Dive into the Public Cloud deployment model, exploring its characteristics, benefits, and common use cases. Learn why AWS is a prime example of a Public Cloud and how organizations leverage its vast resources.
Explore the Private Cloud deployment model, including its characteristics, benefits, and typical use cases. Learn why organizations choose private clouds for enhanced control and data residency requirements.
Discover the Hybrid Cloud deployment model, its characteristics, benefits, and typical use cases. Learn how organizations integrate public and private clouds for optimal flexibility, security, and cost efficiency.
Reinforce your understanding of cloud deployment models with practical examples of public, private, and hybrid clouds. Explore how diverse organizations leverage each model to meet their unique business and technical requirements.
Explore Infrastructure as a Service IaaS, the foundational cloud service model. Understand its characteristics, benefits, typical use cases, and how AWS services like EC2 embody the IaaS paradigm.
Explore Platform as a Service (PaaS), the cloud service model that abstracts infrastructure management. Understand its characteristics, benefits, typical use cases, and how AWS services like Elastic Beanstalk embody the PaaS paradigm.
Discover Software as a Service (SaaS), the most abstracted cloud model. Understand its characteristics, benefits for end-users, typical use cases, and how AWS offers its own SaaS solutions.
Gain a comparative overview of IaaS, PaaS, and SaaS cloud service models. Understand their unique characteristics, responsibilities, and use cases to choose the right model for any cloud computing scenario.
Master the fundamental concepts of scalability and elasticity in cloud computing. Understand the difference between vertical and horizontal scaling, and how AWS services enable dynamic resource adjustment to meet varying demands.
Master the crucial cloud design principles of High Availability and Fault Tolerance. Understand their differences, importance for business continuity, and how AWS global infrastructure and services enable resilient architectures.
Explore the robust AWS Global Infrastructure, including the hierarchical structure of Regions, Availability Zones, and Edge Locations. Understand how this architecture ensures high availability, fault tolerance, and low latency for your cloud deployments worldwide.
Explore the robust AWS Global Infrastructure, including the hierarchical structure of Regions, Availability Zones, and Edge Locations. Understand how this architecture ensures high availability, fault tolerance, and low latency for your cloud deployments worldwide.
Master the foundational AWS Shared Responsibility Model, a critical concept for cloud security and the Cloud Practitioner exam. Understand who is responsible for what, ensuring robust security in the cloud.
Delve deeper into AWS's specific responsibilities under the Shared Responsibility Model. Understand what 'Security OF the Cloud' truly entails, covering AWS's obligations for physical security, infrastructure, networking, and managed services.
Gain a comprehensive understanding of the customer's responsibilities under the AWS Shared Responsibility Model. Explore what 'Security IN the Cloud' entails for your data, operating systems, applications, and network configurations.
Master the fundamental concepts of AWS Identity and Access Management (IAM). Learn about IAM users, groups, roles, and policies, and how they work together to securely control access to your AWS resources with the principle of least privilege.
Fortify your AWS environment by implementing IAM best practices. Learn essential recommendations for managing the root user, enforcing Multi-Factor Authentication (MFA), applying the principle of least privilege, and conducting regular security audits.
Fortify your AWS account security with Multi-Factor Authentication (MFA). Learn what MFA is, why it's critical, the various types of MFA devices supported by AWS, and step-by-step guidance on how to enable it for different AWS identities.
Explore AWS Identity services, focusing on the foundational role of IAM and the centralized access management capabilities of AWS IAM Identity Center (formerly AWS SSO). Learn how these services secure access across multiple AWS accounts and integrated applications.
Master AWS Key Management Service (KMS) and AWS Secrets Manager, essential tools for protecting encryption keys and sensitive credentials. Learn how these services enhance data security, compliance, and streamline credential management across your AWS environment.
Fortify your web applications against common exploits and DDoS attacks using AWS WAF and AWS Shield. Understand how these services provide essential layers of defense, ensuring the availability and integrity of your online presence.
Master the crucial concepts of encryption at rest and encryption in transit within AWS. Understand their importance for data protection, how AWS services implement them, and the various encryption options available to secure your sensitive information.
Master the critical AWS compliance programs like ISO 27001, SOC reports, and PCI DSS. Understand the importance of compliance in cloud environments and how AWS helps customers meet their regulatory obligations through shared responsibility and robust certifications.
Deepen your understanding of shared compliance responsibility in the AWS Cloud. Learn how AWS and customers collaborate to meet regulatory requirements, differentiating between AWS's compliance status and the customer's ongoing compliance obligations.
Master AWS Artifact, your on-demand portal for compliance reports. Learn how to access AWS's security and compliance documents (ISO, SOC, PCI DSS) to streamline your own audit processes and validate AWS's compliance posture.
Master cloud governance frameworks and policies within AWS. Learn how organizations establish internal oversight, integrate AWS tools for policy enforcement, and ensure ongoing compliance and controlled resource management in the cloud.
Design a full ML system for a manufacturing plant. Ingest sensor data, train a forecasting model, deploy via CI/CD, and monitor for drift.
A high-level review of the key concepts for each domain of the Google Cloud Professional Machine Learning Engineer exam.
How to deconstruct the exam questions. A guide to the most common question patterns and how to interpret the scenarios.
How to make the most of your time on the exam. A guide to time management and exam tactics.
A checklist of the key concepts and topics to review before you take the exam.
VPC-SC, CMEK, Private Endpoints, and Custom Service Accounts. How to secure your ML infrastructure for the enterprise.
How to build and maintain a robust and reliable ML system. A guide to the key principles of MLOps.
How to design and build scalable ML systems on Google Cloud. A guide to the most common infrastructure patterns.
How to establish metrics and baseline monitoring for your ML models using Vertex AI Model Monitoring.
How to detect and prevent training-serving skew. A guide to using TensorFlow Data Validation (TFDV) to compare your training and serving data.
How to monitor your model's performance over time and detect feature drift. A guide to using Vertex AI Model Monitoring.
How to troubleshoot common errors in training and serving. A guide to debugging your ML models.
How to build AI systems that are safe, fair, and transparent. A guide to responsible AI practices.
How to ensure that your model is ready for production and that it meets all your ethical requirements.
How to use Vertex Explainable AI to understand your model's predictions. A guide to the different feature attribution methods available on Vertex AI.
How to track and compare datasets and model artifacts using Vertex AI ML Metadata.
How to establish metadata tracking and lineage for your ML workflows using Vertex AI ML Metadata.
How to manage versions of your datasets, models, and other ML assets using the Vertex AI Model Registry and other tools.
When to retrain your model. A guide to defining retraining policies based on schedule, performance decay, and new data.
How to automate your ML workflows using Cloud Build. A guide to integrating your ML pipelines with CI/CD tools.
How to safely and automatically deploy your models to production. A guide to continuous integration and delivery (CI/CD) for ML models.
The heart of MLOps. Learn how to design ML pipeline architectures using Kubeflow Pipelines (KFP), TensorFlow Extended (TFX), and Cloud Composer.
How to ensure data quality and model performance across training and serving. A guide to TensorFlow Data Validation (TFDV) and TensorFlow Model Analysis (TFMA).
How to survive Black Friday. Learn about Autoscaling, GPU Inference, TF-TRT, and optimizing latency for high-throughput serving.