Not just another course

Become a Real Software Engineer in 6 Months

AZ DEPLOY ACADEMY

First time in Belagavi

Flagship · codenameAZCRASH×1.0Full-stack · AI · DevOps · 6 moJump to batches →

Batch timings

Pick one batch — same curriculum, same depth. Each session runs 2 hours so you can stay consistent without burning out.

Morning batch

9:00 AM – 11:00 AM

Two focused hours before the workday. Ideal if you study or work in the afternoon or evening.

Afternoon batch

3:00 PM – 5:00 PM

Mid-day slot after lunch — good for college students with morning classes or professionals with flexible afternoons.

Evening batch

6:00 PM – 8:00 PM

After office hours for working professionals who want to upskill without quitting their job.

Timings shown in local time · Confirm your preferred batch when enrollment opens

Who this is for: paths toward AI Engineer · Data Engineer · Backend Developer · Frontend Developer · DevOps Engineer — one program, many doors; you go deep on the stack employers actually ship.

Full-Stack + AI + DevOps — All-in-One Program

Learn. Build. Deploy. Scale. Get hired.

Six months of structured learning plus hands-on practice: you won't stop at tutorials — you'll design APIs, tune databases, put services behind Nginx, containerize with Docker, automate with CI/CD, and deploy to a real cloud VPS — the same moving parts you'll see on the job.

Industry-ready track

High-level curriculum

One integrated six-month track. Each block below lists what you'll be able to do and recognize on the job — still high level, but concrete enough to compare with industry job descriptions. Open a section for the full topic list; week-by-week pacing stays in your batch syllabus.

6 mo

Structured program

Seats / batch

Project-first

Ship, not only watch

▹Python core: types, collections, functions, comprehensions, exceptions, and packaging (venv, pip, requirements).
▹Structure & OOP: modules, packages, classes, protocols vs inheritance, and organizing code for growth.
▹Quality habits: docstrings, typing basics, logging, and introductory tests (e.g. pytest) so refactors stay safe.
▹Linux shell: filesystem layout, permissions, processes, pipes, cron-style thinking, and reading logs.
▹Git end-to-end: clone → branch → commit → push, merge vs rebase (when each fits), `.gitignore`, and pull-request review flow.

▹PostgreSQL & SQL: SELECT patterns, JOINs, subqueries, GROUP BY, window functions, constraints, and transactions.
▹Performance mindset: indexes, EXPLAIN, avoiding N+1 patterns, and sane migration strategies.
▹Django data layer: models, relations, migrations, the ORM query API, and admin for rapid iteration.
▹Pandas-style workflows: load CSV/JSON, clean, transform, aggregate, merge — bridging analysis to “small ETL” thinking.
▹Data integrity: normalization vs denormalization trade-offs, keys, and when to push logic to SQL vs application code.

▹Django REST-style APIs: serializers, viewsets or class-based views, permissions, throttling, pagination, and filtering.
▹Authentication patterns: sessions vs tokens, password flows at a high level, and protecting sensitive routes.
▹Flask (and similar): blueprints, lightweight JSON APIs, and when a smaller service footprint makes sense.
▹HTTP & API design: status codes, idempotency, errors, versioning, OpenAPI/Swagger-style documentation for consumers.
▹Background work & integration: email/webhooks/file uploads (as applicable), idempotent handlers, and safe retries.

▹React fundamentals: components, JSX, hooks (state, effects, memoization), composition, and lifting state cleanly.
▹Routing & UX: client-side navigation, protected routes, loading and empty states, and form validation feedback.
▹State & data: Redux (or equivalent patterns), async fetching, caching basics, and avoiding unnecessary re-renders.
▹Tooling: Vite-based dev/build, environment config, and lint/format discipline so teams can onboard fast.
▹Quality bar: component tests, accessibility basics (keyboard, labels), and performance checks for real devices.

