Cloud & DevOps

Beginner to Mastery

Certified DevOps, MLOps, AIOps & LLMOps Engineer Program

End-to-end DevOps, Cloud, Kubernetes, MLOps, AIOps, and LLMOps delivery lifecycle.

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

GitOps and declarative delivery for cloud-native and Kubernetes environments.

Infrastructure provisioning using Terraform, cloud templates, and Infrastructure as Code practices.

Group Enrollment with Friends or Colleagues
Certified DevOps, MLOps, AIOps & LLMOps Engineer Program

Course Duration

250 Hours

Next Batch

23 May 2026

Course Material

Live. Online. Interactive.

Containerization and Kubernetes from fundamentals to production-grade deployments.

Serverless and event-driven CI/CD pipelines with cloud automation workflows.

Observability, logging, reliability engineering, and monitoring using Prometheus, Grafana, and ELK.

Hands-on DevOps, MLOps, AIOps, and LLMOps practices for modern cloud-native environments.

Highlight Certified DevOps, MLOps, AIOps & LLMOps Engineer Program

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

  • Weekly sessions with industry professionals
  • Dedicated Learning Management Team
  • 250+ hours of hands-on learning experience
  • Over 55 hours of live sessions for real-time interaction
  • Dedicated bridge classes to ensure seamless progression from DevOps and MLOps
  • Learn from Cloud Certified Industry Experts
  • More than 10+ 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

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

Strong DevOps Foundation

DevOps principles taught through real delivery workflows, not theory.

CI/CD & GitOps First Approach

Pipelines, branching strategies, secure CI, and GitOps-based CD.

Infrastructure as Code & Automation

Work with Terraform, cloud templates, configuration management, and automation tools.

Kubernetes & Cloud-Native Engineering

Master Docker, Kubernetes, Helm, GitOps deployments, and cloud-native operations.

Production Monitoring & Reliability

Learn observability, logging, alerting, troubleshooting, and SRE-focused reliability practices.

Future-Ready DevOps + AI Operations

Gain exposure to MLOps, AIOps, LLMOps, AI pipelines, and intelligent automation systems.

Hands-On Enterprise Projects

Work on practical cloud, Kubernetes, CI/CD, and production deployment scenarios.

Career-Focused Learning

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

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

Certified DevOps, MLOps, AIOps & LLMOps Engineer Program OVERVIEW

This program is designed to build industry-ready DevOps, Cloud, MLOps, AIOps, and LLMOps engineers by aligning closely with real-world enterprise delivery workflows. The learning journey begins with strong foundations in DevOps practices and CI/CD pipelines, then progressively advances into Infrastructure as Code (IaC), automation, and Kubernetes engineering. The program further covers observability, reliability engineering, serverless architectures, and cloud-native operations, and concludes with advanced topics such as MLOps, AIOps, LLMOps, AI pipelines, and production-grade AI infrastructure—preparing learners for modern cloud-native and AI-driven environments.

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

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Certified DevOps, MLOps, AIOps & LLMOps Engineer Program Objectives

The primary objective of this program is to enable learners to design, build, deploy, monitor, and operate production-grade systems across DevOps, CI/CD, Cloud, Kubernetes, MLOps, AIOps, and LLMOps environments. The program focuses on developing strong practical understanding so learners know when, why, and how to use the right tools, automation workflows, observability practices, serverless architectures, and AI infrastructure technologies in real-world production scenarios—not just theoretical concepts.

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

Industry-Focused Learning

Skills aligned with real enterprise cloud, DevOps, and operational best practices.

Real Enterprise Workflows

Learn real workflows used by DevOps, SRE, Platform, and cloud engineering teams.

End-to-End Delivery Ownership

Understand the complete lifecycle from code commit to CI/CD, infrastructure, and monitoring.

Strong Automation & IaC Foundation

Work with Terraform, GitOps, Infrastructure as Code, and pipeline automation practices.

Cloud-Native & Kubernetes Expertise

Build production-grade deployments, Kubernetes orchestration, and rollback strategies.

Observability & Cost Awareness

Learn monitoring, logging, alerting, reliability engineering, and cloud optimization techniques.

MLOps, AIOps & AI Infrastructure Exposure

Gain practical exposure to MLOps, AIOps, LLMOps, and AI infrastructure operations.

Real-World DevOps Scenarios

Work on hands-on labs, architecture mapping, and production-focused deployment scenarios.

Program Advantages

Job-ready DevOps, Cloud, MLOps, AIOps, and LLMOps skills for modern engineering roles.

Strong focus on real production environments, deployment workflows, and operational practices.

GitOps, Kubernetes, CI/CD, and cloud-native engineering-first learning approach.

Covers future-ready DevOps, AI infrastructure, and intelligent automation technologies.

Suitable for product engineering, platform engineering, cloud, and AI-driven teams.

