Fast-Track Your Company’s Digital Transformation with Expert-Led Training

Overview of Deep Learning

• Definition and key concepts
• Historical development and milestones

Neural Networks Basics

• Structure and components of a neural network
• Activation functions and Forward Propagation
• Forward and backward propagation

Activation Functions and Forward Propagation

• Types and their impact on model performance
• Commonly used activation functions in deep learning
• Choosing the right activation function for different scenarios

Backward Propagation and Model Training

• Gradients and optimization algorithms
• Model Training: Hyperparameter tuning and regularization techniques
• Hands-on Exercise: Building a simple neural network in Pytorch

Applications of Deep Learning in Banking

• Overview and use cases
• Importance of deep learning in transforming banking operations
• Future trends in AI and banking

Credit Scoring and Risk Assessment

• Building credit scoring models with neural networks
• Evaluating model performance in credit risk assessment
• Practical considerations and challenges

Fraud Detection and Prevention

• The Autoencoder Architecture
• Deep Learning for Anomaly Detection
• Evaluating model performance in credit risk assessment

Customer Service Automation

• Enhancing customer experience with AI
• Chatbots and virtual assistants in banking
• Real-time fraud detection using neural networks
• Implementing automated customer support systems

‍Convolutional Neural Networks (CNNs)

• Introduction and architecture of CNNs
• Convolutional layers and feature extraction
• Applications of CNNs in image processing

Image Recognition in Banking

• Use cases and case studies
• Implementing image recognition systems in financial institutions
• Challenges and solutions in image-based deep learning applications

Basic of Computer Vision

• Understanding Image Data Structures
• Image Operation using OpenCV

Image Recognition Application

• Image Classification
• Data Annotation
• Object Detection
• Study Case: Image Recognition Application

Recurrent Neural Networks (RNNs)

• Understanding sequential data
• Long Short-Term Memory (LSTM) networks
• Time Series Analysis in Finance: Predictive modeling with RNNs

Transfer Learning and Fine-tuning

• Leveraging pre-trained models
• Customization for Banking Applications: Adapting models for specific case
• Hands-on Workshop: Implementing advanced techniques in Pytorch

Basics of Natural Language Processing

• Understanding Text Processing
• Fundamental Text Processing (Bag of Word & TF-IDF)

Deep Learning for Natural Languange Processing

• Word Tokenization
• Understanding Embedding Layer
• Apply RNN & LSTM to text data
• Study Case: Text Classification

Deep Learning Implementation

• Real-world applications of advanced deep learning
• Q&A session on advanced deep learning technique

Deployment Strategies

• Cloud-based vs. on-premises deployment
• Choosing the right deployment model for banking applications
• Continous Improvement and Continous Delivery (CI & CD)
• Git Workflow
• Containerization using Docker

Model Monitoring and Maintenance

• Importance and challenges
• Continuous monitoring for performance: Tools and techniques
• Handling Concept Drift: Strategies for adapting to changing data

Model Retraining Strategies

• Keeping models up-to-date
• Hands-on Exercise: Deploying a model in a cloud environment
• Case Studies: Success stories and lessons learned in model deployment

Ethical Guidelines in AI

• Overview and importance of Responsible AI Practices
• Implementing ethical principles in deep learning
• Case Studies: Ethical considerations in banking applications

Mitigating Bias in Deep Learning Models

• Identifying and addressing biases
• Strategies for Bias Reduction: Techniques and best practices
• Hands-on: Evaluating model fairness and mitigating bias

Regulatory Compliance and Data Privacy

• GDPR and other regulations
• Data Anonymization Techniques: Protecting customer privacy
• Privacy-preserving techniques in AI applications

Federated Learning: Collaborative Model Training

• Introduction to federated learning
• Advantages and challenges of federated learning in banking
• Implementing federated learning for privacy-preserving

Transformer Models in Deep Learning

• Introduction to Transformer architecture
• Understanding self-attention mechanisms
• Applications of Transformers in banking and finance

Hands-on Workshop: Implementing Transformer Models

• Setting up and working with Transformer architectures
• Practical exercises with attention mechanisms
• Implementing a Transformer model for a banking use case

Generative AI: Understanding and Implementing

• Introduction to generative AI
• Generative AI Applications in the banking industry

LLM Generative AI (ChatGPT, Bard, etc)

• Understanding LLM Gen AI Capability
• Introduction to Prompt Engineering
• LLM Study Case: Text Summarization
• LLM Study Case: AI Chatbot

Capstone Project

• Introduction to the Guided Case Study
• Definition of Project Objectives and Scope

• End to end Deep Learning Study Case
• Group Presentation

Overview of Machine Learning

• What is Machine Learning and what is not machine learning
• Historical Development of AI & Machine Learning
• Types of Machine Learning

Overview of Machine Learning application in Banking

• Key Concept of Machine Learning Application
• Workflow of Machine Learning
• Example of Machine Learning Implementation in Banking

Tools in Machine Learning

• Programming Concept in Machine Learning
• Introduction to Programming Tools
• Basic Python (Variable, Conditions, Loop, Function)
• Python Library

Exploratory Data Analysis (EDA)

• ‍Importance of EDA in Machine Learning
• Descriptive Statistics
• Data Visualization Techniques
• Identifying Patterns and Anomalies

Data Preprocessing Techniques

• ‍Handling Missing Data
• Feature Scaling and Normalization
• Encoding Categorical Variables
• Dealing with Outliers

Practical Session

• ‍Applying EDA and Data Preprocessing Techniques on a Banking Dataset
• Understanding and Implementing Machine Learning Metrics

Supervised Machine Learning

• ‍Understanding Key Concept of Supervised Machine Learning
• Advantages and Disadvantages of Supervised Learning
• Selecting Appropriate Machine Learning Type for Business Objectives

Supervised Machine Learning (Regression vs Classification)

• Distinctions between Regression and Classification
• Choosing the Right Approach for the Task
• Real-world Examples in Banking