▹Server operations: SSH, users & permissions, systemd/services, log locations, disk, and basic troubleshooting.
▹Nginx: reverse proxy to app servers, static assets, gzip/brotli awareness, and TLS termination concepts.
▹Containers: Docker images & layers, multi-stage builds, Compose for local parity, and registry basics.
▹Kubernetes orientation: pods, deployments, services, ConfigMaps/Secrets, ingress at a practitioner level (not certification cram).
▹Infrastructure as code: Terraform (or similar) for providers, modules, state, and safe plan/apply workflows.
▹AWS overview: EC2, S3, RDS/VPC at a “what to click and why” level; cost and security guardrails in mind.
▹CI/CD: GitHub Actions (and Jenkins-style thinking)—build, test, artifact, deploy hooks, secrets in pipelines, rollbacks.

▹Data platforms: batch vs streaming, partitions, idempotent pipelines, and lake vs warehouse trade-offs at a high level.
▹Distributed processing: Spark/PySpark-style transformations (filter, join, aggregate) and when scale tooling matters.
▹ML foundations: supervised vs unsupervised, train/validate/test, metrics, overfitting, and responsible data use.
▹Deep learning touchpoints: tensors, training loops, CNN/RNN intuition, and using PyTorch-style APIs with pretrained models.
▹Gen AI & LLMs: prompting, system prompts, tool use, cost/latency limits, and guardrails (PII, safety, monitoring).
▹RAG & apps: chunking, embeddings, vector search concepts, and wiring LLM calls into Django/React backends safely.
▹Ecosystem: Hugging Face Hub, OpenAI-compatible APIs, and Gemini-style multimodal awareness where relevant to products.

Third-party names and logos belong to their respective owners; they are shown only to identify tools and topics we cover, not to suggest endorsement or partnership. Curriculum scope, depth, and order are defined in your official batch syllabus and may be updated.

[WHY_AZDEPLOY]

Where ambition meets real engineering

Most institutes optimize for completion certificates. We optimize for confidence in interviews, clarity under production pressure, and proof you can ship — because that's what gets you hired and promoted.

Years in industry

500+

Products deployed

Students per batch

REAL_SHIPPING

Mentors who build products, not slide decks

Trainers carry years of shipping software — you learn what breaks in prod, not just what passes tests.

Expect honest war stories: outages, bad deploys, performance cliffs, and how teams recover. That context is what interview panels listen for — and what separates engineers from tutorial finishers.

DEPTH_FIRST

Fundamentals before AI glitter

Strong systems thinking and debugging so AI accelerates you instead of masking gaps.

We drill HTTP, databases, concurrency basics, and reading error messages under pressure. When you reach for AI, it’s to move faster on a foundation you already understand — not to guess your way through fundamentals.

PLACEMENT_REALITY

Curriculum shaped like real hiring

System design, trade-offs, and hands-on depth aligned with how teams actually hire.

You practice explaining architecture, defending schema choices, and walking through failures. The goal isn’t a certificate — it’s sounding credible when someone asks “what happens when traffic 10×’s overnight?”

SMALL_BATCHES

Limited seats — feedback that scales with you

Room for code review, 1:1 mentorship, and accountability — not anonymous lecture halls.

Smaller cohorts mean your questions get answered, your projects get reviewed, and you can’t hide. We optimize for depth and follow-through, not packing the room.

SIX_MONTH_ARC

Six months to go from syntax to deploy

Structured path from UI + API + DB to Linux, Nginx, Docker, CI/CD, and cloud VPS.

You’re not racing through frameworks in isolation — you connect the full path a feature travels before users touch it. That’s the muscle memory companies expect from mid-level engineers.

ONE_TRACK_MANY_DOORS

One program — choose where you lean

Same ongoing track; mentors help you stress build, ops, or AI based on how you fit.

Whether you gravitate toward product code, infrastructure, or AI-enabled features, you stay inside one coherent journey — so your story stays sharp when you interview for the role you actually want.

Ask us on WhatsApp →

/// Tell us your background — we'll help you see how this program maps to the role you want.

Why most students fail?

Many courses stop at syntax. Employers need people who can own a feature end-to-end — from UI to DB to deploy.

•Toy projects only — Todo apps and calculators don't teach scaling, security, or teamwork.
•No production exposure — no logs, no crashes, no real users — so interviews fall apart on "what happens when…?"
•Deployment is a black box — if you've never SSH'd into a server or read an Nginx error, ops questions are guesswork.
•Production vs dev — env vars, secrets, migrations, and rollbacks aren't covered in hello-world courses.
•Interview gap — nervous explaining system design, trade-offs, and past failures because nothing was ever shipped.

