The only certification that covers MLOps, LLMOps, and AgentOps in one 6-month program. Build 8 deployed systems with MLflow, vLLM, LangSmith, and LangGraph — from experiment tracking to autonomous agent orchestration.
In this program, AIOps means building, serving, observing, and governing production AI systems across model pipelines, LLM infrastructure, and agent workflows.
Model lifecycle & training pipelines
Tools: MLflow 2.11+, DVC 3.0+, Kubeflow 1.8+
LLM serving & fine-tuning operations
Tools: vLLM v0.4+, LangSmith, Langtrace
Agent orchestration & governance
Tools: LangGraph 0.1+, CrewAI, AutoGen 0.2+, Evidently
Built for engineers and technical leads already working with AI, ML, data, or platform systems:
designing and scaling production AI systems, model pipelines, and inference infrastructure
building reliable ML pipelines, experiment tracking, and automated retraining workflows
taking models beyond notebooks into deployment, monitoring, drift detection, and operational ownership
managing AI infrastructure, GPU clusters, model serving, and observability pipelines
architecting AI platforms, establishing MLOps/LLMOps practices, and leading data infrastructure teams
Prerequisites: Python proficiency, basic ML concepts, and experience with production systems or infrastructure.
You will build production-grade AI infrastructure components including:
with LangSmith/Langtrace for tracing every model call, agent interaction, and cost attribution
monitoring data drift, concept drift, and prompt drift with automated alerting
using vLLM v0.4+ with PagedAttention, quantization, and auto-scaling for real production workloads
with LangGraph 0.1+ for multi-agent workflows, Model Context Protocol (MCP), and guardrails
with vector databases, retrieval evaluation, and semantic monitoring
tracking token usage, GPU utilization, and budget controls across teams
with model evaluation gates, A/B testing, and rollback capabilities
implementing audit trails, compliance checks, and security policies for AI systems
Six operational pillars define the scope of the program, from model pipelines and inference serving to observability, drift control, and governance.
01
Build reproducible ML pipelines with experiment tracking, model versioning, and CI/CD for model deployments.
▸Experiment tracking with MLflow 2.11+: hyperparameters, metrics, artifacts, and model registry
▸Dataset versioning with DVC 3.0+ — reproducible training runs with lineage tracking
▸CI/CD pipelines for model releases with eval gates and rollback policies
02
Deploy foundation models with vLLM v0.4+, LangServe, and TGI — optimized for latency, throughput, and cost.
▸High-throughput serving with PagedAttention, continuous batching, and KV-cache sizing
▸Quantization tradeoffs: GPTQ, AWQ, INT4/INT8 — pick the right balance for your workload
▸p95/p99 latency profiling with GPU utilization monitoring and autoscaling
03
Build autonomous agents with LangGraph 0.1+, CrewAI, and Model Context Protocol — secure tool calling and multi-agent workflows.
▸Tool-calling agents with schema validation and allowlisted function execution
▸Model Context Protocol (MCP): connect agents to databases, APIs, and external systems
▸Guardrails: input/output filters, sandboxing, and audit logging for every action
04
Instrument every model call with LangSmith and Langtrace — traces, cost analytics, and drift detection.
▸Token-level tracing: latency, cost-per-request, and error diagnostics per chain step
▸Semantic drift detection: compare outputs across weekly golden-set snapshots
▸Alert rules: latency breach, hallucination spike, budget exceeded → Slack/PagerDuty
05
Monitor data drift, model drift, and prompt drift across the entire AI pipeline with Evidently and custom detectors.
▸Data drift: statistical tests on input distributions with automated retraining triggers
▸Model drift: accuracy degradation tracking with A/B comparison pipelines
▸Prompt drift: semantic similarity regression with version-controlled prompt configs
06
Enforce guardrails, budget caps, and governance policies across all AI workloads.
