Cloud Engineering

Inquire now

Cloud Engineering equips IT professionals with the knowledge and practical skills to design, deploy, automate, secure, and manage scalable cloud infrastructure and cloud-native applications.

 

Duration 5 Days – 35 hrs.

 

Overview

Cloud computing has become the foundation of modern IT infrastructure, enabling organizations to build scalable, secure, resilient, and cost-effective solutions. Cloud Engineers play a critical role in designing, deploying, automating, securing, and managing cloud environments that support digital transformation and business innovation.

This Cloud Engineering Training Course provides participants with a comprehensive understanding of cloud computing concepts, cloud architecture, networking, virtualization, storage, compute services, identity and access management, security, automation, monitoring, and cloud operations.

The course introduces cloud engineering best practices that are applicable across major cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). Participants will gain practical knowledge of cloud infrastructure design, deployment strategies, Infrastructure as Code (IaC), containers, DevOps integration, and cloud governance.

Through instructor-led discussions, demonstrations, hands-on laboratories, and real-world case studies, participants will build the foundational skills required to support, deploy, and manage enterprise cloud environments.

 

Objectives

  • Understand core cloud computing concepts, deployment models, and service models.
  • Explain cloud architecture principles and design considerations.
  • Deploy and manage cloud resources using industry best practices.
  • Configure networking, storage, compute, and identity services in cloud environments.
  • Implement cloud security controls and governance practices.
  • Understand virtualization, containers, and orchestration technologies.
  • Apply Infrastructure as Code (IaC) concepts to automate cloud deployments.
  • Monitor cloud environments for availability, performance, and cost optimization.
  • Integrate cloud engineering practices with DevOps methodologies.
  • Apply cloud engineering best practices to support scalable, secure, and resilient enterprise solutions.

 

Target Audience

  • Cloud Engineers
  • Systems Administrators
  • Network Engineers
  • Infrastructure Engineers
  • DevOps Engineers
  • Site Reliability Engineers (SRE)
  • Systems Engineers
  • IT Operations Professionals
  • Software Developers
  • Cloud Support Engineers
  • Application Support Engineers
  • Technical Consultants
  • IT Project Team Members
  • Fresh Graduates entering Cloud or Infrastructure roles

 

Prerequisites

 

  • Basic understanding of computer networking.
  • Basic knowledge of operating systems (Windows or Linux).
  • Familiarity with IT infrastructure concepts.
  • Basic command-line experience is recommended.
  • Prior cloud experience is beneficial but not required.

Course Outline

 

Day 1 – Cloud Computing Foundations

 

Module 1 – Introduction to Cloud Computing

  • Evolution of Cloud Computing
  • Cloud Computing Concepts
  • Benefits and Business Value
  • Cloud Characteristics
  • Shared Responsibility Model

 

Module 2 – Cloud Service Models

  • Infrastructure as a Service (IaaS)
  • Platform as a Service (PaaS)
  • Software as a Service (SaaS)
  • Function as a Service (FaaS)
  • Serverless Computing Concepts

 

Module 3 – Cloud Deployment Models

  • Public Cloud
  • Private Cloud
  • Hybrid Cloud
  • Multi-Cloud
  • Community Cloud

 

Module 4 – Cloud Architecture Fundamentals

  • Regions and Availability Zones
  • High Availability
  • Fault Tolerance
  • Scalability
  • Elasticity
  • Disaster Recovery Concepts

Workshop:  Design a basic cloud architecture for a business application, selecting appropriate deployment and service models.

 

Day 2 – Cloud Infrastructure and Networking

 

Module 5 – Compute Services

  • Virtual Machines
  • Virtual Networks
  • Auto Scaling Concepts
  • Load Balancing
  • Compute Optimization

 

Module 6 – Cloud Storage

  • Object Storage
  • Block Storage
  • File Storage
  • Backup and Recovery
  • Data Lifecycle Management

 

Module 7 – Cloud Networking

  • Virtual Private Cloud (VPC/VNet)
  • Subnets
  • Routing
  • DNS Services
  • VPN Connectivity
  • Firewalls
  • Security Groups
  • Network Access Control

 

Module 8 – Identity and Access Management (IAM)

  • Authentication
  • Authorization
  • Role-Based Access Control (RBAC)
  • Multi-Factor Authentication (MFA)
  • Least Privilege Principle
  • Identity Federation

 

Workshop:  Configure a secure cloud network architecture with identity and access controls.