What we do differently

We train you like you're joining a real product team — architecture, shipping, and iteration matter as much as code style.

•Production-shaped projects — multi-service thinking, realistic constraints, not just "it works on my machine."
•Live startup-style work — priorities change, bugs appear, you learn to triage and document.
•System architecture — how pieces talk: browser → API → DB → cache → queue → deploy pipeline.
•Real deployment environment — Linux shell, process managers, reverse proxy, HTTPS, env-based config.
•Engineer habits — code review mindset, git discipline, reading docs, and clear communication under time pressure.

30% off for first 10 students

Top performer wins MacBook Pro

Offer applies when enrollment opens — ask on WhatsApp for details.

Enrollment opening soon

Full-Stack + AI + DevOps

One program — enquiry only for now. We'll notify you when enrollment opens. Mention your preferred batch timing (morning / afternoon / evening) on WhatsApp.

Enquiry — WhatsApp

What you'll learn — roadmap

Six months to train like a real software engineer — production systems, not toy apps. Depth below follows the same three pillars as the flyer.

Full-Stack + AI + DevOps — 6 months

What you will master — Learn. Build. Deploy. Scale. Get hired.

Below is the skill stack in teaching order; batch timings (morning / afternoon / evening) use the same syllabus.

DevelopmentFrontend · Backend · Database

React: components, hooks, state, routing, forms, consuming REST APIs, modern UI patterns
Django: project/apps, MVT, ORM, auth, REST APIs, error handling, structure for growth
System design: APIs, pagination, caching concepts, async where it fits, trade-offs
PostgreSQL: schema design, migrations, indexes, EXPLAIN, query tuning, avoiding N+1
End-to-end: feature from UI → API → DB with realistic constraints

DevOps & SystemsLinux · Nginx · Docker · Deployment

Linux (server level): shell, users & permissions, processes, logs, networking basics
Nginx: reverse proxy, static files, SSL concepts, upstreams, common prod patterns
Docker: images, Dockerfile, compose for local/prod parity, volumes & networks
CI/CD: automated test & build on push, staging vs production mindset
VPS on AWS: instance basics, SSH, deploy app, domain & HTTPS, service reliability

AI & DataReal use cases & fundamentals

AI in products: when to use APIs, costs, limits, safety & UX for AI features
Integrating AI with Django/React: request flow, keys, fallbacks
Data engineering basics: batch vs stream intuition, ETL-shaped thinking, quality
Connecting data work to PostgreSQL and APIs you already built

→ REAL SOFTWARE ENGINEER

Your journey — choose your focus

One ongoing 6-month intake. The sections below are pillars of that single program — use them to see where you fit (build-heavy, ops-heavy, AI-heavy, or the full arc). Not four different courses.

Full-Stack + AI + DevOps — All-in-One

One integrated 6-month track: you learn to ship full-stack apps (React + Django + PostgreSQL), operate them like production (Linux, Nginx, Docker, CI/CD, AWS VPS), and apply AI & data skills where they matter — not scattered mini-courses.

SAME_PROGRAM · PICK_EMPHASIS

Development

React: components, hooks, routing, state, modern UI patterns. Django: MVT, REST APIs, auth, system design for scalable backends. PostgreSQL: schema design, queries, indexes, and optimization for real traffic.

SAME_PROGRAM · PICK_EMPHASIS

DevOps & Systems

Linux at server depth — users, permissions, processes, networking. Nginx as reverse proxy and static server. Docker images & compose. CI/CD pipelines so every change is built and tested. Deploy to a VPS on AWS with HTTPS and monitoring basics.

SAME_PROGRAM · PICK_EMPHASIS

AI & Data

Practical AI: when to use APIs vs local models, prompt patterns, integrating AI into apps safely. Data engineering: pipelines, batch vs stream mindset, storage choices, and fundamentals that pair with PostgreSQL and your backend.

