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Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer

⚡ Powered by AI-Driven Cloud & Automation

Seamlessly work across AWS, Azure, Kubernetes, and multi-cloud environments.

Hands-on training with Terraform, Docker, Kubernetes, CI/CD, and Infrastructure Automation.

Build real-world DevOps, MLOps, LLMOps and AI deployment pipelines beyond basic projects.

Learn EKS (AWS), AKS (Azure), AI orchestration, monitoring, and scalable cloud operations.

Group Enrollment with Friends or Colleagues
Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer

Course Duration

600 Hours

Next Batch

23 May 2026

Course Material

Live. Online. Interactive.

Stay ahead with AI Infrastructure, Data Versioning, MLOps, AIOps, and LLMOps technologies.

Specialized sessions to connect Cloud, DevOps, AI, and automation concepts effectively.

Focus on practical multi-cloud architecture and real-world automation using production-grade DevOps tools.

Enhance career opportunities in roles like Cloud Architect, DevOps Engineer, MLOps Engineer, and AI Infrastructure Specialist.

Highlight Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer

KEY HIGHLIGHTS OF ADVANCED MULTI-CLOUD DEVOPS, MLOPS AIOPS & LLMOPS ENGINEER PROGRAM

  • Weekly sessions with industry professionals
  • Dedicated Learning Management Team
  • 600+ hours of hands-on learning experience
  • Over 150 hours of live sessions for real-time interaction
  • Specialized Bridge Classes for smooth AWS-to-Azure transitions
  • Learn from Cloud Certified Industry Experts
  • More than 20+ industry-related projects and case studies
  • Personalized mentorship sessions with cloud experts
  • 24*7 Support
  • 1:1 Mock Interviews & Portfolio Building
  • Designed for both working professionals and fresh graduates
  • No-Cost EMI Option available
  • High Demand Skillset with Global Career Opportunities
  • Mastery of GitOps, IaC, and AI-Infrastructure

WHY JOIN ADVANCED MULTI-CLOUD DEVOPS, MLOPS AIOPS & LLMOPS ENGINEER PROGRAM?

Comprehensive Cloud Training

Gain expertise in AWS, Azure, multi-cloud architecture, DevOps, and AI-driven infrastructure systems.

Hands-On Automation

Work on real-world projects involving CI/CD, Infrastructure as Code (IaC), Kubernetes, and cloud automation.

Industry-Relevant Curriculum

Learn modern DevOps, GitOps, MLOps, AIOps, and LLMOps tools used in production environments.

Career Growth

Prepare for high-demand roles in Cloud Engineering, DevOps, MLOps, AI Infrastructure, and automation domains.

The Multi-Cloud Advantage

Build expertise in both AWS and Azure to manage scalable and enterprise-grade multi-cloud environments.

Production-Ready DevOps

Go beyond basic deployment pipelines with Kubernetes, GitOps, observability, and Infrastructure Automation.

Next-Gen Tech

Learn MLOps, AIOps, LLMOps, AI Infrastructure, and intelligent cloud operations aligned with future industry demands.

Zero-to-Hero Labs

Every module includes practical labs, real-world deployment tasks, and hands-on cloud implementation experience.

UPCOMING BATCH:

23 May 2026

SkillzRevo

SkillzRevo Solutions

30 MINUTE MEETING

Web conferencing details provided upon confirmation.

Corporate Training, Enterprise training for teams

Batch schedule

BatchBatch Type
Online Live Instructor Led SessionFull-Time
Online Live Instructor Led SessionPart-Time

Regional Timings

BatchBatch Type
IST (India Standard Time)09:00 PM–12:00 AM
Bahrain, Qatar, Kuwait, Saudi Arabia06:30 PM–09:30 PM
UAE / Oman07:30 PM–09:00 PM

Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer OVERVIEW

This comprehensive program is designed to build strong cloud fundamentals, hands-on administration skills, and production-ready expertise across AWS, Microsoft Azure, DevOps, MLOps, AIOps, and LLMOps practices. The curriculum follows an industry-aligned, role-based learning path covering cloud architecture, security, networking, automation, CI/CD, containers, Kubernetes, observability, AI infrastructure, and intelligent cloud operations. The program blends conceptual clarity, real-world scenarios, practical labs, and architecture-driven thinking, enabling learners to confidently design, automate, deploy, and manage modern cloud-native and hybrid environments.