Regression Model

• ‍Understanding Regression Model
• Basics of Linear Regression for Regression Task
• Assumptions and Model Interpretation
• Hands-on Coding Exercise with a Banking Dataset

Regression Model

• Understanding Classification Model
• Basics of Logistic Regression for Classification Task
• Assumptions and Model Interpretation
• Hands-on Coding Exercise with a Banking Dataset

Advanced Supervised Machine Learning Model

• Introduction to Support Vector Machines (SVM)
• Overview of Decision Trees and Random Forest
• Practical Implementation and Hyperparameter Tuning

Machine Learning Metrics

• Evaluation Metrics for Regression (e.g., Mean Squared Error, R-squared)
• Evaluation Metrics for Classification (e.g., Accuracy, Precision, Recall)
• Selecting Appropriate Metrics for Business Objectives
• Interpretation of Metrics Results

Model Optimization Strategies and Overfitting/Underfitting

• Balancing Bias and Variance
• Recognizing Overfitting and Underfitting
• Techniques to Mitigate Overfitting (Regularization)
• Cross-Validation Techniques
• Hyperparameter Optimization Best Practices

Machine Learning Model Deployment

• The importance of model deployment
• Machine Learning Deployment using Streamlit
• Machine Learning Deployment using FastAPI

Capstone Project

• Introduction to the Guided Case Study
• Definition of Project Objectives and Scope
• End-to-end Project Study Case of Machine Learning in Banking
• Group Presentation

Foundations of Unsupervised Learning

• Introduction to Unsupervised Learning
• Applications in the banking industry
• Types of Unsupervised Machine Learning

Overview of Clustering in Machine Learning

• Clustering Techniques
• Clustering Approach
• Example of Clustering Implementation in Banking

Clustering Algorithm

• Clustering TechniquesK-Means Clustering
• Hierarchical Clustering
• Density-based clustering
• Applications in customer segmentation

Dimensionality Reduction

• Overview of Dimensionality Reduction
• Principal Component Analysis (PCA)
• Singular Value Decomposition (SVD)
• Application of Dimensionality Reduction

Anomaly Detection

• ‍‍Overview of Anomaly Detection
• Anomaly Detection Techniques
• Isolation Forest
• One-Class SVM

Practical Session

• Real-world Application in Banking
• Case studies and use-cases in fraud detection

System Recommendation

• Overview of System Recommendation
• Content-based recommendation
• Collaborative Filtering

Capstone Project

• Introduction to the Guided Case Study
• Definition of Project Objectives and Scope
• End-to-end Project Study Case of Machine Learning in Banking
• Group Presentation

• Aligning security standards and objectives with business goals
• Conducting risk assessments and identifying critical assets
• Developing security policies and procedures
• Selecting and implementing appropriate security controls

• Key performance indicators (KPIs) for cybersecurity
• Data collection and analysis for security metrics
• Reporting and communicating security performance to stakeholders
• Using metrics to drive continuous improvement

• Real-world examples of successful security programs
• Emerging trends and best practices in cybersecurity
• Group discussion and sharing of experiences
• Action planning for implementing security standards and objectives

• Importance of network security
• Security threats and vulnerabilities
• Network security architecture and concepts

• Understanding network functions and configurations
• Port scanning techniques and tools
• Identifying common networking vulnerabilities
• Hands-on practice with port scanning tools

• Types of firewalls and their functionalities
• Firewall configuration basics
• Best practices for firewall deployment

• Common network attack types and methodologies
• Network architecture considerations for security
• Implementing network segmentation and isolation

• Wi-Fi vulnerabilities and common attacks
• Secure Wi-Fi configuration practices
• Encryption protocols and WPA standards

• Introduction to network monitoring tools and techniques
• Identifying suspicious activity and potential threats
• Log analysis and incident response basics

• Online tracking methods and technologies
• Third-party data collection and sharing practices
• Building awareness of online privacy risks

• Search engine tracking practices and privacy settings
• Alternative search engines with privacy focus
• Techniques for minimizing search engine tracking

• Browser security features and settings
• Browser extensions for privacy protection
• Blocking tracking scripts and cookies
• Hands-on practice with browser security tools

• Discussing recent network security incidents and breaches
• Analyzing real-world case studies and attack scenarios
• Applying learnings to practical situations

• Review of key workshop concepts and takeaways
• Hands-on assessment on network security
• Course evaluation and feedback
• Open discussion and answering participant questions

• Define endpoints and explain their diverse types
• Identify key endpoint security threats and associated risks
• Grasp best practices and solutions for effective endpoint protection

• Differentiate traditional anti-virus from Next-Generation (NGAV) endpoint protection technologies
• Understand the capabilities and limitations of different NGAV solutions
• Select and implement an appropriate endpoint protection solution based on organizational needs

• Master various techniques and tools for detecting and monitoring threats on endpoints
• Conduct effective security incident investigations and implement appropriate responses
• Apply active hunting methods to discover malware and attackers within your network

• Apply effective hardening techniques for popular operating systems like Windows, macOS, and Linux
• Implement robust application and software security measures
• Understand and employ the least privilege principle for secure access configuration

• Implement secure deleting techniques to permanently erase sensitive data
• Understand methods for eliminating digital evidence and preventing digital forensics investigations
• Learn strategies to hinder the recovery of deleted data

• Introducing the concept of operational security
• Best practices for maintaining online confidentiality and anonymity
• Preventing and mitigating tracking and surveillance techniques

• Introduction and comparison of secure live operating systems
• Selecting the right OS based on individual needs
• Installation and configuration of live operating systems

• Understanding how VPNs and Tor Browser protect online privacy
• Selecting and using a secure VPN
• Configuring and using Tor Browser for anonymity

• Types of proxies (HTTP, HTTPS, SOCK, and Web) and their functions
• Selecting and using appropriate proxies
• Configuring proxies across various applications

• Introducing SSH and its benefits for secure communication
• Establishing and managing secure SSH connections
• Using SSH for file transfer and remote access

• Introducing 12P and its services for online anonymity
• Using 12P for secure communication

• Key takeaways from the training materials
• Discussion and question-answer session
• Training evaluation

• Understand the core concepts of cybersecurity (CIA triad, confidentiality, integrity, availability)
• Identify common threats and vulnerabilities

• Recognize common attack vectors (phishing, malware, social engineering)
• Analyze attack stages, and implement basic cybersecurity controls (authentication, authorization, encryption, firewalls, intrusion detection/prevention systems)

• Set up a virtual environment (VirtualBox or VMware)
• Install a secure operating system (e.g., Kali Linux) for hands-on practice

• Utilize network scanning tools (Nmap, Nessus) to identify vulnerabilities in networks and web applications
• Interpret scan results
• Prioritize remediation efforts.