SAME_PROGRAM · PICK_EMPHASIS

Office

Plot no. 516, Main Road, Ramteerth Nagar, Lakshmipuri Layout, Auto Nagar, Belagavi, Karnataka 590016

VFF GROUP Building — First Floor

www.azdeploy.com

+91 82965 65587 +91 89712 44513 +91 73383 60607

Not just another course

Become a Real Software Engineer in 6 Months

AZ DEPLOY ACADEMY

First time in Belagavi

Flagship · codenameAZCRASH×1.0Full-stack · AI · DevOps · 6 moJump to batches →

Batch timings

Pick one batch — same curriculum, same depth. Each session runs 2 hours so you can stay consistent without burning out.

Morning batch

9:00 AM – 11:00 AM

Two focused hours before the workday. Ideal if you study or work in the afternoon or evening.

Afternoon batch

3:00 PM – 5:00 PM

Mid-day slot after lunch — good for college students with morning classes or professionals with flexible afternoons.

Evening batch

6:00 PM – 8:00 PM

After office hours for working professionals who want to upskill without quitting their job.

Timings shown in local time · Confirm your preferred batch when enrollment opens

Who this is for: paths toward AI Engineer · Data Engineer · Backend Developer · Frontend Developer · DevOps Engineer — one program, many doors; you go deep on the stack employers actually ship.

Full-Stack + AI + DevOps — All-in-One Program

Learn. Build. Deploy. Scale. Get hired.

Industry-ready track

High-level curriculum

6 mo

Structured program

Seats / batch

Project-first

Ship, not only watch

▹Python core: types, collections, functions, comprehensions, exceptions, and packaging (venv, pip, requirements).
▹Structure & OOP: modules, packages, classes, protocols vs inheritance, and organizing code for growth.
▹Quality habits: docstrings, typing basics, logging, and introductory tests (e.g. pytest) so refactors stay safe.
▹Linux shell: filesystem layout, permissions, processes, pipes, cron-style thinking, and reading logs.
▹Git end-to-end: clone → branch → commit → push, merge vs rebase (when each fits), `.gitignore`, and pull-request review flow.

▹PostgreSQL & SQL: SELECT patterns, JOINs, subqueries, GROUP BY, window functions, constraints, and transactions.
▹Performance mindset: indexes, EXPLAIN, avoiding N+1 patterns, and sane migration strategies.
▹Django data layer: models, relations, migrations, the ORM query API, and admin for rapid iteration.
▹Pandas-style workflows: load CSV/JSON, clean, transform, aggregate, merge — bridging analysis to “small ETL” thinking.
▹Data integrity: normalization vs denormalization trade-offs, keys, and when to push logic to SQL vs application code.

▹Django REST-style APIs: serializers, viewsets or class-based views, permissions, throttling, pagination, and filtering.
▹Authentication patterns: sessions vs tokens, password flows at a high level, and protecting sensitive routes.
▹Flask (and similar): blueprints, lightweight JSON APIs, and when a smaller service footprint makes sense.
▹HTTP & API design: status codes, idempotency, errors, versioning, OpenAPI/Swagger-style documentation for consumers.
▹Background work & integration: email/webhooks/file uploads (as applicable), idempotent handlers, and safe retries.

▹React fundamentals: components, JSX, hooks (state, effects, memoization), composition, and lifting state cleanly.
▹Routing & UX: client-side navigation, protected routes, loading and empty states, and form validation feedback.
▹State & data: Redux (or equivalent patterns), async fetching, caching basics, and avoiding unnecessary re-renders.
▹Tooling: Vite-based dev/build, environment config, and lint/format discipline so teams can onboard fast.
▹Quality bar: component tests, accessibility basics (keyboard, labels), and performance checks for real devices.

▹Server operations: SSH, users & permissions, systemd/services, log locations, disk, and basic troubleshooting.
▹Nginx: reverse proxy to app servers, static assets, gzip/brotli awareness, and TLS termination concepts.
▹Containers: Docker images & layers, multi-stage builds, Compose for local parity, and registry basics.
▹Kubernetes orientation: pods, deployments, services, ConfigMaps/Secrets, ingress at a practitioner level (not certification cram).
▹Infrastructure as code: Terraform (or similar) for providers, modules, state, and safe plan/apply workflows.
▹AWS overview: EC2, S3, RDS/VPC at a “what to click and why” level; cost and security guardrails in mind.
▹CI/CD: GitHub Actions (and Jenkins-style thinking)—build, test, artifact, deploy hooks, secrets in pipelines, rollbacks.