ENROLL NOW, BOOK YOUR SEAT & AVAIL UPTO 30% FEE WAIVER

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Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer Objectives

The primary objective of this program is to develop industry-ready Cloud, DevOps, and MLOps Engineers with skills that go beyond basic tools and certifications. The program focuses on practical, job-oriented learning, enabling learners to work confidently in real enterprise cloud environments. It provides hands-on exposure to AWS, Microsoft Azure, DevOps, Kubernetes, MLOps, AIOps, and Infrastructure Automation, covering how modern cloud platforms are designed, secured, automated, monitored, and scaled in production. Being “JOB-READY” in this program means learners understand when, why, and how to implement cloud and DevOps technologies based on real business requirements. Through practical labs, deployment workflows, and production-style scenarios, learners build strong execution and problem-solving skills, preparing them for roles such as Cloud Engineer, DevOps Engineer, SRE, Platform Engineer, and MLOps professional.

Enroll Now →

Why Learn Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer ?

Industry-Focused Learning

Learn practical skills based on real enterprise cloud, DevOps, and automation environments.

Multi-Cloud Expertise (AWS & Azure)

Gain hands-on experience with the most widely used cloud platforms and services.

End-to-End DevOps Skills

Understand the complete DevOps lifecycle from development, deployment, automation, and monitoring.

Strong Automation & IaC Foundation

Master Infrastructure as Code using Terraform, ARM templates, and cloud automation tools.

Containers & Kubernetes at Scale

Build, deploy, and manage containerized applications using Docker, Kubernetes, EKS, and AKS.

Production Monitoring & Reliability

Learn observability, logging, monitoring, troubleshooting, and production system reliability practices.

Serverless, AI Infrastructure & MLOps

Gain exposure to serverless computing, MLOps workflows, AIOps, and AI infrastructure concepts.

Role-Based Career Preparation

Prepare for Cloud Engineer, DevOps Engineer, SRE, Platform Engineer, and MLOps roles.

Program Advantages

Industry-driven curriculum aligned with current cloud, DevOps, MLOps, and automation industry standards.

Job-ready learning focused on real-world cloud operations and production DevOps practices.

Hands-on experience across AWS, Microsoft Azure, Kubernetes, and multi-cloud environments.

End-to-end training covering cloud fundamentals, DevOps, CI/CD, Kubernetes, and MLOps workflows.

Practical exposure to production-grade architectures, monitoring, automation, and deployment practices.

Strong focus on Infrastructure as Code, GitOps, CI/CD pipelines, and cloud automation tools.

Learn modern industry-standard technologies used in real enterprise DevOps and cloud teams.

Curriculum designed for roles such as Cloud Engineer, DevOps Engineer, SRE, and Platform Engineer.

Future-ready skill development with serverless computing, AI infrastructure, MLOps, and AIOps concepts.

