Gen AI Specialization
End-to-end GenAI engineering: Transformers → agents, multimodal RAG, diffusion, ViT, VLMs, eval & deployment.
AIOps Certification Course
Learn how to build scalable, production-grade AI systems with unified training across MLOps, LLMOps, and AgentOps. This AIOps certification course focuses on full lifecycle observability—from data drift to model drift to prompt drift—using advanced tools like MLflow, LangSmith, Langtrace, and vLLM.
Explore our flexible online AIOps course in India, built for engineers and DevOps professionals working with ML, DL (Vision, NLP, Speech), and Generative AI. Download the detailed AIOps syllabus (PDF), check course fees, or book a free session to see how AIOps transforms your infrastructure.
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Why Choose Our AIOps Course?
AI Infrastructure from ML to PromptOps
Master the full AI lifecycle—from data pipelines to model training to LLM and agent prompt workflows—all in one course.
Hands-on with AIOps Toolchains
Build with MLflow, LangSmith, vLLM, DVC, Langtrace, and more—designed for real-world AI system deployment.
End-to-End Observability & Tracing
Monitor and trace model, prompt, and agent behavior at token level using Langtrace and LangSmith.
Drift Detection at All Levels
Detect and mitigate data drift, model drift, and prompt drift in modern ML and GenAI pipelines.
LLMOps and AgentOps Integration
Deploy large language models and autonomous agents with optimized inference and secure orchestration.
Secure Deployment & Cost Controls
Implement safety layers, usage guardrails, API security, and cost optimization best practices.
Hybrid & Cloud-Ready Deployments
Deploy models and agents across cloud-native and on-prem setups using TorchServe, vLLM, and Kubernetes.
Full-Stack AIOps Projects
Work on industry-aligned AIOps projects—from ML retraining workflows to LLM pipeline observability.
Mentorship from AIOps Engineers
Get mentored by professionals managing AI infrastructure at scale in startups and enterprise environments.
Who Should Join This AIOps Course?
Built for infra teams, MLOps engineers, and AI professionals deploying scalable, traceable GenAI & ML systems — across data, model, and prompt layers.
Top Skills You’ll Gain in AIOps Course
AI Infrastructure Lifecycle
MLOps CI/CD Pipelines
LLMOps & Model Serving (vLLM)
PromptOps & Drift Evaluation
AgentOps Orchestration
LangSmith & Langtrace
Data/Model/Prompt Drift Detection
Secure API Tool Integration
RAGOps with LlamaIndex
Hybrid Cloud Deployment
Monitoring & Logging with Prometheus
DVC & MLflow Versioning
Token-Level Observability
Guardrails & Cost Management
AIOps Tools & Frameworks You’ll Master
MLflow
Model Lifecycle Management
Track, package, and deploy ML models with versioning and experiment tracking.
LangSmith
LLM Observability & Debugging
Visualize, trace, and evaluate prompt chains and LLM runs across pipelines.
Langtrace
AgentOps & Prompt Tracing
Monitor token-level agent activity, drift, and tool usage patterns in real-time.
vLLM
Optimized LLM Inference
Serve LLMs with low latency and high throughput using paged attention and memory pinning.
DVC
Data Version Control
Reproducible data pipelines with Git-compatible versioning for datasets and models.
PromptLayer
PromptOps & Drift Monitoring
Track, compare, and version prompt templates to manage performance over time.
LlamaIndex
RAG Stack for Retrieval
Connect structured and unstructured data to LLMs via embeddings and vector stores.
TorchServe
Model Serving Framework
Deploy PyTorch models at scale using REST APIs and TorchScript/ONNX formats.
MLflow
Model Lifecycle Management
Track, package, and deploy ML models with versioning and experiment tracking.
LangSmith
LLM Observability & Debugging
Visualize, trace, and evaluate prompt chains and LLM runs across pipelines.
Langtrace
AgentOps & Prompt Tracing
Monitor token-level agent activity, drift, and tool usage patterns in real-time.
vLLM
Optimized LLM Inference
Serve LLMs with low latency and high throughput using paged attention and memory pinning.
DVC
Data Version Control
Reproducible data pipelines with Git-compatible versioning for datasets and models.
PromptLayer
PromptOps & Drift Monitoring
Track, compare, and version prompt templates to manage performance over time.
LlamaIndex
RAG Stack for Retrieval
Connect structured and unstructured data to LLMs via embeddings and vector stores.
TorchServe
Model Serving Framework
Deploy PyTorch models at scale using REST APIs and TorchScript/ONNX formats.