▹Data platforms: batch vs streaming, partitions, idempotent pipelines, and lake vs warehouse trade-offs at a high level.
▹Distributed processing: Spark/PySpark-style transformations (filter, join, aggregate) and when scale tooling matters.
▹ML foundations: supervised vs unsupervised, train/validate/test, metrics, overfitting, and responsible data use.
▹Deep learning touchpoints: tensors, training loops, CNN/RNN intuition, and using PyTorch-style APIs with pretrained models.
▹Gen AI & LLMs: prompting, system prompts, tool use, cost/latency limits, and guardrails (PII, safety, monitoring).
▹RAG & apps: chunking, embeddings, vector search concepts, and wiring LLM calls into Django/React backends safely.
▹Ecosystem: Hugging Face Hub, OpenAI-compatible APIs, and Gemini-style multimodal awareness where relevant to products.

[WHY_AZDEPLOY]

Where ambition meets real engineering

Years in industry

500+

Products deployed

Students per batch

REAL_SHIPPING

Mentors who build products, not slide decks

Trainers carry years of shipping software — you learn what breaks in prod, not just what passes tests.

Expect honest war stories: outages, bad deploys, performance cliffs, and how teams recover. That context is what interview panels listen for — and what separates engineers from tutorial finishers.

DEPTH_FIRST

Fundamentals before AI glitter

Strong systems thinking and debugging so AI accelerates you instead of masking gaps.

PLACEMENT_REALITY

Curriculum shaped like real hiring

System design, trade-offs, and hands-on depth aligned with how teams actually hire.

SMALL_BATCHES

Limited seats — feedback that scales with you

Room for code review, 1:1 mentorship, and accountability — not anonymous lecture halls.

Smaller cohorts mean your questions get answered, your projects get reviewed, and you can’t hide. We optimize for depth and follow-through, not packing the room.

SIX_MONTH_ARC

Six months to go from syntax to deploy

Structured path from UI + API + DB to Linux, Nginx, Docker, CI/CD, and cloud VPS.

You’re not racing through frameworks in isolation — you connect the full path a feature travels before users touch it. That’s the muscle memory companies expect from mid-level engineers.

ONE_TRACK_MANY_DOORS

One program — choose where you lean

Same ongoing track; mentors help you stress build, ops, or AI based on how you fit.

Ask us on WhatsApp →

/// Tell us your background — we'll help you see how this program maps to the role you want.

Why most students fail?

Many courses stop at syntax. Employers need people who can own a feature end-to-end — from UI to DB to deploy.

•Toy projects only — Todo apps and calculators don't teach scaling, security, or teamwork.
•No production exposure — no logs, no crashes, no real users — so interviews fall apart on "what happens when…?"
•Deployment is a black box — if you've never SSH'd into a server or read an Nginx error, ops questions are guesswork.
•Production vs dev — env vars, secrets, migrations, and rollbacks aren't covered in hello-world courses.
•Interview gap — nervous explaining system design, trade-offs, and past failures because nothing was ever shipped.

What we do differently

We train you like you're joining a real product team — architecture, shipping, and iteration matter as much as code style.

•Production-shaped projects — multi-service thinking, realistic constraints, not just "it works on my machine."
•Live startup-style work — priorities change, bugs appear, you learn to triage and document.
•System architecture — how pieces talk: browser → API → DB → cache → queue → deploy pipeline.
•Real deployment environment — Linux shell, process managers, reverse proxy, HTTPS, env-based config.
•Engineer habits — code review mindset, git discipline, reading docs, and clear communication under time pressure.

30% off for first 10 students

Top performer wins MacBook Pro

Offer applies when enrollment opens — ask on WhatsApp for details.

Enrollment opening soon

Full-Stack + AI + DevOps

One program — enquiry only for now. We'll notify you when enrollment opens. Mention your preferred batch timing (morning / afternoon / evening) on WhatsApp.