• Comprehend common password vulnerabilities
• Explore password cracking techniques (brute force, dictionary attacks, rainbow tables)
• Apply password best practices for mitigation

• Introduce the Metasploit Framework
• Exploit vulnerabilities using pre-configured modules
• Gain access to systems
• Perform post-exploitation activities in a safe, controlled environment

• Understanding the CTI landscape and its role in cybersecurity
• Key concepts: threat actors, indicators of compromise (IOCs), intelligence cycle
• Benefits and limitations of CTI
• CTI applications in different organizational contexts
• Interactive workshop: Identifying threats and vulnerabilities in a case study

• Essential security concepts: confidentiality, integrity, availability (CIA triad)
• Common attack vectors and mitigation strategies
• Network security principles and controls
• Hands-on activity: Simulating network attacks and defenses using a virtual environment

• Techniques for gathering data from publicly available sources
• Tools and platforms for effective OSINT collection
• Ethical considerations and legal boundaries
• Group exercise: Conducting an OSINT investigation on a specific threat actor

• Structured analytical techniques (SATs) for CTI analysis
• Threat reporting best practices: clarity, conciseness, actionable insights
• Communication strategies for different audiences (technical, managerial)
• Collaborative analysis and threat sharing across teams
• Case study: Analyzing a complex cyberattack and generating a comprehensive report

• Common threat actor groups and their motivations
• Tactics, techniques, and procedures (TTPs) analysis
• Attribution challenges and methodologies
• Group activity: Profiling a real-world threat actor based on intelligence reports

• SIEM/SOC integration for threat detection and response
• SOAR automation to streamline CTI workflows
• Continuous threat monitoring and incident response using CTI insights
• Demonstration: Integrating CTI feeds into a SIEM platform

• SIEM/SOC integration for threat detection and response
• SOAR automation to streamline CTI workflows
• Continuous threat monitoring and incident response using CTI insights
• Demonstration: Integrating CTI feeds into a SIEM platform

• Artificial intelligence (AI) and machine learning (ML) applications in CTI
• Cyber threat sharing platforms and communities
• The future of threat actor behavior and attack evolution
• Group discussion: Brainstorming future developments and implications for CTI

• Apply learned skills to a simulated real-world scenario
• Conduct a comprehensive threat intelligence investigation
• Develop a mitigation plan based on analysis
• Present findings and recommendations

• Definition and types of phishing attacks (email, phone, SMS, etc.)
• Red flags to identify phishing attempts (suspicious sender, urgent tone, grammar errors, etc.)
• Best practices for safe email handling and password management
• Interactive quiz and phishing simulation exercise

• Common causes of data breaches (accidental leaks, malicious attacks, etc.)
• Impact of data breaches on individuals and organizations (financial loss, reputational damage, legal consequences)
• Importance of data protection and compliance regulations
• Case studies of real-world data breaches

• Definition and how ransomware works (encryption, data exfiltration)
• Different types of ransomware attacks and their targets
• Consequences of ransomware infection (operational disruption, data loss, ransom demands)
• Best practices for preventing ransomware attacks (software updates, backups, awareness training)

• Definition and importance of IRM in cybersecurity
• Key phases of the IRM framework (preparation, identification, containment, eradication, recovery, post-incident activities)
• Overview of roles and responsibilities within an IRM team

• Preparation: Develop an IR plan, conduct risk assessments, establish communication protocols
• Identification: Detect security incidents through monitoring and reporting procedures
• Containment: Minimize the impact of the incident by isolating affected systems and stopping the attack
• Eradication: Remove the threat and identify the root cause of the incident
• Recovery: Restore compromised systems and data following established procedures
• Post-Incident Activities: Document lessons learned, improve security posture, and conduct post-incident reviews

• Divide participants into teams and simulate different cyber incidents (phishing attack, data breach, ransomware infection)
• Teams apply IRM framework steps to each scenario, discuss decision-making, and identify challenges
• Debriefing and discussion of team responses and lessons learned

• Importance of clear and timely communication during an incident
• Internal communication protocols for informing leadership, IT teams, and affected users
• External communication strategies for communicating with customers, partners, and regulators

• What is IAM and why is it important?
• Key IAM components and technologies (e.g., Active Directory, LDAP, SAML, OAuth)
• IAM risks and challenges

• Identification: User registration and management
• Authentication: Methods and factors (e.g., passwords, multi-factor authentication)
• Authorization: Roles, permissions, and access control models (e.g., RBAC, ABAC)
• Accounting: Monitoring and logging user activity

• Why PAM is critical for security
• Identifying and classifying privileged accounts
• Least privilege principle and enforcing it
• Secure password management for privileged accounts
• Monitoring and auditing privileged user activity

• Access request and provisioning processes
• Identity lifecycle management
• User self-service options
• Role-based access control (RBAC) and fine-grained access control (ABAC)
• Identity reconciliation and synchronization

• Understanding data classification and sensitivity levels
• Data access control principles and best practices
• Securing data at rest, in transit, and in use
• Integrating IAM with data protection solutions
• Compliance considerations for data governance