Course Roadmap – From ML Pipelines to AgentOps Observability
AIOps Foundations
Understand AI infrastructure holistically: • What is AIOps? • MLOps vs LLMOps vs AgentOps • AIOps lifecycle: Data → Model → Prompt • Tools: Git, Python, Shell, GitHub Actions
MLOps in Production
Pipeline orchestration to CI/CD: • Data versioning with DVC • CI/CD for ML workflows • Monitoring training + model registry • Tools: MLflow, GitHub Actions, Docker
LLMOps & Scalable Inference
Deploy and optimize LLMs: • vLLM serving • Quantization & optimization • Token-level observability • Tools: vLLM, DeepSpeed, HuggingFace
PromptOps & RAGOps
Manage prompt-level operations: • Drift-resistant prompts • RAG pipelines and hybrid retrieval • Testing + evaluation frameworks • Tools: LangChain, LlamaIndex, PromptLayer
Drift Detection Across Stages
Mitigate failures across the AI pipeline: • Data drift monitoring • Model drift alerts • Prompt drift evaluation • Tools: Evidently, LangSmith, LlamaIndex Eval
Hybrid & Multi-Cloud Deployments
Flexible deployment at scale: • On-prem, hybrid, and cloud setups • Serving with TorchServe, FastAPI, Kubernetes • Tools: AWS/GCP, TorchServe, Kubernetes
AI Observability & Tracing
Detect, trace, and log across ML + GenAI: • Log model behavior • Prompt tracing and agent routes • Visualization and alerts • Tools: Langtrace, Helicone, Prometheus
AgentOps & Secure Deployments
Orchestrate autonomous agents safely: • Secure tool-calling APIs • MCP, guardrails, fallback • Role-based access + sandboxing • Tools: LangSmith, AutoGen, MCP
End-to-End AIOps Use Cases
Apply your skills on real systems: • MLOps + LLMOps + AgentOps integration • CI/CD + RAG + observability + tracing • Real-world business pipelines • Stack: MLflow, LangSmith, vLLM, AutoGen
Capstone Projects & Monitoring
Deploy monitored, production-ready apps: • Build and evaluate full pipelines • Auto-tracing and logging • Load tests and feedback loops • Tools: Langtrace, Grafana, Streamlit
AIOps Foundations
Understand AI infrastructure holistically: • What is AIOps? • MLOps vs LLMOps vs AgentOps • AIOps lifecycle: Data → Model → Prompt • Tools: Git, Python, Shell, GitHub Actions
MLOps in Production
Pipeline orchestration to CI/CD: • Data versioning with DVC • CI/CD for ML workflows • Monitoring training + model registry • Tools: MLflow, GitHub Actions, Docker
LLMOps & Scalable Inference
Deploy and optimize LLMs: • vLLM serving • Quantization & optimization • Token-level observability • Tools: vLLM, DeepSpeed, HuggingFace
PromptOps & RAGOps
Manage prompt-level operations: • Drift-resistant prompts • RAG pipelines and hybrid retrieval • Testing + evaluation frameworks • Tools: LangChain, LlamaIndex, PromptLayer
AgentOps & Secure Deployments
Orchestrate autonomous agents safely: • Secure tool-calling APIs • MCP, guardrails, fallback • Role-based access + sandboxing • Tools: LangSmith, AutoGen, MCP
AI Observability & Tracing
Detect, trace, and log across ML + GenAI: • Log model behavior • Prompt tracing and agent routes • Visualization and alerts • Tools: Langtrace, Helicone, Prometheus
Hybrid & Multi-Cloud Deployments
Flexible deployment at scale: • On-prem, hybrid, and cloud setups • Serving with TorchServe, FastAPI, Kubernetes • Tools: AWS/GCP, TorchServe, Kubernetes
Drift Detection Across Stages
Mitigate failures across the AI pipeline: • Data drift monitoring • Model drift alerts • Prompt drift evaluation • Tools: Evidently, LangSmith, LlamaIndex Eval
End-to-End AIOps Use Cases
Apply your skills on real systems: • MLOps + LLMOps + AgentOps integration • CI/CD + RAG + observability + tracing • Real-world business pipelines • Stack: MLflow, LangSmith, vLLM, AutoGen
Capstone Projects & Monitoring
Deploy monitored, production-ready apps: • Build and evaluate full pipelines • Auto-tracing and logging • Load tests and feedback loops • Tools: Langtrace, Grafana, Streamlit
Industry-Trusted AIOps Certificate
Industry-Trusted AIOps Certificate
On completing the AIOps Certification Course, you’ll receive an industry-grade certificate— proving your ability to design, deploy, and monitor scalable AI systems. This includes MLOps, LLMOps, AgentOps, drift detection, tracing, and secure deployments using modern tools like MLflow, LangSmith, and Langtrace.
