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🤖 Bootcamp

AIOps & MLOps Bootcamp

Build AI-Powered Operations and Production ML Pipelines.

A practical bootcamp covering AI-powered operations — anomaly detection, automated incident triage, predictive alerting — combined with end-to-end MLOps: training pipelines, model serving, drift monitoring and CI/CD for ML models.

Duration10 Weeks

Total Hours80 Hours

LevelIntermediate

FormatOnline + Offline

CertificateYes

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Delivery Format

Train How You Learn Best

💻 Online — Live Instructor-Led

Live sessions via Zoom with a ZippyOPS practitioner. 4 sessions per week, all recordings provided. Ask questions in real time and get code reviewed live.

🏢 Offline — Chennai Lab Sessions

In-person at ZippyOPS Chennai labs. Mon–Fri batches. Lab machines provided. Direct hands-on access to instructors throughout every session.

Who Should Attend

Is This Bootcamp Right for You?

✅ This bootcamp is for you if…

DevOps and SRE engineers wanting to apply ML to operational problems
Data scientists wanting to get models to production reliably
Platform engineers building ML infrastructure for data science teams
Engineers at companies beginning their AI operations journey

📋 Prerequisites

Python proficiency — comfortable writing scripts and using libraries
Working knowledge of Kubernetes and containers
Basic understanding of metrics and time-series data
Familiarity with Git and CI/CD concepts

Full Curriculum

What You'll Learn — Week by Week

AIOps Fundamentals

Week 1

▾

What AIOps actually is — and what it is not
The AIOps capability model — from reactive monitoring to predictive operations
Alert fatigue analysis — measuring and quantifying alert noise
Log analytics — structured log processing and pattern extraction
Event correlation fundamentals — grouping related alerts into incidents
Lab: Analyse 30 days of production alert data — identify top 5 noise sources and design suppression rules

Anomaly Detection for Operations

Week 2

▾

Time series anomaly detection — statistical baselines, seasonality and trend decomposition
Prometheus anomaly detection with VictoriaMetrics ML and MAD scoring
Isolation Forest, LSTM and Prophet for operational metric anomaly detection
Tuning anomaly detectors — precision vs recall trade-offs in operations
Dynamic baseline alerting vs static threshold alerting
Lab: Train an anomaly detection model on Prometheus metrics and compare against static threshold alerting

Alert Correlation & Noise Reduction

Week 3

▾

Alert correlation algorithms — temporal, causal and topological correlation
SigNoz — AI-powered alert correlation and noise reduction
Building custom alert correlation engines in Python
Dependency graph-based correlation — correlating alerts to root cause services
Lab: Build an alert correlation system reducing 1,000 daily alerts to 20 actionable notifications

Automated Incident Triage & Remediation

Week 4

▾

Automated runbook execution — Ansible, AWS Lambda and Kubernetes operators
PagerDuty automation — event routing and webhook-triggered runbooks
Building a remediation library — safe automated responses for known failure patterns
Human-in-the-loop automation — when to automate and when to always escalate
Lab: Build an automated remediation system handling 5 common failure patterns without human intervention

Predictive Operations

Week 5

▾

Predictive alerting — warning before failure, not reacting after it
Capacity prediction — forecasting resource exhaustion with Prophet and LSTM
Failure prediction from logs — using NLP to extract early warning signals
Integrating predictions into PagerDuty with enriched alert context
Lab: Build a predictive alerting system detecting 3 known failure patterns 20 minutes before impact

MLOps Foundations

Week 6

▾

The MLOps problem — why data science notebooks don't reach production
The ML lifecycle — data, training, evaluation, serving, monitoring and retraining
MLflow — experiment tracking, model registry and artifact management
DVC — data version control for reproducible ML pipelines
Feature stores — Feast for feature management and serving
Lab: Build a complete tracked, reproducible ML experiment pipeline using MLflow and DVC

CI/CD for ML Models

Week 7

▾

Automated ML pipelines with Apache Airflow and Kubeflow Pipelines
CI/CD for ML — triggering retraining on data drift, code changes or schedule
Model evaluation gates — automated quality checks before model promotion
A/B testing for ML models — traffic splitting and statistical significance
ZenML for ML pipeline orchestration and stack management
Lab: Build a full CI/CD pipeline for an ML model — training, evaluation, A/B testing and production promotion

Model Serving & Inference

Week 8

▾

Model serving options — REST APIs, batch inference and streaming inference
BentoML — packaging and deploying models with built-in versioning
Seldon Core — Kubernetes-native model serving with A/B and canary deployment
Scaling model serving — autoscaling based on inference latency and throughput
Lab: Deploy a production ML model with BentoML on Kubernetes with canary deployment and latency SLO monitoring

ML Monitoring & Drift Detection

Week 9

▾

Types of model drift — data drift, concept drift and prediction drift
Evidently — data quality, data drift and model performance monitoring
Automated retraining triggers — drift thresholds and business metric degradation
Building ML monitoring dashboards in Grafana
Lab: Implement full drift monitoring — detect drift, trigger retraining and validate new model before promotion

Capstone Projects

Week 10

▾

AIOps Capstone: AI-powered ops stack — anomaly detection, alert correlation, automated remediation, predictive alerting
MLOps Capstone: End-to-end MLOps pipeline for a fraud detection model — training, CI/CD, BentoML serving, drift monitoring
Live review of both capstone projects with ZippyOPS AIOps and MLOps engineers

On Completion

Earn Your ZippyOPS Certificate

🎓

ZippyOPS Certified AIOps & MLOps Engineer (ZCAME)

Tests practical skills in building AIOps detection systems and end-to-end MLOps pipelines through capstone projects covering both disciplines.

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Seats are limited per batch. Contact us to check availability and get full pricing for the next online or offline cohort.

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