Description

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

Certified DevOps, MLOps, AIOps & LLMOps Engineer Program Curriculum

Lecture 1: Numerical Computing with Numpy - Fundamental package for scientific computing with Python.
Lecture 2: Data Manipulation with Pandas - High-performance data structures and data analysis tools.
Lecture 3: Core ML Libraries with scikit-learn | XGBoost - Standard libraries for classical machine learning and gradient boosting.
Lecture 4: Serialization & Config with joblib | pyyaml - Tools for saving model artifacts and managing YAML configuration files.
Lecture 5: Linux & Networking with Linux Foundations | Basic HTTP | DNS | Rest APIs - Essential OS skills and understanding of web communication protocols.
Lecture 6: Version Control with Git & GitHub - Collaborative coding and source code management.
Lecture 7: Testing & Code Quality with pytest - Framework for writing and running unit tests for ML code.
Lecture 8: Experiment Tracking with MLflow | Weights & Biases - Tools to log parameters | metrics | and manage the model registry.
Lecture 9: Data & Model Versioning with DVC | LakeFS - Version control for large datasets and machine learning artifacts.
Lecture 10: Hyperparameter Tuning with optuna - Framework for automated hyperparameter optimization.
Lecture 11: API Development with FastAPI - Modern web framework for building APIs to serve model predictions.
Lecture 12: Containerization with Docker - Packaging applications into containers for consistent deployment.
Lecture 13: Scalable Serving with KServe | Tensorflow Serving - Advanced platforms for deploying and scaling ML models in production.
Lecture 14: Orchestration / Pipelines with Airflow | Kubeflow Pipelines (KFP DSL) | Argo Workflows - Tools to automate and schedule end-to-end ML workflows.
Lecture 15: CI/CD for MLOps with GitHub Actions | GitLab CI - Automating testing and deployment pipelines.
Lecture 16: Cloud Fundamentals with AWS | Azure | GCP - Major cloud platforms for hosting ML workloads.
Lecture 17: Platform & IaC with Kubernetes | Terraform | Pulumi | Crossplane - Container orchestration and Infrastructure as Code for environment management.
Lecture 18: Monitoring & Visualization with Prometheus | Grafana - Tools for tracking system health and real-time performance metrics.
Lecture 19: Observability & Logs with Fiddler | ELK | opensearch - Specialized tools for model performance monitoring and centralized logging.
Lecture 20: Infrastructure Context with CMDB | Topology Mapping - Understanding the relationships between physical and virtual assets.
Lecture 21: Observability Pillars with Prometheus | Grafana | ELK Stack | OpenSearch | Jaeger - Mastering the collection of Metrics | Logs | and Traces.
Lecture 22: Fundamentals with Supervised & Unsupervised Learning - Understanding the different types of learning models applicable to IT data.
Lecture 23: Key Algorithms with Isolation Forests | ARIMA | Prophet | LSTM - Specialized algorithms for Anomaly Detection and Time-Series Forecasting.
Lecture 24: Intelligent Logic with Event Correlation | Noise Reduction | Deduplication - Moving from static thresholds to pattern-based alerting and deduplication.
Lecture 25: Causal AI with Root Cause Analysis (RCA) - Using AI to identify the underlying cause of incidents rather than just symptoms.
Lecture 26: Data Ingestion with Fluentd | Logstash | Vector - Tools for high-performance data shipping from various sources.
Lecture 27: Stream Processing with Apache Kafka | Flink - Processing real-time operational data streams at scale.
Lecture 28: AIOps Platforms with Dynatrace | Datadog | Splunk ITSI | BigPanda - Enterprise platforms that provide out-of-the-box AIOps capabilities.
Lecture 29: Cloud-Native AI with AWS DevOps Guru | Azure Monitor | Google Cloud Operations - Cloud-specific AI tools for monitoring and optimization.
Lecture 30: IT Use Case Training with Log Clustering | Smart Alerting - Training models to group millions of log lines and suppress noisy alerts.
Lecture 31: Experiment Tracking with MLflow - Tracking model versions and performance in catching operational bugs.
Lecture 32: ITSM Integration with ServiceNow | Jira Service Management - Connecting AI insights to ticket management systems.
Lecture 33: Collaboration (ChatOps) with Slack | Microsoft Teams - Real-time delivery of AI insights to engineering teams.
Lecture 34: Event-Driven Automation with Ansible Rulebooks | StackStorm | Argo Events - Automating responses to specific events detected by AI.
Lecture 35: Closed-Loop Remediation with Self-Healing Scripts - Automatic execution of playbooks to resolve issues (e.g. restarting services).
Lecture 36: Scalability with Kubernetes | KEDA - Managing AIOps across global clusters and event-driven autoscaling.
Lecture 37: FinOps Integration with Cost Optimization Models - Using AI to predict and optimize cloud infrastructure spending.
Lecture 38: Prompt Engineering with Few-shot | Chain-of-Thought | ReAct - Mastering advanced techniques to guide LLM reasoning and output quality.
Lecture 39: Model Selection with Llama 3 | Mistral | GPT-4 | Gemini - Understanding the trade-offs between open-source and proprietary models.
Lecture 40: Vector Databases with Pinecone | Milvus | Weaviate | ChromaDB - Storing and retrieving high-dimensional embeddings for contextual data.
Lecture 41: Orchestration Frameworks with LangChain | LlamaIndex - Building complex chains that connect LLMs with external data sources.
Lecture 42: Fine-Tuning with LoRA | QLoRA | PEFT - Techniques for specializing models on domain data with minimal compute.
Lecture 43: Quantization with GGUF | EXL2 | AWQ - Reducing model size and memory requirements for faster inference.
Lecture 44: Semantic Evaluation with Ragas | DeepEval | Promptfoo - Using automated frameworks and "LLM-as-a-judge" to score outputs.
Lecture 45: Testing & Reliability with Deterministic Tests | Keyword Checks - Implementing standard software tests for non-deterministic model outputs.
Lecture 46: Inference Servers with vLLM | Text Generation Inference (TGI) | Ollama - High-throughput engines for serving LLMs in production environments.
Lecture 47: GPU Management with NVIDIA CUDA | Triton Inference Server - Optimizing GPU utilization and managing specialized hardware drivers.
Lecture 48: Observability & Tracing with LangSmith | Arize Phoenix - Debugging and visualizing the step-by-step execution of LLM chains.
Lecture 49: Prompt Versioning with LiteLLM | Portkey - Managing multiple model providers and versioning prompts as code.
Lecture 50: Safety & Compliance with NeMo Guardrails | Guardrails AI - Real-time filtering of inputs and outputs to prevent hallucinations and bias.
Lecture 51: Security with Prompt Injection Defense | PII Scrubbing - Protecting against adversarial attacks and ensuring data privacy.
Lecture 52: Cost & Usage with Token Tracking | Rate Limiting - Monitoring API consumption and managing infrastructure costs.
Lecture 53: Feedback Loops with Human-in-the-loop | Reinforcement Learning - Collecting user feedback to improve model performance over time.

