feature Serving from Vertex AI Feature Store
• 69. Integrating Streaming Data for Real-time ML Inference
• 70. MLOps Principles and Practices on GCP
• 71. Introduction to Vertex AI Pipelines for MLOps Automation
• 72. Building CI/CD Workflows for ML Models with Vertex AI Pipelines
• 73. Automated Model Retraining and Redeployment
• 74. Drift Detection and Adaptation in Real-time ML Systems
• 75. Introduction to Natural Language Processing (NLP) APIs
• 76. Using Cloud Natural Language API for Text Analysis
• 77. Speech-to-Text and Text-to-Speech APIs for Voice Interaction
• 78. Introduction to Cloud Vision AI for Image Analysis
• 79. Object Detection and OCR with Vision AI
• 80. Video Intelligence API for Video Analysis
• 81. Cloud Translation API for Multilingual Applications
• 82. Building Custom AI Models with AutoML Vision/Natural Language
• 83. Advanced Machine Learning: Deep Learning Fundamentals
• 84. Time Series Analysis for Real-time Predictive Modeling
• 85. Real-time Anomaly Detection using ML Techniques
• 86. Developing Real-time Recommendation Engines
• 87. Advanced Security: VPC Service Controls for Data Loss Prevention
• 88. Data Governance and Compliance with Cloud Data Loss Prevention (DLP)
• 89. Cost Optimization for Real-time Data and ML Workloads
• 90. Performance Tuning for BigQuery and Dataflow in Production
• 91. Designing an End-to-End Real-time Fraud Detection System
• 92. Building a Personalized Content Recommendation Application
• 93. Real-time Inventory Management and Demand Forecasting
• 94. Developing an IoT Data Analytics Platform
• 95. Creating Real-time Dashboards with Looker
• 96. Advanced Data Security: Encryption at Rest and In Transit
• 97. Audit Logging and Security Monitoring Best Practices
• 98. Disaster Recovery and High Availability Strategies for Real-time Systems
• 99. Future Trends in Real-time AI, ML, and Data Analytics on GCP
• 100. Capstone Project: End-to-End Real-time AI Application Development