SCHOOLOFCOREAI
CERTIFICATE
OF ACHIEVEMENT
This certificate is presented to
Shweta Sharma
Has successfully mastered the AIOps Certification Course and has demonstrated the competencies required in the field.
Aishwarya Pandey
Founder & CEO
DD/MM/YY
SCAI-AIOPS-000123
AIOps Course vs Free Courses & Tutorials
| Feature | AIOps Course | Other Courses |
|---|---|---|
| MLOps + LLMOps + AgentOps Integration | ✔ Unified coverage across ML pipelines, LLM serving, and agent orchestration | ✘ Focuses on one layer only (e.g. ML or LLM), not full-stack |
| PromptOps, RAGOps & DriftOps | ✔ Covers prompt evaluation, RAG with LlamaIndex, and full drift detection lifecycle | ✘ Lacks prompt testing or drift/resilience strategies |
| LangSmith + Langtrace Observability | ✔ Token-level tracing, logs, error insights, and cost analytics built-in | ✘ No tools to trace or debug model/agent behavior |
| Production-Ready Deployment | ✔ Hybrid and cloud deployment using TorchServe, Docker, Kubernetes, and FastAPI | ✘ Teaches only offline notebooks or local runs |
| Real AIOps Use Cases | ✔ Includes CI/CD pipelines, secure agent APIs, monitored LLM flows, and retraining triggers | ✘ Mostly demo-level examples without full stack visibility |
| Career Coaching & Capstone Certification | ✔ Get mentored by infra engineers and certified with portfolio-grade AIOps systems | ✘ Limited resume value or production exposure |
| Placement Support & ROI | ✔ ₹40,000 one-time with job prep, mentor feedback, and placement assistance till hired | ✘ No structured outcome tracking or job support |
Which AI Infrastructure Track Fits You?
- MLOps Course: Master end-to-end ML workflows — from versioning and CI/CD to scalable model serving with Docker, Kubernetes, and MLflow.
- LLMOps Course: Specialize in LLM deployment — covering quantization, vLLM, LangServe, LangSmith, distributed inference, and cost optimization.
- AIOps Course: The all-in-one track — covering MLOps, LLMOps, and AgentOps. Dive deep into drift detection, PromptOps, RAG pipelines, and secure agent deployment.
AIOps Course Fees
As India’s most comprehensive AIOps program, we offer full-stack infrastructure training with one-time pricing and complete placement support.
One-time Payment
₹95,000
Flat ₹95,000 – No hidden charges. Includes full placement support & AIOps certification.
Included Benefits:
- Live mentorship from AIOps infrastructure engineers.
- Capstone projects using MLflow, LangSmith, vLLM, and Langtrace.
- Placement prep: mock interviews, resume building, referral network.
- Lifetime access to course recordings, toolkits, and future updates.
Explore Our Core AI Tracks
Already on AIOPS? Level up with a specialization. Bundle any 2 and save more.
🎁 Special: Bundle any 2 courses & save 20%
What Our Learners Say
Hear how professionals transformed their careers with Agentic AI
"The AIOps course helped me scale from DevOps to ML pipeline orchestration. With LangSmith, vLLM, and TorchServe, I now deploy secure and observable LLM systems in production."
Karthik Ramesh
AI Infrastructure Engineer, Zoho
"I joined to learn drift detection and ended up mastering AgentOps too. From MLflow to Langtrace, this course covers full-stack AIOps workflows used in real-world deployments."
Sanjeev Malik
MLOps Engineer, HCL Technologies
"As someone managing infra for LLMs, the AIOps course was perfect. It bridged LLMOps, prompt observability, and scalable agent deployment—exactly what our stack needed."
Megha Nair
AI Platform Engineer, Razorpay
"This course taught me how to manage model and prompt drift, integrate LangSmith and Langtrace, and build agent-aware CI/CD workflows. Total career upgrade."
Ankur Varma
Lead DataOps Engineer, Infosys
"The AIOps course helped me unify MLOps and LLMOps for enterprise use cases. With projects involving hybrid deployments and vector database tracing, it was incredibly practical."
Divya Reddy
AI Solutions Architect, TCS
"I was deploying ML models manually before this course. Now I build resilient agent pipelines using vLLM, LangGraph, and PromptLayer with drift tracing in production."
Arvind Pillai
AI Engineer, HealthTech Startup
Your Questions Answered – Agentic AI Course
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