Cloud & DevOps

Beginner to Mastery

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program

End-to-end AWS cloud engineering with real-world deployment use cases.

Hands-on DevOps with CI/CD, GitOps, and Infrastructure Automation.

Docker, Kubernetes, EKS, ECS, and Helm-based cloud orchestration.

Production-grade monitoring with AWS, Prometheus, Grafana, MLOps, AIOps, and LLMOps.

Group Enrollment with Friends or Colleagues

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program

Course Duration

400 Hours

Next Batch

23 May 2026

Course Material

Live. Online. Interactive.

Practical MLOps, AIOps, and LLMOps exposure for production AI and ML systems.

Weekly sessions with experienced cloud and industry professionals.

Dedicated Learning Management and technical support team.

400+ hours of hands-on cloud, DevOps, and AI infrastructure learning

Highlight AWS DevOps, MLOps, AIOps & LLMOps Engineer Program

KEY HIGHLIGHTS OF AWS DEVOPS, MLOPS, AIOPS & LLMOPS ENGINEER PROGRAM PROGRAM

Over 100 hours of live sessions for real-time interaction
Dedicated bridge classes to ensure seamless progression from AWS to DevOps and MLOps.
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 AWS DEVOPS, MLOPS, AIOPS & LLMOPS ENGINEER PROGRAM PROGRAM?

Strong AWS Foundations

Learn AWS architecture, security, networking, governance, and cloud cost optimization.

Hands-on DevOps & Automation

Work with CI/CD, Terraform, CloudFormation, GitOps, Kubernetes, and AWS automation tools.

Industry-Relevant Tools

Gain practical experience with AWS, Docker, Kubernetes, observability, MLOps, AIOps, and LLMOps.

Career-Oriented

Prepare for AWS Cloud, DevOps, Platform, MLOps, AIOps, and AI Infrastructure Engineer roles.

UPCOMING BATCH:

23 May 2026

SkillzRevo Solutions

30 MINUTE MEETING

Web conferencing details provided upon confirmation.

Corporate Training, Enterprise training for teams

Batch schedule

Batch	Batch Type
Online Live Instructor Led Session	Full-Time
Online Live Instructor Led Session	Part-Time

Regional Timings

Batch	Batch Type
IST (India Standard Time)	09:00 PM–12:00 AM
Bahrain, Qatar, Kuwait, Saudi Arabia	06:30 PM–09:30 PM
UAE / Oman	07:30 PM–09:00 PM

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program OVERVIEW

This program is designed to build strong AWS cloud engineering skills combined with production-ready DevOps, MLOps, AIOps, and LLMOps practices. Learners gain hands-on experience across AWS infrastructure, automation, CI/CD pipelines, containers, Kubernetes, observability, and AI-driven cloud operations. The curriculum follows a real enterprise learning path — starting from AWS cloud foundations and security, progressing through DevOps engineering and Kubernetes, and extending into modern MLOps, AIOps, and AI infrastructure concepts. Practical labs and real-world deployment scenarios ensure learners are prepared to work confidently in production environments.

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

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AWS DevOps, MLOps, AIOps & LLMOps Engineer Program Objectives

The primary objective of this program is to build industry-ready AWS DevOps, MLOps, AIOps, and LLMOps Engineers with strong practical skills beyond tools and certifications. The focus is on real enterprise workflows, automation, scalability, observability, and production reliability. Learners will understand when, why, and how to implement AWS services, DevOps pipelines, Kubernetes, MLOps, AIOps, and LLMOps practices in real business scenarios - enabling them to contribute effectively from day one in production environments.

Enroll Now →

Why Learn AWS DevOps, MLOps, AIOps & LLMOps Engineer Program ?

Industry-Focused Learning

Skills aligned with real enterprise AWS, DevOps, MLOps, AIOps, and LLMOps environments.

AWS-Centric Cloud Expertise

Gain hands-on experience with AWS architecture, services, security, and cloud best practices.

End-to-End DevOps Skills

Learn CI/CD pipelines, GitOps, automation, testing, and deployment strategies.