 

Day 3 – Cloud Security, Monitoring, and Governance

 

Module 9 – Cloud Security Fundamentals

  • Cloud Security Principles
  • Data Protection
  • Encryption at Rest and in Transit
  • Key Management
  • Secrets Management
  • Security Monitoring

 

Module 10 – Governance and Compliance

  • Cloud Governance
  • Resource Management
  • Tagging Strategies
  • Cost Management
  • Compliance Frameworks
  • Cloud Policies

 

Module 11 – Monitoring and Logging

  • Performance Monitoring
  • Log Management
  • Alerts and Notifications
  • Health Monitoring
  • Resource Optimization
  • Incident Response

 

Module 12 – Backup and Disaster Recovery

  • Backup Strategies
  • Replication
  • Business Continuity
  • Recovery Objectives (RPO/RTO)
  • Disaster Recovery Planning

Workshop:  Develop a cloud monitoring, governance, and disaster recovery plan for a production environment.

 

Day 4 – Automation, Containers, and DevOps

 

Module 13 – Infrastructure as Code (IaC)

  • Infrastructure Automation Concepts
  • Declarative vs Imperative Approaches
  • Infrastructure Templates
  • Version Control Integration
  • Configuration Management

 

Module 14 – Containers

  • Container Fundamentals
  • Docker Concepts
  • Container Images
  • Registries
  • Container Security
  • Container Networking

 

Module 15 – Container Orchestration

  • Kubernetes Fundamentals
  • Pods
  • Services
  • Deployments
  • Scaling
  • High Availability

 

Module 16 – DevOps Integration

  • Continuous Integration (CI)
  • Continuous Delivery (CD)
  • CI/CD Pipelines
  • DevSecOps Concepts
  • Automation Best Practices

Workshop:  Design an automated cloud deployment workflow using Infrastructure as Code and CI/CD concepts.

 

Day 5 – Cloud Operations and Enterprise Best Practices

 

Module 17 – Cloud Operations

  • Provisioning Resources
  • Capacity Planning
  • Performance Optimization
  • Operational Excellence
  • Service Reliability

 

Module 18 – Cloud Cost Optimization

  • Cost Monitoring
  • Resource Right-Sizing
  • Reserved Capacity
  • Auto Scaling Strategies
  • Budget Management

 

Module 19 – Enterprise Cloud Architecture

  • Well-Architected Framework Principles
  • Multi-Tier Applications
  • High Availability Design
  • Resilient Architectures
  • Migration Strategies

 

Module 20 – Emerging Trends in Cloud Engineering

  • Artificial Intelligence for Cloud Operations (AIOps)
  • Edge Computing
  • Hybrid Cloud Operations
  • Cloud-Native Applications
  • Microservices Architecture
  • Platform Engineering
  • FinOps Fundamentals
  • Sustainable Cloud Computing

 

Capstone Workshop

Participants will work in teams to design and present a secure, scalable, and highly available cloud solution for a business application, including:

  • Cloud Architecture Design
  • Service Model Selection
  • Network Topology
  • Compute and Storage Design
  • Identity and Access Management
  • Security Controls
  • Monitoring and Logging Strategy
  • Backup and Disaster Recovery Plan
  • Infrastructure Automation Approach
  • Cost Optimization Recommendations

Inquire now

Best selling courses

The Data Analytics with DAX, Python, SQL, and Business Intelligence Tools Training Course provides participants with practical knowledge and skills for collecting, preparing, analyzing, modeling, and visualizing data to support informed business decision-making. The course combines key data analytics technologies and techniques, including Python, SQL, DAX (Data Analysis Expressions), Power BI, data modeling, data transformation,...

The Project Management – SDLC (Software Project Management: The Complete Course) Training Course is a comprehensive program designed to equip project managers, software development leaders, business analysts, product owners, and IT professionals with the knowledge and practical skills required to successfully manage software development projects throughout the entire Software Development Life Cycle (SDLC). This course...