Description

Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer program Certifications

Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer Curriculum

Lecture 1: AWS Well-Architected Framework - Introduction, Six pillars (Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, Sustainability)
Lecture 2: Cloud Economics & Cost Optimization - Fixed cost vs variable cost, CapEx vs OpEx, Rightsizing, Automation benefits, Managed services vs self-managed
Lecture 3: Migration to AWS & AWS CAF - Cloud migration benefits, AWS Cloud Adoption Framework (CAF), Migration strategies (6 Rs overview), AWS Snow Family, AWS Migration Hub
Lecture 4: AWS Shared Responsibility Model - AWS responsibility vs Customer responsibility, Responsibility shift by service (EC2 vs RDS vs Lambda)
Lecture 5: AWS Security Services Overview - Encryption, AWS Artifact, Compliance, Logging & Monitoring (CloudTrail, CloudWatch, AWS Config, Audit Manager)
Lecture 6: Identity & Access Management - IAM users, groups, roles, Policies, Least privilege, Root protection, MFA & Federation
Lecture 7: Network & Application Security - Security Groups vs NACLs, AWS WAF, AWS Shield, GuardDuty, Inspector, Security Hub, Trusted Advisor
Lecture 8: AWS Compute Services - Amazon EC2, Instance types, Auto Scaling, Load Balancers, AWS Lambda, Elastic Beanstalk, Lightsail
Lecture 9: Containers & Serverless - Amazon ECS, Amazon EKS, Serverless benefits, Containers vs Lambda
Lecture 10: AWS Storage Services - Amazon S3 & classes, Lifecycle policies, EBS, EFS, FSx, AWS Backup, Storage Gateway
Lecture 11: AWS Database Services - RDS, Aurora, DynamoDB, In-memory, Migration tools (DMS, SCT)
Lecture 12: AWS Networking Services - Amazon VPC, Subnets, Route tables, Route 53, VPN, Direct Connect, CloudFront, Global Accelerator
Lecture 13: Analytics Services - Amazon Athena, AWS Glue, Amazon Kinesis, Amazon QuickSight, Amazon Redshift
Lecture 14: AWS Pricing Models - On-Demand, Reserved Instances, Spot, Savings Plans, Data transfer, Storage pricing
Lecture 15: Cost Management & AWS Support - AWS Budgets, Cost Explorer, Pricing Calculator, Organizations, Support plans, Marketplace, Partner Network
Lecture 16: Governance & Subscriptions - Azure Subscriptions & Management Groups, Resource Locks, Tags and Cost Tracking, Azure Policy
Lecture 17: Role-Based Access Control (RBAC) - Azure RBAC Concepts, Built-in vs Custom Roles, Scope Management, Best Practices for Access Control
Lecture 18: Azure Administration Tools - Azure Resource Manager (ARM), Azure Portal, Cloud Shell, PowerShell, Azure CLI
Lecture 19: ARM Templates & Automation - ARM Templates Structure, Parameters & Variables, Deployment Modes, Infrastructure as Code Benefits
Lecture 20: Virtual Networking Fundamentals - Azure VNets, Subnets & IP Addressing, Public vs Private IPs, Network Security Groups (NSGs)
Lecture 21: Advanced Networking & DNS - Azure Firewall, Azure DNS, Private vs Public DNS Zones, Network Security Design Patterns
Lecture 22: Intersite Connectivity - VNet Peering, VPN Gateways, Site-to-Site & Point-to-Site VPNs, ExpressRoute & Virtual WAN
Lecture 23: Network Traffic Management - Network Routing & Service Endpoints, Azure Load Balancer (L4), Application Gateway (L7), Traffic Manager
Lecture 24: Designing Azure Network Solutions - Choosing Correct Networking Solutions, High Availability Networking, Network Security Scenarios
Lecture 25: Azure Storage Fundamentals - Storage Accounts, Blob Storage & Containers, Storage Tiers, Storage Security (Keys, SAS, Encryption)
Lecture 26: Azure Files, File Sync & Storage Tools - Azure Files, Azure File Sync, Storage Explorer, Backup and Recovery Basics
Lecture 27: Azure Virtual Machines - VM Planning & Sizing, Creating VMs, Availability Sets & Zones, Virtual Machine Scale Sets (VMSS)
Lecture 28: VM Management & Extensions - VM Extensions, Custom Script Extension, Monitoring VMs, VM Backup Overview
Lecture 29: Serverless & Containers - Azure App Service Plans, Web Apps, Azure Container Instances (ACI), Azure Kubernetes Service (AKS)
Lecture 30: Data Protection & Backup - Azure Backup, Recovery Services Vault, File & Folder Backups, VM Backup & Restore
Lecture 31: Monitoring Azure Infrastructure - Azure Monitor, Metrics & Logs, Azure Alerts, Log Analytics Workspace, Network Watcher
Lecture 32: End-to-End Azure Administration Scenarios - Identity → Networking → Compute → Storage Flow, Real-World Azure Admin Scenarios, Architecture Mapping
Lecture 33: Numerical Computing with Numpy - Fundamental package for scientific computing with Python.
Lecture 34: Data Manipulation with Pandas - High-performance data structures and data analysis tools.
Lecture 35: Core ML Libraries with scikit-learn | XGBoost - Standard libraries for classical machine learning and gradient boosting.
Lecture 36: Serialization & Config with joblib | pyyaml - Tools for saving model artifacts and managing YAML configuration files.
Lecture 37: Linux & Networking with Linux Foundations | Basic HTTP | DNS | Rest APIs - Essential OS skills and understanding of web communication protocols.
Lecture 38: Version Control with Git & GitHub - Collaborative coding and source code management.
Lecture 39: Testing & Code Quality with pytest - Framework for writing and running unit tests for ML code.
Lecture 40: Experiment Tracking with MLflow | Weights & Biases - Tools to log parameters | metrics | and manage the model registry.
Lecture 41: Data & Model Versioning with DVC | LakeFS - Version control for large datasets and machine learning artifacts.
Lecture 42: Hyperparameter Tuning with optuna - Framework for automated hyperparameter optimization.
Lecture 43: API Development with FastAPI - Modern web framework for building APIs to serve model predictions.