Strong Automation & IaC Foundation

Master infrastructure automation using Terraform, CloudFormation, and configuration management tools.

Containers & Kubernetes at Scale

Build and manage applications using Docker, Amazon EKS/ECS, Kubernetes, and Helm.

Production Monitoring & Reliability

Learn observability, logging, alerting, monitoring, and production troubleshooting practices.

MLOps, AIOps & AI Infrastructure Readiness

Understand data versioning, model lifecycle management, AI infrastructure, and intelligent monitoring concepts.

Role-Based Career Preparation

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

Program Advantages

Job-ready skills focused on real-world AWS and DevOps implementations

End-to-end learning from AWS foundations to DevOps, Kubernetes, and MLOps

Strong emphasis on automation, Infrastructure as Code, and CI/CD pipelines

Training aligned with real production architectures and operational practices

Future-ready skill set covering cloud-native, platform engineering, and MLOps concepts

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program program Certifications

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program 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: Numerical Computing with Numpy - Fundamental package for scientific computing with Python.

Lecture 17: Data Manipulation with Pandas - High-performance data structures and data analysis tools.

Lecture 18: Core ML Libraries with scikit-learn | XGBoost - Standard libraries for classical machine learning and gradient boosting.

Lecture 19: Serialization & Config with joblib | pyyaml - Tools for saving model artifacts and managing YAML configuration files.

Lecture 20: Linux & Networking with Linux Foundations | Basic HTTP | DNS | Rest APIs - Essential OS skills and understanding of web communication protocols.

Lecture 21: Version Control with Git & GitHub - Collaborative coding and source code management.

Lecture 22: Testing & Code Quality with pytest - Framework for writing and running unit tests for ML code.

Lecture 23: Experiment Tracking with MLflow | Weights & Biases - Tools to log parameters | metrics | and manage the model registry.

Lecture 24: Data & Model Versioning with DVC | LakeFS - Version control for large datasets and machine learning artifacts.

Lecture 25: Hyperparameter Tuning with optuna - Framework for automated hyperparameter optimization.

Lecture 26: API Development with FastAPI - Modern web framework for building APIs to serve model predictions.

Lecture 27: Containerization with Docker - Packaging applications into containers for consistent deployment.

Lecture 28: Scalable Serving with KServe | Tensorflow Serving - Advanced platforms for deploying and scaling ML models in production.

Lecture 29: Orchestration / Pipelines with Airflow | Kubeflow Pipelines (KFP DSL) | Argo Workflows - Tools to automate and schedule end-to-end ML workflows.

Lecture 30: CI/CD for MLOps with GitHub Actions | GitLab CI - Automating testing and deployment pipelines.

Lecture 31: Cloud Fundamentals with AWS | Azure | GCP - Major cloud platforms for hosting ML workloads.

Lecture 32: Platform & IaC with Kubernetes | Terraform | Pulumi | Crossplane - Container orchestration and Infrastructure as Code for environment management.

Lecture 28: Monitoring & Visualization with Prometheus | Grafana - Tools for tracking system health and real-time performance metrics.

Lecture 29: Observability & Logs with Fiddler | ELK | opensearch - Specialized tools for model performance monitoring and centralized logging.

Lecture 30: Infrastructure Context with CMDB | Topology Mapping - Understanding the relationships between physical and virtual assets.

Lecture 32: Fundamentals with Supervised & Unsupervised Learning - Understanding the different types of learning models applicable to IT data.

Lecture 33: Key Algorithms with Isolation Forests | ARIMA | Prophet | LSTM - Specialized algorithms for Anomaly Detection and Time-Series Forecasting.

Lecture 34: Intelligent Logic with Event Correlation | Noise Reduction | Deduplication - Moving from static thresholds to pattern-based alerting and deduplication.

Lecture 35: Causal AI with Root Cause Analysis (RCA) - Using AI to identify the underlying cause of incidents rather than just symptoms.

Lecture 36: Data Ingestion with Fluentd | Logstash | Vector - Tools for high-performance data shipping from various sources.

Lecture 37: Stream Processing with Apache Kafka | Flink - Processing real-time operational data streams at scale.