• Implementing multi-factor authentication
• Configuring role-based access control
• Granting and revoking user access
• Monitoring privileged user activity

• DevSecOps principles and benefits
• Security challenges in DevOps environment
• Shifting security left philosophy
• Future of DevSecOps landscape

• Setting up virtual machines using tools like VirtualBox or Vagrant
• Pre-installation of required software on VMs
• User and account management

• Installing Jenkins on a dedicated VM
• Configuring plugins and security settings
• Creating initial Jenkins jobs

• Multiplayer snake application setup
• Running the application on an application server
• Mapping domain name to application server IP address

• Introduction to Pipeline syntax and functionalities
• Building a CI/CD pipeline for the snake application
• Integration with Git repository and build tools
• Automating deployments and testing using pipeline stages

• Static code analysis (SAST) tools and techniques
• Identifying and mitigating vulnerabilities in code
• Integrating SAST tools into the pipeline

• Dynamic Application Security Testing (DAST) principles
• Utilizing DAST tools to identify runtime vulnerabilities
• Automating DAST scans in the pipeline

• Introduction to containerization and Docker
• Security considerations for container images and deployments
• Vulnerability scanning and container registries
• Secure container development practices

• Containerizing the snake application using Dockerfile
• Building and pushing container images to a registry
• Running containerized applications in Kubernetes

• Overview of popular DevSecOps tools and platforms
• Automating security tasks in the CI/CD pipeline
• Integrating security findings into development workflows

• Monitoring application security posture and infrastructure
• Logging for security incident detection and analysis
• Utilizing security information and event management (SIEM) tools

• What is vulnerability assessment?
• Why is vulnerability assessment important?
• Benefits of conducting vulnerability assessments
• Different types of vulnerability assessments

• Defining security threats and vulnerabilities in network environments
• Understanding common attack vectors and techniques
• Introduction to network security controls like firewalls, intrusion detection/prevention systems (IDS/IPS), and access control lists (ACLs)

• Importance of encryption and its role in secure communication
• Understanding and configuring secure protocols like HTTPS and VPN
• Practical hands-on with tools like Wireshark to analyze network traffic and identify potential security issues

• Introduction to different types of databases and their security considerations
• Understanding the concepts of data integrity, confidentiality, and availability
• Identifying common database vulnerabilities like SQL injection and cross-site scripting (XSS)

• Implementing user authentication and authorization mechanisms for database access
• Understanding the principles of least privilege and role-based access control (RBAC)
• Practical demonstration of user management and access control techniques

• Importance of data encryption at rest and in transit for secure storage and transmission
• Understanding and implementing encryption technologies like AES and RSA
• Exploring best practices for data security, including backups, disaster recovery, and vulnerability management

• Detailed exploration of common network attacks like denial-of-service (DoS), man-in-the-middle (MitM), and malware attacks
• Understanding and implementing mitigation strategies for different types of network threats
• Practical exercises using tools like nmap and Metasploit to simulate and analyze security vulnerabilities

• Exploring advanced security features like intrusion detection/prevention systems (IDS/IPS) for databases
• Understanding data masking and tokenization techniques for sensitive information protection
• Implementing security measures for database auditing and logging

• Understanding the importance of incident response planning and procedures
• Learning basic forensics techniques for identifying and analyzing security incidents
• Practical exercises simulating incident response scenarios and practicing evidence collection and analysis

• Exploring popular open-source and commercial tools for network and database security assessments and monitoring
• Understanding the role of security frameworks and standards like NIST Cybersecurity Framework and ISO 27001
• Discussing best practices for staying updated on the latest security threats and vulnerabilities

• Analyzing real-world case studies of successful network breaches and database security incidents
• Learning from the mistakes of others and discussing strategies to avoid similar vulnerabilities
• Group discussions and brainstorming solutions to address emerging security challenges

• What is vulnerability assessment?
• Why is vulnerability assessment important?
• Benefits of conducting vulnerability assessments
• Different types of vulnerability assessments

• Common network vulnerabilities
• Server and web application vulnerabilities
• Remote service vulnerabilities
• Understanding CVSS scoring system

• Using Nmap and Nessus for network scanning
• Identifying and prioritizing network vulnerabilities
• Conducting network security assessments

• Using Nessus and OWASP ZAP for server and web application scanning
• Identifying and prioritizing server and web application vulnerabilities
• Conducting web application security assessments

• Assessing vulnerabilities in VPN services
• Identifying and prioritizing remote service vulnerabilities
• Conducting remote service security assessments

• Nmap
• Nessus
• OWASP ZAP
• Other industry-standard tools

• Analyzing vulnerability assessment results
• Prioritizing remediation efforts
• Generating vulnerability reports

• Scheduling regular vulnerability assessments
• Integrating vulnerability assessment into the security lifecycle
• Maintaining a vulnerability management program

Understanding Design Patterns

• Introduction to design patterns and their benefits
• Common design patterns used in cloud computing

Designing for Scalability and Performance

• Principles of designing scalable and performant cloud solutions
• Techniques for optimizing resource utilization

Designing for Reliability and Availability

• Strategies for ensuring high availability and reliability
• Techniques for building fault-tolerant systems

Designing for Security and Compliance

• Best practices for designing secure and compliant cloud solutions
• Techniques for meeting regulatory requirements

Monitoring and Analyzing Performance

• Tools and techniques for monitoring cloud performance
• Identifying and resolving performance bottlenecks

Optimizing Compute Resources

• Techniques for optimizing the performance of compute resources
• Strategies for rightsizing and autoscaling

Optimizing Storage Resources

• Techniques for optimizing the performance of storage resources
• Strategies for choosing the right storage type

Optimizing Network Resources

• Techniques for optimizing the performance of network resources
• Strategies for designing efficient network topologie

Securing Compute Resources

• Techniques for securing compute resources
• Best practices for managing user access and permissions

Securing Storage Resources

• Techniques for securing storage resources
• Best practices for data encryption and access control