Enquiry — WhatsApp

What you'll learn — roadmap

Six months to train like a real software engineer — production systems, not toy apps. Depth below follows the same three pillars as the flyer.

Full-Stack + AI + DevOps — 6 months

What you will master — Learn. Build. Deploy. Scale. Get hired.

Below is the skill stack in teaching order; batch timings (morning / afternoon / evening) use the same syllabus.

DevelopmentFrontend · Backend · Database

React: components, hooks, state, routing, forms, consuming REST APIs, modern UI patterns
Django: project/apps, MVT, ORM, auth, REST APIs, error handling, structure for growth
System design: APIs, pagination, caching concepts, async where it fits, trade-offs
PostgreSQL: schema design, migrations, indexes, EXPLAIN, query tuning, avoiding N+1
End-to-end: feature from UI → API → DB with realistic constraints

DevOps & SystemsLinux · Nginx · Docker · Deployment

Linux (server level): shell, users & permissions, processes, logs, networking basics
Nginx: reverse proxy, static files, SSL concepts, upstreams, common prod patterns
Docker: images, Dockerfile, compose for local/prod parity, volumes & networks
CI/CD: automated test & build on push, staging vs production mindset
VPS on AWS: instance basics, SSH, deploy app, domain & HTTPS, service reliability

AI & DataReal use cases & fundamentals

AI in products: when to use APIs, costs, limits, safety & UX for AI features
Integrating AI with Django/React: request flow, keys, fallbacks
Data engineering basics: batch vs stream intuition, ETL-shaped thinking, quality
Connecting data work to PostgreSQL and APIs you already built

→ REAL SOFTWARE ENGINEER

Your journey — choose your focus

One ongoing 6-month intake. The sections below are pillars of that single program — use them to see where you fit (build-heavy, ops-heavy, AI-heavy, or the full arc). Not four different courses.

Full-Stack + AI + DevOps — All-in-One

SAME_PROGRAM · PICK_EMPHASIS

Development

SAME_PROGRAM · PICK_EMPHASIS

DevOps & Systems

SAME_PROGRAM · PICK_EMPHASIS

AI & Data

SAME_PROGRAM · PICK_EMPHASIS

Office

Plot no. 516, Main Road, Ramteerth Nagar, Lakshmipuri Layout, Auto Nagar, Belagavi, Karnataka 590016

VFF GROUP Building — First Floor

www.azdeploy.com

+91 82965 65587 +91 89712 44513 +91 73383 60607

Become a Real Software Engineer in 6 Months

Full-Stack + AI + DevOps — All-in-One Program

High-level curriculum

Python & engineering foundations

Data & persistence

APIs & backend services

Modern frontend & UX

DevOps, cloud & delivery

Data platforms, AI & Gen AI

Where ambition meets real engineering

Mentors who build products, not slide decks

Fundamentals before AI glitter

Curriculum shaped like real hiring

Limited seats — feedback that scales with you

Six months to go from syntax to deploy

One program — choose where you lean

Why most students fail?

What we do differently

Full-Stack + AI + DevOps

What you'll learn — roadmap

Full-Stack + AI + DevOps — 6 months

Your journey — choose your focus

Full-Stack + AI + DevOps — All-in-One

Development

DevOps & Systems

AI & Data

Office

Become a Real Software Engineer in 6 Months

Full-Stack + AI + DevOps — All-in-One Program

High-level curriculum

Python & engineering foundations

Data & persistence

APIs & backend services

Modern frontend & UX

DevOps, cloud & delivery

Data platforms, AI & Gen AI

Where ambition meets real engineering

Mentors who build products, not slide decks

Fundamentals before AI glitter

Curriculum shaped like real hiring

Limited seats — feedback that scales with you

Six months to go from syntax to deploy

One program — choose where you lean

Why most students fail?

What we do differently

Full-Stack + AI + DevOps

What you'll learn — roadmap

Full-Stack + AI + DevOps — 6 months

Your journey — choose your focus

Full-Stack + AI + DevOps — All-in-One

Development

DevOps & Systems

AI & Data

Office