Flagship project
End-to-End ML CI/CD Pipeline
Build a complete CI/CD pipeline that trains, tests, packages, and deploys a machine learning model automatically.
- MLOps
- CI/CD
- Docker
MLOps Crash CourseHigh-Demand
Complete MLOps & Model Monitoring Crash Course in Amritsar – Zero to Job Ready with Real Projects & Placement Support
MLOps & Model Monitoring Crash Course course in AmritsarJob-oriented MLOps training built on real production scenarios — small batches, daily hands-on labs and 100% placement assistance.
3 / 6 MonthsBeginnerHybrid
3 / 6 Months
Duration
Weekday/Weekend
Mode
12th Pass/Any Grad
Eligibility
10-12 Students
Batch Size
Admissions open
MLOps & Model Monitoring Crash Course · 3 / 6 Months
Batch timings
- Morning
- Evening
- Weekend
- Live online
About the programme
Course overview
Course overviewtechcadd's Complete MLOps & Model Monitoring Crash Course in Amritsar is a practical, project-first introduction to MLOps engineering for beginners and career switchers.
It opens with ML lifecycle fundamentals, experiment tracking, and data versioning, then moves into CI/CD for ML, containerization, model deployment, drift detection, ML observability, and automated retraining on real datasets.
You finish with a documented MLOps portfolio, a capstone project, CV preparation and interview coaching.
Every business in Amritsar — from traditional manufacturers near Focal Point to cutting-edge IT firms on Airport Road, from local startups to healthcare providers scaling across Punjab — is now shipping AI models into production. The problem isn't building a model; it's keeping it alive once it's live. A model that performed brilliantly in a notebook quietly decays the moment real-world data shifts. Data drift, concept drift, feature skew, silent data quality failures, and unmonitored model degradation turn working AI systems into liability-generating black boxes. The real skill isn't just training models — it's operating them. That's MLOps. It's the ability to version data, track experiments, automate pipelines, deploy models, monitor them in production, and retrain them before they fail. Companies are desperate for professionals who can bridge the gap between data science and production engineering, cut deployment time from months to days, and guarantee that AI systems stay accurate, reliable, and observable long after launch.
techcadd's Complete MLOps & Model Monitoring Crash Course in Amritsar is built on the tools and workflows you actually see in job descriptions and platform engineering briefs — MLflow, DVC, Docker, Kubernetes, Airflow, Evidently AI, and Prometheus/Grafana — not abstract theory. You work with real ML pipelines, real production datasets, and real monitoring dashboards from the very first module and finish with a documented MLOps portfolio, not just a certificate. The programme runs in a 3-month foundation track, with an optional 6-month extended track for deeper mastery. Batches are capped at 10–12 students, and every project you build is reviewed weekly by mentors who still work on live ML platforms and production monitoring challenges.
Placement assistanceLive ML labsInternship certificate
- Duration
- 3 / 6 Months
- Level
- Beginner
- Mode
- Hybrid
- Batches
- Morning, evening & weekend
Module by module
The curriculum, module by module
Module by module curriculumThe Complete MLOps & Model Monitoring Crash Course is a nested ladder — the longer track includes every module from the shorter track and adds more depth. No module is repeated; you continue building on the skills you have already learned.
- Module 01 of 06
ML Lifecycle, Experiment Tracking & Data Versioning
The foundational knowledge every MLOps Engineer assumes you already know — how ML projects move from notebook to reproducible pipeline.
Topics covered
- ML lifecycle
- MLOps principles
- experiment tracking
- data versioning
- reproducibility
- feature stores
- pipeline orchestration
- model registry basics
Tools in this module
MLflowDVCWeights & BiasesGitPythonFeastJupyterYou finish withA documented report on experiment tracking and data versioning for a real ML project
- Module 02 of 06
CI/CD for ML, Containers & Model Deployment
Core MLOps Engineering — building automated pipelines that train, test, package, and deploy models reliably.
Topics covered
- CI/CD for ML
- Docker
- Kubernetes
- model serving
- REST APIs
- batch vs real-time inference
- model registry
- pipeline automation
- rollback strategies
Tools in this module
GitHub ActionsDockerKubernetesFastAPIBentoMLMLflowSeldonAirflowYou finish withAn end-to-end CI/CD pipeline that deploys a machine learning model to a live endpoint
- Module 03 of 06
Capstone Project, Career & Interview Readiness
A capstone project combining pipeline design, model deployment, and business insights, plus career readiness for MLOps roles.