Securing Network Resources

• Techniques for securing network resources
• Best practices for firewall configuration and intrusion detection

Maintaining Compliance

• Strategies for maintaining compliance with regulatory requirements
• Best practices for auditing and reporting

• Review of real-world case studies demonstrating the application of design patterns and best practices
• Discussion of best practices for designing, implementing, and managing efficient, reliable, secure, and compliant cloud solutions

• Key concepts and benefits of cloud computing
• Differentiating between public, private, and hybrid clouds
• Introduction to AWS as a public cloud provider

• Core AWS components: regions, Availability Zones, and security groups
• Understanding VPCs (Virtual Private Clouds) and subnets
• AWS Identity and Access Management (IAM) basics

• Amazon EC2: On-demand virtual servers (instances) with diverse configurations (t2 Micro, c4 Large, etc.)
• Amazon Lambda: Serverless compute service for running code without managing servers
• Amazon ECS & Amazon EKS: Container orchestration platforms for managing containerized applications
• Hands-on: Launching an EC2 instance and exploring its console
• Hands-on: Creating a simple Lambda function

• Amazon S3: Object storage for any type of data, scalable and durable
• Amazon EBS: Block storage for EC2 instances, providing persistent volumes
• Amazon RDS: Managed relational database service (MySQL, PostgreSQL, etc.)
• Amazon DynamoDB: NoSQL database for flexible, scalable data storage
• Containerization with Docker and Amazon ECS

• VPC (Virtual Private Cloud) creation and management
• Subnets and network security groups
• Internet Gateways and NAT Gateways for outbound traffic
• Route 53 for domain name registration and DNS management

• IAM basics: Access policies, roles, and users
• AWS Security Groups and network access control
• Amazon Cognito: User authentication and authorization
• Amazon Key Management Service (KMS): Secure storage and management of encryption keys

• Amazon CloudWatch for monitoring metrics and logs
• CloudTrail for auditing AWS API calls
• Amazon CloudFormation for infrastructure as code (IaC)
• Hands-on: CloudWatch monitoring of an EC2 instance CPU usage

• Building serverless functions for event-driven applications in GitHub
• Utilizing triggers and scaling in Lambda functions or EKS
• Integrating Lambda with other services like S3 and DynamoDB
• Best practices for building and deploying serverless applications in AWS
• Hands-on: Creating a simple Lambda function  and connecting it to S3

• Configuring CloudWatch alarms for metrics like CPU, memory, and network usage
• Visualizing performance metrics and dashboards
• Correlating logs and events with metrics for troubleshooting

• Collecting and ingesting application logs from various sources
• Filtering and analyzing logs for root cause analysis
• Integrating CloudWatch Logs with other services for further analysis
• Creating and routing events based on triggers (e.g., resource state changes, alarms)
• Integrating events with other AWS services for automated actions.
  Implementing incident management workflow with CloudWatch Events

• Monitor a sample application with CloudWatch metrics, logs, and events
• Create alarms and automate actions based on monitoring data
• Simulate and troubleshoot application issues using CloudWatch tools

• Key concepts and benefits of cloud computing
• Differentiating between public, private, and hybrid clouds
• Introduction to AWS as a public cloud provider

• Core AWS components: regions, Availability Zones, and security groups
• Understanding VPCs (Virtual Private Clouds) and subnets
• AWS Identity and Access Management (IAM) basics

• Amazon EC2: On-demand virtual servers (instances) with diverse configurations (t2 Micro, c4 Large, etc.)
• Amazon Lambda: Serverless compute service for running code without managing servers
• Amazon ECS & Amazon EKS: Container orchestration platforms for managing containerized applications
• Hands-on: Launching an EC2 instance and exploring its console
• Hands-on: Creating a simple Lambda function

• Amazon S3: Object storage for any type of data, scalable and durable
• Amazon EBS: Block storage for EC2 instances, providing persistent volumes
• Amazon RDS: Managed relational database service (MySQL, PostgreSQL, etc.)
• Amazon DynamoDB: NoSQL database for flexible, scalable data storage
• Containerization with Docker and Amazon ECS

• VPC (Virtual Private Cloud) creation and management
• Subnets and network security groups
• Internet Gateways and NAT Gateways for outbound traffic
• Route 53 for domain name registration and DNS management

• IAM basics: Access policies, roles, and users
• AWS Security Groups and network access control
• Amazon Cognito: User authentication and authorization
• Amazon Key Management Service (KMS): Secure storage and management of encryption keys

• Amazon CloudWatch for monitoring metrics and logs
• CloudTrail for auditing AWS API calls
• Amazon CloudFormation for infrastructure as code (IaC)
• Hands-on: CloudWatch monitoring of an EC2 instance CPU usage

• DevOps principles and concepts (continuous integration, continuous delivery, infrastructure as code)
• Integrating GitHub with AWS for seamless developer workflows
• Overview of relevant AWS services and GitHub Actions
• Hands-on: Setting up a GitHub repository for DevOps with AWS

• Managing Git repositories effectively in GitHub
• Branching strategies and collaboration best practices
• Utilizing Pull Requests for code review and merge control
• Integrating CodeCommit with GitHub (optional)
• Hands-on: Creating a repository, pushing code, managing branches in GitHub

• Creating automated workflows for builds and tests within GitHub Actions
• Configuring build steps, dependencies, and environment variables
• Triggering workflows automatically on Git events (push, pull request)
• Integrating Actions with CodeBuild or other services
• Hands-on: Building a sample application with GitHub Actions

• Designing and building pipelines for deployment automation utilizing Actions
• Defining stages for code commit, build, test, and deploy using workflows
• Integrating Actions with CodePipeline, CodeDeploy, or other AWS services
• Managing approvals and deployments to various environments (staging, production)
• Hands-on: Creating a simple pipeline for code deployment with Actions and AWS

• Unit testing and integration testing within Actions workflows
• Utilizing AWS services like CodeBuild and Lambda for test execution
• Implementing test coverage and reporting within the pipeline
• Integration with CodePipeline for advanced testing scenarios
• Hands-on: Setting up basic unit tests and integrating them with the Actions pipelin

