WALLET Ai
Vertex AI Training Course
Google Vertex
12 Months
$1000
- Overview of Vertex AI
- Understanding AI Concepts
- Setting up the Vertex AI Environment
- Regression and Classification Concepts in Vertex AI
- NLP Concepts in Vertex AI
- Setting up a Containerize Training Code
- Deploying a Model Endpoint
- Troubleshooting and Testing
BigQuery
2 Years
$5000
- Introduction to BigQuery Web UI
- Creating and managing datasets and tables
- Loading data into BigQuery
- Basic SQL queries in BigQuery
- Advanced SQL querying techniques
- Aggregating data with GROUP BY
- Using user-defined functions (UDFs)
- Managing and scheduling queries
- Introduction to BigQuery scripting
- Handling arrays and structs
- Working with JSON data
- Using the Query Execution Plan
- Troubleshooting and debugging queries
- Cost control and billing best practices
- Security and access control in BigQuery
- Real-time data analysis with streaming data
- Integrating BigQuery with Google Data Studio
- Introduction to BigQuery ML for machine learning
- Building and deploying machine learning models with BigQuery ML
- Data lake integration with BigQuery
Architecting
4 Years
$10,000
- Module 1: Introduction to Google Cloud
- List the different ways of interacting with Google Cloud
- Use the Cloud Console and Cloud Shell
- Create Cloud Storage buckets
- Use the Google Cloud Marketplace to deploy solutions
- Module 2: Virtual Networks
- List the VPC objects in Google Cloud
- Differentiate between the different types of VPC networks
- Implement VPC networks and firewall rules
- Implement Private Google Access and Cloud NAT
- Module 3: Virtual Machines
- Recall the CPU and memory options for virtual machines
- Describe the disk options for virtual machines
- Explain VM pricing and discounts
- Use Compute Engine to create and customize VM instances
- Module 4: Cloud IAM
- Describe the Cloud IAM resource hierarchy
- Explain the different types of IAM roles
- Recall the different types of IAM members
- Implement access control for resources using Cloud IAM
- Module 5: Storage and Database Services
- Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable
- Choose a data storage service based on your requirements
- Implement data storage services
- Module 6: Resource Management
- Describe the cloud resource manager hierarchy
- Recognize how quotas protect Google Cloud customers
- Use labels to organize resources
- Explain the behavior of budget alerts in Google Cloud
- Examine billing data with BigQuery
- Module 7: Resource Monitoring
- Describe the services for monitoring, logging, error reporting, tracing, and debugging
- Create charts, alerts, and uptime checks for resources with Cloud Monitoring
- Use Cloud Debugger to identify and fix errors
- Module 8: Interconnecting Networks
- Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud
- Determine which Google Cloud interconnect or peering service to use in specific circumstances
- Create and configure VPN gateways
- Recall when to use Shared VPC and when to use VPC Network Peering
- Module 9: Load Balancing and Autoscaling
- Recall the various load balancing services
- Determine which Google Cloud load balancer to use in specific circumstances
- Describe autoscaling behavior
- Configure load balancers and autoscaling
- Module 10: Infrastructure Automation
- Automate the deployment of Google Cloud services using Deployment Manager or Terraform
- Outline the Google Cloud Marketplace
- Module 11: Managed Services
- Describe the managed services for data processing in Google Cloud