Lecture 38: AIOps Platforms with Dynatrace | Datadog | Splunk ITSI | BigPanda - Enterprise platforms that provide out-of-the-box AIOps capabilities.

Lecture 39: Cloud-Native AI with AWS DevOps Guru | Azure Monitor | Google Cloud Operations - Cloud-specific AI tools for monitoring and optimization.

Lecture 40: IT Use Case Training with Log Clustering | Smart Alerting - Training models to group millions of log lines and suppress noisy alerts.

Lecture 41: Experiment Tracking with MLflow - Tracking model versions and performance in catching operational bugs.

Lecture 42: ITSM Integration with ServiceNow | Jira Service Management - Connecting AI insights to ticket management systems.

Lecture 43: Collaboration (ChatOps) with Slack | Microsoft Teams - Real-time delivery of AI insights to engineering teams.

Lecture 44: Event-Driven Automation with Ansible Rulebooks | StackStorm | Argo Events - Automating responses to specific events detected by AI.

Lecture 45: Closed-Loop Remediation with Self-Healing Scripts - Automatic execution of playbooks to resolve issues (e.g. restarting services).

Lecture 46: Scalability with Kubernetes | KEDA - Managing AIOps across global clusters and event-driven autoscaling.

Lecture 47: FinOps Integration with Cost Optimization Models - Using AI to predict and optimize cloud infrastructure spending.

Lecture 48: Prompt Engineering with Few-shot | Chain-of-Thought | ReAct - Mastering advanced techniques to guide LLM reasoning and output quality.

Lecture 49: Model Selection with Llama 3 | Mistral | GPT-4 | Gemini - Understanding the trade-offs between open-source and proprietary models.

Lecture 50: Vector Databases with Pinecone | Milvus | Weaviate | ChromaDB - Storing and retrieving high-dimensional embeddings for contextual data.

Lecture 51: Orchestration Frameworks with LangChain | LlamaIndex - Building complex chains that connect LLMs with external data sources.

Lecture 52: Fine-Tuning with LoRA | QLoRA | PEFT - Techniques for specializing models on domain data with minimal compute.

Lecture 53: Quantization with GGUF | EXL2 | AWQ - Reducing model size and memory requirements for faster inference.

Lecture 54: Semantic Evaluation with Ragas | DeepEval | Promptfoo - Using automated frameworks and "LLM-as-a-judge" to score outputs.

Lecture 55: Testing & Reliability with Deterministic Tests | Keyword Checks - Implementing standard software tests for non-deterministic model outputs.

Lecture 56: Inference Servers with vLLM | Text Generation Inference (TGI) | Ollama - High-throughput engines for serving LLMs in production environments.

Lecture 57: GPU Management with NVIDIA CUDA | Triton Inference Server - Optimizing GPU utilization and managing specialized hardware drivers.

Lecture 58: Observability & Tracing with LangSmith | Arize Phoenix - Debugging and visualizing the step-by-step execution of LLM chains.

Lecture 59: Prompt Versioning with LiteLLM | Portkey - Managing multiple model providers and versioning prompts as code.

Lecture 60: Safety & Compliance with NeMo Guardrails | Guardrails AI - Real-time filtering of inputs and outputs to prevent hallucinations and bias.

Lecture 61: Security with Prompt Injection Defense | PII Scrubbing - Protecting against adversarial attacks and ensuring data privacy.

Lecture 62: Cost & Usage with Token Tracking | Rate Limiting - Monitoring API consumption and managing infrastructure costs.

Lecture 63: Feedback Loops with Human-in-the-loop | Reinforcement Learning - Collecting user feedback to improve model performance over time.

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program Skills Covered

AWS Cloud Architecture & Administration

Cloud Security & Governance

CI/CD Pipeline Engineering

Infrastructure as Code (IaC)

Containerization & Kubernetes Engineering

GitOps & Release Management

Observability & Reliability Engineering

Serverless & Event-Driven Architectures

MLOps, AIOps & LLMOps Operations

AI Infrastructure & Intelligent Automation

Production Troubleshooting & Cloud Optimization

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program Tools Covered

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program Program Benefits

CAREER OPPORTUNITIES AFTER THIS COURSE

DevOps Engineer Salary Range

Min

$600,000

Average

$1,000,000

Max

$2,000,000

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 AWS cloud infrastructure using enterprise and production best practices.