• Writing infrastructure templates in YAML or JSON and storing them in GitHub
• Defining resources like EC2 instances, VPCs, and security groups
• Utilizing Terraform
• Hands-on: Creating a basic Terraform template in GitHub and deploying it

• Implementing GitOps and feature flags using GitHub Actions
• Utilizing canary deployments and A/B testing for safe rollouts
• Leveraging blue-green deployments for controlled releases
• Hands-on: Implementing a canary deployment using Actions and AWS services

• Understanding containerization benefits and Docker basics
• Building and managing Docker containers within GitHub repositories
• Deploying containerized applications to Amazon ECS
• Scaling and managing containerized applications efficiently
• Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions

• Building serverless functions for event-driven applications in GitHub
• Utilizing triggers and scaling in Lambda functions or EKS
• Integrating Lambda with other services like S3 and DynamoDB
• Best practices for building and deploying serverless applications with GitHub
• Hands-on: Creating a simple Lambda function within GitHub and connecting it to S3

• Configuring CloudWatch alarms for metrics like CPU, memory, and network usage
• Visualizing performance metrics and dashboards
• Correlating logs and events with metrics for troubleshooting

• Collecting and ingesting application logs from various sources
• Filtering and analyzing logs for root cause analysis
• Integrating CloudWatch Logs with other services for further analysis
• Creating and routing events based on triggers (e.g., resource state changes, alarms)
• Integrating events with other AWS services for automated actions
• Implementing incident management workflow with CloudWatch Events

• Monitor a sample application with CloudWatch metrics, logs, and events
• Create alarms and automate actions based on monitoring data
• Simulate and troubleshoot application issues using CloudWatch tools

• DevOps principles and concepts (continuous integration, continuous delivery, infrastructure as code)
• Integrating GitHub with AWS for seamless developer workflows
• Overview of relevant AWS services and GitHub Actions
• Hands-on: Setting up a GitHub repository for DevOps with AWS

• Managing Git repositories effectively in GitHub
• Branching strategies and collaboration best practices
• Utilizing Pull Requests for code review and merge control
• Integrating CodeCommit with GitHub (optional)
• Hands-on: Creating a repository, pushing code, managing branches in GitHub

• Creating automated workflows for builds and tests within GitHub Actions
• Configuring build steps, dependencies, and environment variables
• Triggering workflows automatically on Git events (push, pull request)
• Integrating Actions with CodeBuild or other services
• Hands-on: Building a sample application with GitHub Actions

• Designing and building pipelines for deployment automation utilizing Actions
• Defining stages for code commit, build, test, and deploy using workflows
• Integrating Actions with CodePipeline, CodeDeploy, or other AWS services
• Managing approvals and deployments to various environments (staging, production)
• Hands-on: Creating a simple pipeline for code deployment with Actions and AWS

• Unit testing and integration testing within Actions workflows
• Utilizing AWS services like CodeBuild and Lambda for test execution
• Implementing test coverage and reporting within the pipeline
• Integration with CodePipeline for advanced testing scenarios
• Hands-on: Setting up basic unit tests and integrating them with the Actions pipeline

• Writing infrastructure templates in YAML or JSON and storing them in GitHub
• Defining resources like EC2 instances, VPCs, and security groups
• Utilizing Terraform
• Hands-on: Creating a basic Terraform template in GitHub and deploying it

• Implementing GitOps and feature flags using GitHub Actions
• Utilizing canary deployments and A/B testing for safe rollouts
• Leveraging blue-green deployments for controlled releases
• Hands-on: Implementing a canary deployment using Actions and AWS services

• Understanding containerization benefits and Docker basics
• Building and managing Docker containers within GitHub repositories
• Deploying containerized applications to Amazon ECS
• Scaling and managing containerized applications efficiently
• Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions

• Building serverless functions for event-driven applications in GitHub
• Utilizing triggers and scaling in Lambda functions or EKS
• Integrating Lambda with other services like S3 and DynamoDB
• Best practices for building and deploying serverless applications with GitHub
• Hands-on: Creating a simple Lambda function within GitHub and connecting it to S3

• Key concepts and benefits of cloud computing
• Differentiating between public, private, and hybrid clouds
• Introduction to AWS as a public cloud provider

• Core AWS components: regions, Availability Zones, and security groups
• Understanding VPCs (Virtual Private Clouds) and subnets
• AWS Identity and Access Management (IAM) basics

• Amazon EC2: On-demand virtual servers (instances) with diverse configurations (t2 Micro, c4 Large, etc.)
• Amazon Lambda: Serverless compute service for running code without managing servers
• Amazon ECS & Amazon EKS: Container orchestration platforms for managing containerized applications
• Hands-on: Launching an EC2 instance and exploring its console
• Hands-on: Creating a simple Lambda function

• Amazon S3: Object storage for any type of data, scalable and durable
• Amazon EBS: Block storage for EC2 instances, providing persistent volumes
• Amazon RDS: Managed relational database service (MySQL, PostgreSQL, etc.)
• Amazon DynamoDB: NoSQL database for flexible, scalable data storage
• Containerization with Docker and Amazon ECS

• VPC (Virtual Private Cloud) creation and management
• Subnets and network security groups
• Internet Gateways and NAT Gateways for outbound traffic
• Route 53 for domain name registration and DNS management

• IAM basics: Access policies, roles, and users
• AWS Security Groups and network access control
• Amazon Cognito: User authentication and authorization
• Amazon Key Management Service (KMS): Secure storage and management of encryption keys

• Amazon CloudWatch for monitoring metrics and logs
• CloudTrail for auditing AWS API calls
• Amazon CloudFormation for infrastructure as code (IaC)
• Hands-on: CloudWatch monitoring of an EC2 instance CPU usage