Warehouse Operations and Inventory Management Training Course     Duration 2 Days – 14 hrs.   Overview   This course provides a practical understanding of warehouse operations and inventory management for personnel who supervise, review, or audit warehouse processes. It covers the movement of goods from receiving through dispatch, with emphasis on warehouse layout, storage systems, picking strategies, stock rotation, inventory accuracy, and operational controls.   Participants will learn to identify process weaknesses, investigate stock discrepancies, reduce shrinkage, and interpret warehouse productivity indicators. The course connects day-to-day warehouse activities with the records, controls, and performance measures needed for effective supervision and auditing.   Objectives  

  • Explain warehouse processes and their impact on inventory accuracy and service performance.
  • Evaluate warehouse layouts, storage systems, and product locations.
  • Identify appropriate controls for receiving, put-away, storage, picking, and dispatch.
  • Apply First-In, First-Out (FIFO) and First-Expired, First-Out (FEFO) principles.
  • Compare picking strategies based on order characteristics and operational requirements.
  • Identify causes of inventory discrepancies and recommend corrective actions.
  • Develop a cycle counting approach based on inventory value, movement, and risk.
  • Recognize sources of shrinkage and recommend preventive and detective controls.
  • Interpret warehouse productivity and inventory performance KPIs.
  • Review warehouse processes using supporting records, observations, and control evidence.
        Target Audience  
  • Warehouse supervisors, team leaders, and managers.
  • Inventory controllers and stock control personnel.
  • Logistics and distribution supervisors.
  • Internal auditors and operational compliance personnel.
  • Supply chain and operations professionals.
  • Personnel responsible for reviewing warehouse controls and performance.
  Prerequisites  
  • Basic familiarity with warehouse, logistics, or inventory activities.
  • General understanding of business processes and operational documentation.
  • Basic numerical skills, including percentages and simple calculations.
  • Basic spreadsheet skills are helpful.
  • No prior experience with a specific Warehouse Management System (WMS) is required.
   

Course Outline

  Day 1: Warehouse Processes, Layout, and Storage Controls Module 1: Warehouse Operations and Process Controls  
  • Warehouse roles within the supply chain.
  • End-to-end flow: receiving, put-away, storage, replenishment, picking, packing, and dispatch.
  • Physical stock movement and corresponding system transactions.
  • Responsibilities, authorization, and segregation of duties.
  • Common operational weaknesses and audit evidence.
  Module 2: Warehouse Layout and Storage Systems  
  • Functional zones, movement paths, and space utilization.
  • Receiving, storage, picking, staging, and dispatch areas.
  • Storage options: floor stacking, shelving, pallet racking, and bin storage.
  • Fixed and dynamic location systems.
  • Product slotting based on movement, size, handling needs, and compatibility.
  • Layout bottlenecks and their effects on productivity and inventory control.
  Module 3: Receiving and Put-Away  
  • Purchase orders, delivery documents, and expected receipts.
  • Quantity, condition, and item identification checks.
  • Recording shortages, overages, damage, and discrepancies.
  • Controls for acceptance, quarantine, and release.
  • Put-away rules and location confirmation.
  • Traceability through labels, barcodes, lot numbers, and serial numbers.
  • Timely recording of receipts and stock movements.
  Module 4: Storage, Stock Rotation, and Replenishment  
  • Storage discipline and location integrity.
  • FIFO and FEFO principles, applications, and limitations.
  • Batch, lot, and expiry-date management.
  • Controls for slow-moving, obsolete, damaged, and expired stock.
  • Replenishment of picking locations.
  • Supervision and review of stock rotation compliance.
    Day 2: Order Fulfillment and Inventory Accuracy Module 5: Picking Strategies and Order Fulfillment  
  • Discrete, batch, zone, and wave picking.
  • Selecting picking strategies for different order profiles.
  • Pick paths, location sequencing, and travel reduction.
  • Replenishment coordination and stock availability.
  • Picking verification and error prevention.
  • Packing, staging, and dispatch confirmation.
  • Handling short picks, substitutions, cancellations, and returns.
  Module 6: Inventory Records and Accuracy Controls  
  • Item master data, units of measure, and location records.
  • On-hand, available, allocated, and quarantined stock.
  • Transaction controls for receipts, transfers, issues, returns, and adjustments.
  • Common causes of physical and system stock mismatches.
  • Barcode scanning and WMS control points.
  • Inventory reconciliation and supporting documentation.
  • Measuring inventory accuracy and interpreting discrepancies.
  Module 7: Cycle Counting and Physical Inventory  
  • Cycle counting versus full physical inventory.
  • ABC classification and risk-based count priorities.
  • Count frequency based on value, movement, and discrepancy history.
  • Count preparation, blind counting, and movement controls.
  • Recounts, variance investigation, and adjustment approval.
  • Root cause analysis and corrective action follow-up.
  • Reviewing the effectiveness of the counting program.
  Module 8: Shrinkage Reduction and Inventory Protection  
  • Sources of shrinkage: loss, damage, theft, misplacement, and recording errors.
  • Access controls and accountability for stock movements.
  • Controls for high-value and sensitive inventory.
  • Returns, write-offs, scrap, and disposal controls.
  • Exception reporting and unusual transaction patterns.
  • Investigating recurring losses and unexplained adjustments.
  • Preventive and detective controls for shrinkage reduction.
    Day 3: Performance Management, Process Review, and Improvement Module 9: Warehouse Productivity and Inventory KPIs  
  • Receiving productivity and dock-to-stock time.
  • Put-away productivity and replenishment performance.
  • Picking productivity and order accuracy.
  • Inventory accuracy and cycle count variance.
  • Space utilization and storage capacity.
  • Shrinkage, damage, and expiry indicators.
  • Defining KPI formulas, data sources, and reporting boundaries.
  • Interpreting trends while balancing speed, accuracy, and service.
      Module 10: Supervising and Auditing Warehouse Processes  
  • Process mapping and identification of critical control points.
  • Reviewing procedures against actual warehouse practices.
  • Tracing transactions from documents to systems and physical stock.
  • Reviewing approvals, access rights, and segregation of duties.
  • Identifying control gaps and recurring process exceptions.
  • Documenting findings with clear supporting evidence.
  • Prioritizing corrective actions and tracking closure.
  Module 11: Warehouse Improvement Planning  
  • Identifying bottlenecks, unnecessary movement, and rework.
  • Improving layout, slotting, and picking arrangements.
  • Addressing inventory discrepancy root causes.
  • Standardizing transaction recording and exception handling.
  • Assigning improvement owners and measurable targets.
  • Monitoring results and sustaining operational improvements.
  Module 12: Capstone — Warehouse Operations and Inventory Improvement Plan  
  • Review a warehouse scenario containing layout, transaction, inventory, and performance information.
  • Identify weaknesses across receiving, put-away, storage, picking, and dispatch.
  • Recommend appropriate FIFO or FEFO controls.
  • Develop a cycle counting and inventory reconciliation plan.
  • Propose controls to reduce shrinkage and improve traceability.
  • Select relevant KPIs and define their calculation and reporting requirements.
  • Produce a prioritized improvement plan with responsibilities and expected outcomes.
 