▸Prompt injection defense: multi-layer sanitization, model-side filters, output scanning
▸Budget caps per team/model with auto-throttle at threshold and usage dashboards
▸Audit logs: every request traced with user identity, tool calls, and compliance checks
Every pillar ends with a working system — traced, monitored, and deployed. Your capstone wires all six into one production-ready AIOps platform.
These are the operational capabilities you should be able to demonstrate after completing the program in labs, reviews, and deployed systems.
Every skill is assessed during the capstone — ML pipelines, LLM serving, agent orchestration, and cost budgets reviewed by practising engineers.
Every tool is used inside a project. You'll know when to pick each one, what it trades off, and how it fails under load.
MLflow
Experiment tracking, model registry, and artifact versioning
Industry standard for ML lifecycle — tracks hyperparameters, metrics, and model lineage
DVC
Dataset and pipeline versioning like Git for data
Reproducible training runs with full data lineage and remote storage support
Evidently
Data drift detection and model monitoring
Automated drift reports with statistical tests and retraining triggers
Great Expectations
Data validation and quality gates
Schema enforcement and data quality checks in CI/CD pipelines
vLLM
High-throughput LLM serving with PagedAttention
Production standard for self-hosted LLM inference — continuous batching, KV-cache, tensor parallelism
LangServe
FastAPI-style LLM API endpoints with streaming
Deploy LangChain apps as production APIs with health checks and validation
TGI
Hugging Face text-generation server
Native HF model support with flash-attention, quantization, and token streaming
TorchServe
PyTorch model serving at scale
REST endpoints with GPU scheduling, batching, and A/B deployment support
LangSmith
LLM tracing, evaluation, and dataset management
End-to-end trace visibility — latency, cost, and quality metrics in one view
Langtrace
Open-source agent and LLM tracing
Self-hosted option with detailed tool call traces and cost analytics
Grafana + Prometheus
Dashboards for metrics, alerts, and SLAs
Industry-standard infra monitoring — integrates with existing oncall stacks
OpenTelemetry
Distributed tracing standard
Vendor-agnostic trace export for unified observability across services
LangGraph
Stateful multi-agent DAGs with memory
Build complex agent workflows with branching, loops, and persistent state
LlamaIndex
RAG pipelines with retrieval evaluation
Index, retrieve, and evaluate over structured + unstructured data
CrewAI
Multi-agent collaboration framework
Define agent roles, tasks, and workflows for team-based AI systems
PromptLayer
Prompt versioning and performance tracking
Track prompt changes, compare variants, and monitor regression
Six phases. Six deployed systems. Each month ends with infrastructure you can point to — not just theory checkpoints.
Month 1
AIOps lifecycle: data → model → prompt → agent → observe → iterate
Python automation for ML APIs (async requests, retries, error handling)
Git strategies for data, model, prompt, and config versioning
Docker: containerize ML and inference servers with multi-stage builds
Deliverable
Dockerized ML API with health checks, CI pipeline, and version-controlled configs
Month 2
MLflow: track experiments, hyperparameters, metrics, and model registry
DVC: dataset versioning with reproducible training pipelines
Data validation with Great Expectations and quality gates in CI
Data drift detection with Evidently and automated retraining triggers
Deliverable
Reproducible ML pipeline with MLflow lineage, data validation, and drift monitoring
Month 3
vLLM: PagedAttention, continuous batching, tensor parallelism, KV-cache sizing
LangServe: FastAPI-based LLM endpoints with streaming and circuit breakers
Quantization: GPTQ, AWQ, INT4/INT8 tradeoffs (latency vs accuracy vs VRAM)
Benchmarking: p50/p95/p99 latency, throughput (tok/s) under concurrent load
Deliverable
Load test report with p95/p99 latency benchmarks + GPU utilization dashboard
Month 4
LangGraph and CrewAI: multi-agent workflows with memory and state
MCP integrations: connect agents to databases, APIs, and external systems
RAGOps with LlamaIndex: retrieval pipelines with evaluation metrics
Guardrails: input/output filters, tool allowlisting, and sandboxed execution
Deliverable
Production agent system with MCP integrations, RAG pipeline, and security controls
Month 5
LangSmith/Langtrace: trace every chain, prompt, and tool call with cost-per-request
Drift detection: data, model, and prompt drift with automated alerting
Golden-set regression testing with acceptance thresholds in CI
Dashboards: Grafana + Prometheus for latency, throughput, and cost metrics
Deliverable
Observability stack with drift detection pipeline, dashboards, and oncall runbook
Month 6
End-to-end system: ML pipeline → LLM serving → agent orchestration → observe
CI/CD with eval gates: golden-set pass rate blocks bad deploys
Security review: prompt injection tests, PII scan, access audit, cost controls
Ops drill: simulated incident (latency spike, drift regression) — you triage and respond
Deliverable
Production-ready AIOps system with full CI/CD, observability, security review, and ops drill postmortem
Built for engineers who want depth, rigor, mentorship, and deployment discipline rather than lightweight survey content.