• DevOps principles and concepts (continuous integration, continuous delivery, infrastructure as code)
• Integrating GitHub with AWS for seamless developer workflows
• Overview of relevant AWS services and GitHub Actions
• Hands-on: Setting up a GitHub repository for DevOps with AWS

• Managing Git repositories effectively in GitHub
• Branching strategies and collaboration best practices
• Utilizing Pull Requests for code review and merge control
• Integrating CodeCommit with GitHub (optional)
• Hands-on: Creating a repository, pushing code, managing branches in GitHub

• Creating automated workflows for builds and tests within GitHub Actions
• Configuring build steps, dependencies, and environment variables
• Triggering workflows automatically on Git events (push, pull request)
• Integrating Actions with CodeBuild or other services
• Hands-on: Building a sample application with GitHub Actions

• Designing and building pipelines for deployment automation utilizing Actions
• Defining stages for code commit, build, test, and deploy using workflows
• Integrating Actions with CodePipeline, CodeDeploy, or other AWS services
• Managing approvals and deployments to various environments (staging, production)
• Hands-on: Creating a simple pipeline for code deployment with Actions and AWS

• Unit testing and integration testing within Actions workflows
• Utilizing AWS services like CodeBuild and Lambda for test execution
• Implementing test coverage and reporting within the pipeline
• Integration with CodePipeline for advanced testing scenarios
• Hands-on: Setting up basic unit tests and integrating them with the Actions pipelin

• Writing infrastructure templates in YAML or JSON and storing them in GitHub
• Defining resources like EC2 instances, VPCs, and security groups
• Utilizing Terraform
• Hands-on: Creating a basic Terraform template in GitHub and deploying it

• Implementing GitOps and feature flags using GitHub Actions
• Utilizing canary deployments and A/B testing for safe rollouts
• Leveraging blue-green deployments for controlled releases
• Hands-on: Implementing a canary deployment using Actions and AWS services

• Understanding containerization benefits and Docker basics
• Building and managing Docker containers within GitHub repositories
• Deploying containerized applications to Amazon ECS
• Scaling and managing containerized applications efficiently
• Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions

• Building serverless functions for event-driven applications in GitHub
• Utilizing triggers and scaling in Lambda functions or EKS
• Integrating Lambda with other services like S3 and DynamoDB
• Best practices for building and deploying serverless applications with GitHub
• Hands-on: Creating a simple Lambda function within GitHub and connecting it to S3

• Configuring CloudWatch alarms for metrics like CPU, memory, and network usage
• Visualizing performance metrics and dashboards
• Correlating logs and events with metrics for troubleshooting

• Collecting and ingesting application logs from various sources
• Filtering and analyzing logs for root cause analysis
• Integrating CloudWatch Logs with other services for further analysis
• Creating and routing events based on triggers (e.g., resource state changes, alarms)
• Integrating events with other AWS services for automated actions
• Implementing incident management workflow with CloudWatch Events

• Monitor a sample application with CloudWatch metrics, logs, and events
• Create alarms and automate actions based on monitoring data
• Simulate and troubleshoot application issues using CloudWatch tools

• Explain the concept of cloud computing.
• Discuss its benefits, such as scalability, flexibility, and cost-effectiveness.
• Highlight the differences between on-premises, cloud, and hybrid models.
• Explain the different cloud deployment models (public, private, hybrid).
• Explore the three main service models (IaaS, PaaS, SaaS) and their characteristics.

• Discuss the history and evolution of Google Cloud Platform.
• Highlight its key services and features.
• Introduce major GCP services, such as Compute Engine, Cloud Storage, Cloud SQL, Cloud Functions, and more.
• Provide examples of how these services are used in real-world applications.
• Hands-on Lab: Setting up a GCP project and navigating the console (30 min)

• Define virtual machines and their benefits.
• Compare VMs to physical machines.
• Guide participants through creating a VM instance in the console.
• Cover selecting machine type, configuring boot disk, and setting network access.
• Demonstrate connecting to the VM instance and managing its resources.
• Explain scaling options for VMs (up or down) based on resource needs.
• Introduce basic networking concepts in GCP and how VMs connect to networks.
• Hands-on Lab: Deploying and managing a VM (60 min)

• Object Storage Basics
• Uploading and Downloading Data
• Access Control and Lifecycle Management
• Hands-on Lab: Uploading and downloading data to different storage classes (45 min)
  

• Introduction to Cloud SQL
• Choosing the Right Instance Type
• Creating and Managing Cloud SQL Instances
• Exploring Cloud SQL features
• Hands-on Lab: Creating a Cloud SQL database instance and connecting to it from an application

• Introduction to VPC
• Managing VPC and Firewall
• Deploying Firewall and Network Tags
• Best practices of GCP Networking

• Using Cloud Monitoring
• Understanding GCP Billing

• Security Best Practices in GCP
• Understanding IAM
• GCP Compliance Offerings
• Hands-on: Building and deploying a Dockerized application to ECS using GitHub Actions

• Introduction to cloud computing concepts and benefits
• AWS overview and key services relevant to migration
• Understanding migration drivers, challenges, and success factors
• Identifying different migration strategies (rehost, refactor, replatform, rebuild)

• Hands-on labs: Creating an AWS account, managing IAM roles and users
• S3 object storage for data migration and archiving
• EC2 instances for application hosting and scaling
• VPCs for creating secure and isolated network environment

• Techniques for evaluating application dependencies and architecture
• Identifying potential migration risks and mitigation strategies
• Tools for dependency analysis and code refactoring

• Developing a migration roadmap and timeline
• Estimating migration costs using AWS pricing tools
• Risk mitigation strategies and rollback plans
  

• Code refactoring for cloud-native architecture
• Database migration strategies and tools
• Containerization with Docker and Amazon ECS

• CloudWatch, CloudTrail, and other monitoring tools for performance and cost insights
• Optimization techniques for cost savings and resource efficiency

• IAM policies and access control mechanisms
• Security considerations for data encryption and network security
• AWS Shield for DDoS protection