Lecture 44: Containerization with Docker - Packaging applications into containers for consistent deployment.
Lecture 45: Scalable Serving with KServe | Tensorflow Serving - Advanced platforms for deploying and scaling ML models in production.
Lecture 46: Orchestration / Pipelines with Airflow | Kubeflow Pipelines (KFP DSL) | Argo Workflows - Tools to automate and schedule end-to-end ML workflows.
Lecture 47: CI/CD for MLOps with GitHub Actions | GitLab CI - Automating testing and deployment pipelines.
Lecture 48: Cloud Fundamentals with AWS | Azure | GCP - Major cloud platforms for hosting ML workloads.
Lecture 49: Platform & IaC with Kubernetes | Terraform | Pulumi | Crossplane - Container orchestration and Infrastructure as Code for environment management.
Lecture 50: Monitoring & Visualization with Prometheus | Grafana - Tools for tracking system health and real-time performance metrics.
Lecture 51: Observability & Logs with Fiddler | ELK | opensearch - Specialized tools for model performance monitoring and centralized logging.
Lecture 52: Infrastructure Context with CMDB | Topology Mapping - Understanding the relationships between physical and virtual assets.
Lecture 53: Observability Pillars with Prometheus | Grafana | ELK Stack | OpenSearch | Jaeger - Mastering the collection of Metrics | Logs | and Traces.
Lecture 54: Fundamentals with Supervised & Unsupervised Learning - Understanding the different types of learning models applicable to IT data.
Lecture 55: Key Algorithms with Isolation Forests | ARIMA | Prophet | LSTM - Specialized algorithms for Anomaly Detection and Time-Series Forecasting.
Lecture 56: Intelligent Logic with Event Correlation | Noise Reduction | Deduplication - Moving from static thresholds to pattern-based alerting and deduplication.
Lecture 57: Causal AI with Root Cause Analysis (RCA) - Using AI to identify the underlying cause of incidents rather than just symptoms.
Lecture 58: Data Ingestion with Fluentd | Logstash | Vector - Tools for high-performance data shipping from various sources.
Lecture 59: Stream Processing with Apache Kafka | Flink - Processing real-time operational data streams at scale.
Lecture 60: AIOps Platforms with Dynatrace | Datadog | Splunk ITSI | BigPanda - Enterprise platforms that provide out-of-the-box AIOps capabilities.
Lecture 61: Cloud-Native AI with AWS DevOps Guru | Azure Monitor | Google Cloud Operations - Cloud-specific AI tools for monitoring and optimization.
Lecture 62: IT Use Case Training with Log Clustering | Smart Alerting - Training models to group millions of log lines and suppress noisy alerts.
Lecture 63: Experiment Tracking with MLflow - Tracking model versions and performance in catching operational bugs.
Lecture 64: ITSM Integration with ServiceNow | Jira Service Management - Connecting AI insights to ticket management systems.
Lecture 65: Collaboration (ChatOps) with Slack | Microsoft Teams - Real-time delivery of AI insights to engineering teams.
Lecture 66: Event-Driven Automation with Ansible Rulebooks | StackStorm | Argo Events - Automating responses to specific events detected by AI.
Lecture 67: Closed-Loop Remediation with Self-Healing Scripts - Automatic execution of playbooks to resolve issues (e.g. restarting services).
Lecture 68: Scalability with Kubernetes | KEDA - Managing AIOps across global clusters and event-driven autoscaling.
Lecture 69: FinOps Integration with Cost Optimization Models - Using AI to predict and optimize cloud infrastructure spending.
Lecture 70: Prompt Engineering with Few-shot | Chain-of-Thought | ReAct - Mastering advanced techniques to guide LLM reasoning and output quality.
Lecture 71: Model Selection with Llama 3 | Mistral | GPT-4 | Gemini - Understanding the trade-offs between open-source and proprietary models.
Lecture 72: Vector Databases with Pinecone | Milvus | Weaviate | ChromaDB - Storing and retrieving high-dimensional embeddings for contextual data.
Lecture 73: Orchestration Frameworks with LangChain | LlamaIndex - Building complex chains that connect LLMs with external data sources.
Lecture 74: Fine-Tuning with LoRA | QLoRA | PEFT - Techniques for specializing models on domain data with minimal compute.
Lecture 75: Quantization with GGUF | EXL2 | AWQ - Reducing model size and memory requirements for faster inference.
Lecture 76: Semantic Evaluation with Ragas | DeepEval | Promptfoo - Using automated frameworks and "LLM-as-a-judge" to score outputs.
Lecture 77: Testing & Reliability with Deterministic Tests | Keyword Checks - Implementing standard software tests for non-deterministic model outputs.
Lecture 78: Inference Servers with vLLM | Text Generation Inference (TGI) | Ollama - High-throughput engines for serving LLMs in production environments.
Lecture 79: GPU Management with NVIDIA CUDA | Triton Inference Server - Optimizing GPU utilization and managing specialized hardware drivers.
Lecture 80: Observability & Tracing with LangSmith | Arize Phoenix - Debugging and visualizing the step-by-step execution of LLM chains.
Lecture 81: Prompt Versioning with LiteLLM | Portkey - Managing multiple model providers and versioning prompts as code.
Lecture 82: Safety & Compliance with NeMo Guardrails | Guardrails AI - Real-time filtering of inputs and outputs to prevent hallucinations and bias.
Lecture 83: Security with Prompt Injection Defense | PII Scrubbing - Protecting against adversarial attacks and ensuring data privacy.
Lecture 84: Cost & Usage with Token Tracking | Rate Limiting - Monitoring API consumption and managing infrastructure costs.
Lecture 85: Feedback Loops with Human-in-the-loop | Reinforcement Learning - Collecting user feedback to improve model performance over time.

Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer Skills Covered

AWS and Azure Cloud Fundamentals
Cloud Security and Identity Management
Infrastructure as Code (Terraform, ARM, IaC)
CI/CD Pipelines and Cloud Automation
Containerization with Docker
Kubernetes Orchestration and Cluster Management
Cloud Networking and Architecture Basics
Monitoring, Logging, and Observability
Production-Grade Troubleshooting and Cloud Operations
GitOps, DevOps, and Deployment Workflows
Serverless Computing Fundamentals
MLOps, AIOps, and AI Infrastructure Concepts

Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer Tools Covered

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Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer Program Benefits

Advanced Multi-Cloud DevOps, MLOps AIOps & LLMOps Engineer Program Benefits Illustration

CAREER OPPORTUNITIES AFTER THIS COURSE

Cloud DevOps Engineer Salary Range

Min

$700,000

Average

$1,200,000

Max

$2,200,000

Projects

MASTER CLOUD COMPUTING WITH REAL-WORLD PROJECTS

Comprehensive Multi-Cloud Deployment Experience

Industry-Aligned Advanced Scenarios

Build Enterprise-Grade Production Solutions

Cloud Infrastructure & Architecture
NO. OF PROJECTS: 8
DevOps & Automation
NO. OF PROJECTS: 7
Security, Compliance & FinOps
NO. OF PROJECTS: 5

Capstone Projects of this Program

Enterprise Multi-Cloud Infrastructure Deployment

Design and deploy enterprise-scale applications across AWS, Azure, and GCP using Infrastructure as Code with advanced networking and security configurations.

Advanced CI/CD Pipeline with Multi-Stage Deployment

Build comprehensive CI/CD pipelines using Jenkins and GitHub Actions with multi-environment deployments, automated testing, and rollback strategies.

Production-Grade Kubernetes Cluster Architecture

Deploy, scale, and manage production-ready microservices using Kubernetes with Helm charts, service mesh, and advanced monitoring.

Cloud Security & Compliance Framework Implementation

Implement comprehensive security controls including IAM policies, encryption, network security, and compliance frameworks (GDPR, HIPAA, ISO 27001).

Multi-Cloud Terraform Infrastructure Automation

Automate cloud resource provisioning and management using Terraform across AWS, Azure, and GCP with state management and modular architecture.

Enterprise Disaster Recovery & High Availability Solution

Design and implement enterprise-grade disaster recovery strategy with automated backup, failover mechanisms, and business continuity planning.

FinOps: Cloud Cost Optimization & Management Platform

Analyze and optimize cloud spending using FinOps principles, automated cost management tools, and resource right-sizing strategies.

Serverless Application Architecture with Event-Driven Design

Build scalable event-driven serverless applications using AWS Lambda, Azure Functions, and GCP Cloud Functions with API Gateway integration.