Topics covered
- End-to-end MLOps project
- problem framing
- ML system design
- portfolio development
- resume
- LinkedIn profile
- interview readiness
- MLOps certification exam preparation
Tools in this module
PythonGitHubPortfolioResumeLinkedInMock interviewsYou finish withA complete capstone project with documentation, and an interview-ready MLOps portfolio and resume
- Module 04 of 06
Model Monitoring, Drift Detection & Data Quality
Advanced model monitoring and drift detection — catching silent model degradation before it reaches customers.
Topics covered
- Model monitoring
- data drift
- concept drift
- feature skew
- data quality checks
- performance tracking
- alerting
- shadow deployment
- A/B testing
Tools in this module
Evidently AIWhyLabsArizeNannyMLGreat ExpectationsPrometheusGrafanaYou finish withA drift detection and monitoring system for a live ML model with alerting
- Module 05 of 06
ML Observability, Retraining & Production Reliability
ML observability and production reliability — making ML systems self-healing, auditable, and cost-efficient at scale.
Topics covered
- ML observability
- automated retraining
- continuous training
- pipeline observability
- logging
- cost optimization
- incident response
- SLA monitoring
Tools in this module
Apache AirflowKubeflowGrafanaPrometheusMLflowLokiKubernetesYou finish withAn automated retraining pipeline with observability dashboards and documentation
- Module 06 of 06
Advanced Capstone, Career & Interview Readiness
An advanced capstone project combining CI/CD for ML, model deployment, drift detection, ML observability, and business impact, plus career readiness for senior MLOps roles.
Topics covered
- Full MLOps project lifecycle
- problem framing
- ML platform strategy
- system deployment
- portfolio development
- resume
- LinkedIn profile
- interview readiness
- MLOps certification exam preparation
Tools in this module
GitHubPortfolioResumeLinkedInMock interviewsCase studiesYou finish withA complete advanced capstone project with documentation, and an interview-ready MLOps portfolio and resume
What you build
Projects that answer the interview question for you
Portfolio projectsEach project is reviewed individually, and the review notes go into your portfolio documentation.
Real-Time Model Monitoring Dashboard
Create a monitoring dashboard tracking model performance, latency, and data quality with live alerting.
- Model Monitoring
- Grafana
- Prometheus
Drift Detection & Retraining System
Build a drift detection system using Evidently AI that triggers automated retraining when thresholds are breached.
- Drift Detection
- Evidently AI
- Retraining
Expert Capstone Project
Complete an end-to-end MLOps project with CI/CD, deployment, monitoring, drift detection, and a production readiness plan.
- Multi-Domain
- Portfolio
- MLOps Strategy
The working loop
Learn it. Build it. Make it yours.
Learn it. Build it. Make it yours.Every project moves through the same loop: understand the brief, build it with guidance, then explain the decisions behind your work. The certificate is the receipt — the portfolio is the point.
- 01
Industry-focused curriculum
You learn it the way professional ML platform teams use it, with the workflow and standards an MLOps Engineer expects.
End-to-End ML CI/CD Pipeline
- 02
Real datasets and projects
Every module runs on real ML models and real production data with real business questions and real outcomes, not a slide or a screenshot.
Real-Time Model Monitoring Dashboard
- 03
Mentor-reviewed deliverables
Each module ends with a working pipeline reviewed weekly by a mentor who still works on live ML platform problems.
Drift Detection & Retraining System
- 04
Real projects and scenarios from the start
Every ML pipeline is built on real-world data briefs and measured through reliability, latency, and business impact, the way MLOps teams work.
Expert Capstone Project
The honest pitch
Your months should build more than a certificate.
Your months should build more than a certificate.A course is worth the time you give it only if you finish with real pipelines you can show and MLOps frameworks you can defend.
Project completion certificate
Issued for the project you build and defend
Course completion certificate
Carries a certificate ID and a scan-to-verify code
Why choose MLOps & Model Monitoring Crash Course
Why students choose techcadd
Why students choose techcaddWhat makes this course at techcadd different from a typical alternative is that every module is built on a real MLOps project and documented to professional standards, so you finish with an end-to-end MLOps portfolio rather than disconnected certificates.
- 01
Mentors who still work on live ML platforms
Not full-time lecturers — trainers who are still doing the job they teach.
- 02
One project, extended through every module
You start the project in Module 01, extend it through every module, and complete the capstone by Module 03 or 06.
- 03
Small batch sizes
10–12 students per batch ensures every student gets personalised feedback on every deliverable.