• Key concepts and benefits of cloud computing
• Differentiating between public, private, and hybrid clouds
• Introduction to AWS as a public cloud provider

• Core AWS components: regions, Availability Zones, and security groups
• Understanding VPCs (Virtual Private Clouds) and subnets
• AWS Identity and Access Management (IAM) basics

• Amazon EC2: On-demand virtual servers (instances) with diverse configurations (t2 Micro, c4 Large, etc.)
• Amazon Lambda: Serverless compute service for running code without managing servers
• Amazon ECS & Amazon EKS: Container orchestration platforms for managing containerized applications
• Hands-on: Launching an EC2 instance and exploring its console
• Hands-on: Creating a simple Lambda function

• Amazon S3: Object storage for any type of data, scalable and durable
• Amazon EBS: Block storage for EC2 instances, providing persistent volumes
• Amazon RDS: Managed relational database service (MySQL, PostgreSQL, etc.)
• Amazon DynamoDB: NoSQL database for flexible, scalable data storage
• Containerization with Docker and Amazon ECS
  

• VPC (Virtual Private Cloud) creation and management
• Subnets and network security groups
• Internet Gateways and NAT Gateways for outbound traffic
• Route 53 for domain name registration and DNS management

• IAM basics: Access policies, roles, and users
• AWS Security Groups and network access control
• Amazon Cognito: User authentication and authorization
• Amazon Key Management Service (KMS): Secure storage and management of encryption keys

• Amazon CloudWatch for monitoring metrics and logs
• CloudTrail for auditing AWS API calls
• Amazon CloudFormation for infrastructure as code (IaC)
• Hands-on: CloudWatch monitoring of an EC2 instance CPU usage

• Building serverless functions for event-driven applications in GitHub
• Utilizing triggers and scaling in Lambda functions or EKS
• Integrating Lambda with other services like S3 and DynamoDB
• Best practices for building and deploying serverless applications in AWS
• Hands-on: Creating a simple Lambda function  and connecting it to S3

• Configuring CloudWatch alarms for metrics like CPU, memory, and network usage
• Visualizing performance metrics and dashboards
• Correlating logs and events with metrics for troubleshooting

• Collecting and ingesting application logs from various sources
• Filtering and analyzing logs for root cause analysis
• Integrating CloudWatch Logs with other services for further analysis
• Creating and routing events based on triggers (e.g., resource state changes, alarms)
• Integrating events with other AWS services for automated actions
• Implementing incident management workflow with CloudWatch Events

• Monitor a sample application with CloudWatch metrics, logs, and events
• Create alarms and automate actions based on monitoring data
• Simulate and troubleshoot application issues using CloudWatch tools

• Introduction to network architectures and topologies
• Understanding the OSI model and its layers
• Deep dive into TCP/IP protocol suite and its functionalities
• Practical demonstration of network communication using tools like ping and traceroute

• Defining security threats and vulnerabilities in network environments
• Understanding common attack vectors and techniques
• Introduction to network security controls like firewalls, intrusion detection/prevention systems (IDS/IPS), and access control lists (ACLs)

• Importance of encryption and its role in secure communication
• Understanding and configuring secure protocols like HTTPS and VPN
• Practical hands-on with tools like Wireshark to analyze network traffic and identify potential security issues

• Introduction to different types of databases and their security considerations
• Understanding the concepts of data integrity, confidentiality, and availability
• Identifying common database vulnerabilities like SQL injection and cross-site scripting (XSS)

• Implementing user authentication and authorization mechanisms for database access
• Understanding the principles of least privilege and role-based access control (RBAC)
• Practical demonstration of user management and access control techniques

• Importance of data encryption at rest and in transit for secure storage and transmission
• Understanding and implementing encryption technologies like AES and RSA
• Exploring best practices for data security, including backups, disaster recovery, and vulnerability management

• Detailed exploration of common network attacks like denial-of-service (DoS), man-in-the-middle (MitM), and malware attacks
• Understanding and implementing mitigation strategies for different types of network threats
• Practical exercises using tools like nmap and Metasploit to simulate and analyze security vulnerabilities

• Exploring advanced security features like intrusion detection/prevention systems (IDS/IPS) for databases
• Understanding data masking and tokenization techniques for sensitive information protection
• Implementing security measures for database auditing and logging

• Understanding the importance of incident response planning and procedures
• Learning basic forensics techniques for identifying and analyzing security incidents
• Practical exercises simulating incident response scenarios and practicing evidence collection and analysis

• Exploring popular open-source and commercial tools for network and database security assessments and monitoring
• Understanding the role of security frameworks and standards like NIST Cybersecurity Framework and ISO 27001
• Discussing best practices for staying updated on the latest security threats and vulnerabilities

• Analyzing real-world case studies of successful network breaches and database security incidents
• Learning from the mistakes of others and discussing strategies to avoid similar vulnerabilities
• Group discussions and brainstorming solutions to address emerging security challenges

• What is vulnerability assessment?
• Why is vulnerability assessment important?
• Benefits of conducting vulnerability assessments
• Different types of vulnerability assessments

• Common network vulnerabilities
• Server and web application vulnerabilities
• Remote service vulnerabilities
• Understanding CVSS scoring system

• Using Nmap and Nessus for network scanning
• Identifying and prioritizing network vulnerabilities
• Conducting network security assessments

• Using Nessus and OWASP ZAP for server and web application scanning
• Identifying and prioritizing server and web application vulnerabilities
• Conducting web application security assessments

• Assessing vulnerabilities in VPN services
• Identifying and prioritizing remote service vulnerabilities
• Conducting remote service security assessments

• Nmap
• Nessus
• OWASP ZAP
• Other industry-standard tools

• Analyzing vulnerability assessment results
• Prioritizing remediation efforts
• Generating vulnerability reports

• Scheduling regular vulnerability assessments
• Integrating vulnerability assessment into the security lifecycle
• Maintaining a vulnerability management program