Hybrid Cloud Architecture Integration

Design and implement hybrid cloud solutions connecting on-premises infrastructure with cloud platforms using VPN, Direct Connect, and ExpressRoute.

Advanced Monitoring, Observability & SRE Implementation

Implement comprehensive monitoring and observability solutions using Grafana, Prometheus, and CloudWatch with SRE best practices.

Job Obligation After This Course

WE CAN APPLY FOR JOBS IN

Deploy, manage, and scale cloud infrastructure across AWS and Microsoft Azure environments.

Provision and automate infrastructure using Terraform, ARM Templates, CloudFormation, and IaC practices.

Design and manage compute, storage, networking, and database services in multi-cloud environments.

Implement CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, and Azure DevOps tools.

Build, deploy, and manage containerized applications using Docker, Kubernetes, EKS, and AKS.

Apply IAM, RBAC, MFA, and cloud security best practices for secure infrastructure management.

Monitor systems using CloudWatch, Azure Monitor, Prometheus, Grafana, and observability platforms.

Perform troubleshooting, incident management, reliability engineering, and production support operations.

Implement cloud cost optimization, backup strategies, disaster recovery, and infrastructure scaling solutions.

Support serverless computing, MLOps workflows, AI infrastructure, and intelligent deployment pipelines.

Companies Hiring for this Course

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Admission Process

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Course Fees & Financing

Course Fees

Upto

30%

Off

In USD

$1,750

In INR

₹1,49,999

Inclusive of All Taxes

Enroll Now →
Payment Partners

We partnered with financing companies to provide competitive finance options at 0% interest rate with no hidden costs.

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UPCOMING BATCHES/PROGRAM COHORTS

BatchDateTime (IST)Batch Type
Weekend Online Live Sessions23 May 2026Saturday & SundayBatch 1
Weekend Online Live SessionsVery SoonSaturday & SundayBatch 2

COMPARISON WITH OTHERS

FeatureOur CourseCOMPETITOR ACOMPETITOR B
Cloud Platforms CoveredAWS & Azure with True Multi-Cloud ExpertiseAWS onlySingle cloud platform focus
Cloud Architecture & FoundationsWell-Architected Framework, CAF, Migration & Hybrid Cloud StrategiesBasic cloud conceptsLimited architecture exposure
DevOps IntegrationCI/CD, GitOps, Terraform, Docker, Kubernetes & AutomationBasic Docker and Jenkins conceptsLimited DevOps ecosystem
Infrastructure as Code (IaC)Terraform, ARM Templates, CloudFormation & Infrastructure AutomationBasic IaC exposureTerraform-focused learning
Containers & KubernetesDocker, Kubernetes, EKS, AKS, Helm & GitOps DeploymentsIntroductory container conceptsBasic Kubernetes coverage
Monitoring & ReliabilityCloudWatch, Azure Monitor, Prometheus, Grafana & Observability ToolsBasic monitoring conceptsTool-specific monitoring only
Security & GovernanceIAM, RBAC, MFA, Cloud Security, Governance & Compliance StandardsBasic security overviewLimited security implementation
Hands-On ProjectsReal-world labs, capstone projects, deployment scenarios & automation tasksFew theoretical projectsLimited practical exposure
Faculty ExpertiseIndustry Experts with Cloud, DevOps & AI Infrastructure ExperienceGeneral industry trainersMixed teaching background
Career & Placement SupportCareer guidance, mock interviews, portfolio support & placement assistanceLimited career supportStandard placement support

Official Partnership Recognition

Proud to be a Recognised Skilling Partner of IT-ITeS SSC Nasscom

Partnership Certificate
Verified

Certificate of Partnership

SkillzRevo Solutions Private Limited

Partnership Details

Organization

SkillzRevo Solutions Private Limited

Recognition Status

Recognised Skilling Partner

Certifying Authority

IT-ITeS SSC Nasscom

Validity Period

24/11/2025 - 24/11/2026

FutureSkills Prime Initiative

A MeitY - Nasscom Digital Skilling Initiative empowering professionals with cutting-edge IT skills

Active Partnership

10+

Year Partnership

100%

Certified

Committed to Excellence in Digital Skilling

As a recognized skilling partner, we are dedicated to delivering world-class IT training and development programs aligned with industry standards and government initiatives.

Skill IndiaIT-ITeS SectorNasscom Certified

Frequently Asked Questions