Provision and automate AWS resources using Terraform, CloudFormation, and Infrastructure as Code.

Design and manage compute, storage, networking, database, and security services on AWS.

Implement CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, and GitOps workflows.

Build and manage containerized applications using Docker, Kubernetes, Amazon EKS, and ECS.

Apply IAM governance, security policies, MFA, compliance controls, and cloud security best practices.

Monitor cloud infrastructure using CloudWatch, Prometheus, Grafana, ELK Stack, and observability tools.

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

Implement backup, disaster recovery, auto-scaling, cost optimization, and high-availability solutions on AWS.

Support MLOps, AIOps, and LLMOps workflows including model deployment, monitoring, and AI infrastructure operations.

Companies Hiring for this Course

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

$1099

In INR

₹1,09,999

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.

UPCOMING BATCHES/PROGRAM COHORTS

Batch	Date	Time (IST)	Batch Type
Weekend Online Live Sessions	23 May 2026	Saturday & Sunday	Batch 1
Weekend Online Live Sessions	Very Soon	Saturday & Sunday	Batch 2

COMPARISON WITH OTHERS

Feature	Our Course	COMPETITOR A
Cloud Focus	AWS-first with production-grade DevOps, MLOps, AIOps & LLMOps practices	Tool-based or certification-focused learning
DevOps Coverage	End-to-end CI/CD, GitOps, IaC, automation & deployment workflows	Limited pipeline exposure
Kubernetes	Advanced EKS, Helm, GitOps, orchestration & container management	Basic container concepts
Observability	Prometheus, Grafana, ELK, CloudWatch & production monitoring tools	Minimal monitoring coverage
MLOps & AI Ops	MLOps, AIOps, LLMOps, AI infrastructure & model operations	Usually not included
Learning Approach	Real enterprise workflows, production labs & deployment scenarios	Mostly theoretical learning
Job Readiness	High – aligned with production cloud and DevOps environments	Medium – limited practical exposure

Official Partnership Recognition

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

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

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program

Course Duration

Next Batch

Course Material

KEY HIGHLIGHTS OF AWS DEVOPS, MLOPS, AIOPS & LLMOPS ENGINEER PROGRAM PROGRAM

WHY JOIN AWS DEVOPS, MLOPS, AIOPS & LLMOPS ENGINEER PROGRAM PROGRAM?

Strong AWS Foundations

Hands-on DevOps & Automation

Industry-Relevant Tools

Career-Oriented

UPCOMING BATCH:

SkillzRevo Solutions

Batch schedule

Regional Timings

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program OVERVIEW

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program Objectives

Why Learn AWS DevOps, MLOps, AIOps & LLMOps Engineer Program ?

Industry-Focused Learning

AWS-Centric Cloud Expertise

End-to-End DevOps Skills

Strong Automation & IaC Foundation

Containers & Kubernetes at Scale

Production Monitoring & Reliability

MLOps, AIOps & AI Infrastructure Readiness

Role-Based Career Preparation

Program Advantages

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program program Certifications

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program Curriculum

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program Skills Covered

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program Tools Covered

AWS DevOps, MLOps, AIOps & LLMOps Engineer Program Program Benefits

CAREER OPPORTUNITIES AFTER THIS COURSE

DevOps Engineer Salary Range

MASTER CLOUD COMPUTING WITH REAL-WORLD PROJECTS

Capstone Projects of this Program

Job Obligation After This Course

Companies Hiring for this Course

Admission Process

Course Fees & Financing

UPCOMING BATCHES/PROGRAM COHORTS

COMPARISON WITH OTHERS

Official Partnership Recognition

Partnership Details

FutureSkills Prime Initiative

Committed to Excellence in Digital Skilling

Frequently Asked Questions