Master MLOps, LLMOps, and AgentOps in one unified track — from ML pipelines to LLM serving to autonomous agent orchestration.
Detect and mitigate data drift, model drift, and prompt drift using Evidently, custom pipelines, and automated alerting workflows.
End-to-end tracing with LangSmith, Langtrace, and OpenTelemetry — token-level cost tracking, latency profiling, and error diagnostics.
Deploy models with vLLM, TGI, and LangServe — continuous batching, quantization tradeoffs, and p95/p99 latency optimization.
Build tool-calling agents with LangGraph, CrewAI, and Model Context Protocol — secure orchestration with audit trails.
Retrieval pipelines with LlamaIndex, prompt versioning with PromptLayer, and evaluation-driven prompt iteration.
Prompt injection defense, tool allowlisting, PII masking, budget caps, and full audit logging for compliance.
Docker + Kubernetes deployments across cloud and on-prem — TorchServe, FastAPI, canary rollouts, and auto-rollback.
PR-style code reviews, simulated ops drills (latency spikes, GPU failures), and weekly architecture office hours.
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 covers MLOps, LLMOps, AgentOps, drift detection, tracing, and secure deployments with tools like MLflow, LangSmith, and Langtrace.
Has successfully mastered the AIOps Certification Course and has demonstrated the competencies required in the field.
| Feature | Our 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 | ✔₹80,000 one-time with job prep, mentor feedback, and placement assistance till hired | ✘No structured outcome tracking or job support |
Unified coverage across ML pipelines, LLM serving, and agent orchestration
Focuses on one layer only (e.g. ML or LLM), not full-stack
Covers prompt evaluation, RAG with LlamaIndex, and full drift detection lifecycle
Lacks prompt testing or drift/resilience strategies
Token-level tracing, logs, error insights, and cost analytics built-in
No tools to trace or debug model/agent behavior
Hybrid and cloud deployment using TorchServe, Docker, Kubernetes, and FastAPI
Teaches only offline notebooks or local runs
Includes CI/CD pipelines, secure agent APIs, monitored LLM flows, and retraining triggers
Mostly demo-level examples without full stack visibility
Get mentored by infra engineers and certified with portfolio-grade AIOps systems
Limited resume value or production exposure
₹80,000 one-time with job prep, mentor feedback, and placement assistance till hired
No structured outcome tracking or job support
One-time payment
₹80,000
Advanced program • Live training • Placement support
AIOps course fees are 80,000 INR for an advanced program with live mentorship, production projects, and comprehensive placement support.
No entrance exam. No lengthy admissions. Four steps to get started.
Get a 15-minute overview of the curriculum, tooling, and how it maps to your current stack.
ScheduleAlign the program with your role — AI engineer, MLOps, SRE, or data infrastructure lead.
Call UsComplete payment (one-time or EMI). Get immediate access to pre-work and cohort onboarding.
Join your cohort, set up your dev environment, and start deploying from Month 1.
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End-to-end GenAI engineering: Transformers → agents, multimodal RAG, diffusion, ViT, VLMs, eval & deployment.