This course develops the foundational knowledge and practical skills needed to configure, administer, and maintain the ServiceNow platform. Participants explore platform navigation, instance configuration, application collaboration, self-service, automation, database management, security, and configuration migration. The proposed five-day duration accommodates foundational learning, configuration activities, and an integrated capstone. Duration 5 Days – 35 hrs.   Objectives...

Duration: 5 days – 35 hrs   Overview The “SOC Network and Threat Detection and Analysis” training course is designed to equip Security Operations Center (SOC) analysts and IT security professionals with the skills and knowledge required to detect, analyze, and respond to network threats effectively. This comprehensive course covers essential topics such as threat...

Duration 1 day – 7 hrs   Overview   This 1-day training builds upon basic warehouse operations knowledge and introduces key logistics concepts involved in the movement and coordination of goods—especially wet and dry food items—within and outside the warehouse. Participants will explore transport logistics, inbound and outbound coordination, documentation practices, and cold chain considerations,...

Duration 2 days – 14 hrs   Overview   This hands-on course provides an introduction to Splunk, a powerful platform for searching, monitoring, and analyzing machine-generated data. The training focuses on how developers and QA professionals can leverage Splunk to gain insights from logs and metrics, improve application observability, detect anomalies, and support test validation....

Duration 3 days – 21 hrs   Overview.   This course is designed for fresh graduates aspiring to build a career in Data Science. It introduces the fundamentals of data science, focusing on data analysis, visualization, and basic machine learning concepts using Python. The course provides hands-on practice with real-world datasets, equipping participants with the...

// BG EARTH WHEN NOT PLAYING

We use cookies on our website to personalize your experience by storing your preferences and recognizing repeat visits. By clicking “Accept”, you agree to the use of all cookies. You can also select “Cookie Settings” to adjust your preferences and provide more specific consent. Cookie Policy