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

DevOps Foundations & Delivery Models
Git, GitOps & CI Systems
CI/CD Pipeline Engineering
Infrastructure as Code & Configuration Management
Docker, Kubernetes, Helm & GitOps Deployments
Observability, Logging & Reliability Engineering
Serverless & Event-Driven Systems
MLOps Pipelines & Model Lifecycle Management
AIOps, LLMOps & AI Infrastructure Operations
Production ML Deployment & Intelligent Automation

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

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

Certified DevOps, MLOps, AIOps & LLMOps Engineer Program Program Benefits Illustration

CAREER OPPORTUNITIES AFTER THIS COURSE

Cloud Engineer / Platform Engineer Salary Range

Min

$550,000

Average

$900,000

Max

$1,600,000

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

Design, implement, and maintain CI/CD pipelines for automated build, testing, and deployment workflows.

Manage GitOps-based deployment strategies for scalable, secure, and reliable cloud releases.

Provision, automate, and optimize cloud infrastructure using Terraform and Infrastructure as Code practices.

Deploy, manage, and scale Kubernetes workloads in production cloud-native environments.

Implement monitoring, logging, observability, and alerting for system reliability and performance.

Design and manage serverless, event-driven, and cloud automation architectures.

Support MLOps, AIOps, LLMOps pipelines, AI infrastructure, and production ML operations.

Troubleshoot, debug, and resolve production incidents to ensure high availability and operational efficiency.

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

$649

In INR

₹59,999

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
FoundationsDevOps foundations aligned with real enterprise delivery workflowsTheory-based DevOps conceptsGeneral terminology overview
CI/CDMulti-tool CI/CD pipelines with GitHub Actions, GitLab CI & JenkinsSingle-tool CI demosBasic pipeline theory
GitOpsDedicated GitOps and declarative deployment workflowsRarely coveredLimited or missing coverage
Infrastructure as CodeTerraform, cloud templates, automation & Infrastructure as Code practicesBasic Terraform onlyIntroductory IaC concepts
Configuration ManagementAnsible, Puppet, Chef & real automation workflowsTool overview onlySingle tool focus
Containers & KubernetesDocker, Kubernetes, Helm & GitOps-based cloud-native deploymentsDocker with basic KubernetesIntroductory container concepts
ObservabilityPrometheus, Grafana, ELK, monitoring & troubleshooting practicesBasic monitoring overviewLimited logging setup
ServerlessEvent-driven systems and serverless CI/CD workflowsOptional topicNot covered
MLOps & AI OpsMLOps, AIOps, LLMOps, CI/CT/CD & AI infrastructure operationsNot includedMissing
AI InfrastructureGPU orchestration, ML monitoring, drift detection & AI operationsCompletely missingNot available
Career OutcomeDevOps, Cloud, MLOps, AIOps & LLMOps Engineer readinessEntry-level DevOps focusGeneral IT operations knowledge

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