- 04
Nested, no-repeat curriculum
Longer programs include everything from shorter tracks and add more depth — you never repeat a module.
- 05
Documented internship experience
Every student receives a documented internship certificate alongside their course certificate.
- 06
Placement support
Resume and LinkedIn preparation, interview coaching, and active placement assistance for graduates.
Eligibility
Who can join the MLOps & Model Monitoring Crash Course course
Who can join the MLOps & Model Monitoring Crash Course courseThis course works for a specific starting point — here's who gets the most out of it.
- Educational backgroundNo specific degree or stream required — school leavers, graduates and career switchers all qualify.
- Prior experienceNone required. The programme opens at beginner level and builds from there across 3 / 6 months.
- AgeNo upper age limit. What matters is being able to commit to the full course rather than dropping off midway.
- EquipmentA laptop or desktop with a stable internet connection for classroom or live-online batches.
- Time commitmentAble to attend regularly across 3 / 6 months, in a morning, evening or weekend batch.
Where it leads
The roles this portfolio opens
Job titles vary by company, but the underlying expectations do not. Each destination below maps to work you will have already done during the programme.
Tools you will actually use
The working stack for MLOps & Model Monitoring Crash Course
Tools and technologies coveredEvery tool below is installed, configured and used by you during the course — not demonstrated on a slide. You leave able to set up your own environment from scratch.
20tools covered
Experiment Tracking & Model Registry
- MLflow
Data Version Control
- DVC
Experiment Tracking
- Weights & Biases
ML Orchestration
- Kubeflow
Pipeline Orchestration
- Apache Airflow
Containerization
- Docker
Container Orchestration
- Kubernetes
Model Serving API
- FastAPI
Model Serving
- BentoML
Model Deployment
- Seldon
Model Monitoring
- Evidently AI
ML Observability
- WhyLabs
- Arize
Drift Detection
- NannyML
Data Quality
- Great Expectations
Metrics & Alerting
- Prometheus
Monitoring Dashboards
- Grafana
Feature Store
- Feast
CI/CD Automation
- GitHub Actions
Programming language
- Python
Version control
- Git
Code hosting
- GitHub
Code editor
- VS Code
Notebook environment
- Jupyter
Cloud notebooks
- Google Colab
Certification
Get certified in MLOps & Model Monitoring Crash Course
Get certified in MLOps & Model Monitoring Crash CourseIssued on completion against the modules you finished and the projects you submitted — plus an internship letter where the industrial training track applies. Shareable to LinkedIn, and verifiable by an employer who calls the Amritsar desk.
- Module-wise assessment, not attendance-based
- Project submissions logged against your certificate
- Internship letter on the industrial training track
- Verifiable directly with the campus office
Future scope
Where this takes you over the next five years
Where this takes you over the next five yearsThe skill does not stand still, and neither does what it is worth.
- MLOps is the new baselineEmployers increasingly assume data science knowledge will extend to deployment and pipeline engineering.
- Observability and drift detection are the new layersTeams expect ML engineers to integrate model monitoring, drift detection, and automated retraining, not just train models.
The objective is practical knowledge rather than memorised concepts.
Due diligence
How techcadd compares
How techcadd comparesWhat actually differs between this course at techcadd and a typical alternative.
| What you should ask | techcadd Amritsar | Other institutes |
|---|---|---|
| Live project work | Every module, from week one | Varies, often only at the end |
| Learning format | One project extended through every module | Topics demonstrated separately, rarely combined |
| Tools used | MLflow, Docker, Kubernetes, Evidently AI, Python | Usually limited to basic notebooks |
| Batch size | 10–12 students per batch | Often 25–40 students per batch |
| CI/CD & Deployment | Live integration, real pipelines | Rarely covered |
| Advanced topics | Model Monitoring, Drift Detection, Observability | Often only theory |
| Automation & Scripts | Automated retraining, Monitoring alerts | Rarely covered |
| Portfolio | 6 documented project deliverables | Often just assignments, not portfolio-ready work |
| Certificate | Completion certificate + documented internship | Only a completion certificate |
| Placement support | Resume, LinkedIn, interview coaching, active placement | Generic referrals, no dedicated coaching |
Every comparison here is about substance, not marketing language.
Before you enrol
Questions we get asked at the admissions desk
Frequently asked questionsIf something here is not covered, the Amritsar desk will answer it directly — no call-back queue.
No. The programme opens at beginner level and assumes no background beyond comfort with a computer. Students who already have some exposure move through the early modules faster and spend the saved time on project work.
