Linux & Cloud Foundations
Outcome Work confidently in a terminal, script routine tasks and launch your first infrastructure on AWS.
DevOps & Cloud program00 Live online · India, US & UK
Shift into tech with engineers who do the work.
Live online courses in AI, DevOps and Data Engineering for students, graduates and working professionals. Build real projects in batches of ten, with cloud labs, code reviews and placement assistance.
Live weekend batches. Ten learners at most. Taught by working engineers.
devops-lab — bash
AWS · EKS
$
kubectl apply -f k8s/deploy.yaml
deployment.apps/shop-api created
$
kubectl rollout status deploy/shop-api
deployment "shop-api" successfully rolled out
3/3 replicas ready · [review]
fig. 00 — a deployment from a live lab
Illustrative- ① build
alongside the instructor - ② break
debug real failures live - ③ ship
deploy and verify on AWS
The curriculum covers
- 01 Linux, Git and cloud foundations
- 02 Docker, Kubernetes and CI/CD
- 03 Terraform and AWS infrastructure
- 04 Machine learning and generative AI
- 05 Data pipelines with Spark and Kafka
01 What you'll learn
From your first command to production systems.
Five capabilities across our programs, taught live in the order you need them. Each one ends with something you can run, show and explain.
Containers & Kubernetes
Outcome Package applications in containers and run them on Kubernetes with deployments, services, ConfigMaps and Secrets.
Kubernetes complete guideCI/CD & Infrastructure as Code
Outcome Automate builds, tests and deployments, and provision cloud infrastructure from code you can review.
Jenkins CI/CD pipeline tutorialMachine Learning & Generative AI
Outcome Train and evaluate models, then build LLM applications that answer from your own documents.
AI & Machine Learning programData Pipelines at Scale
Outcome Build batch and streaming pipelines on AWS, and explain your design choices the way interviewers expect.
Data Engineering program02 Flagship program
DevOps & Cloud: From Linux to Production
Master the complete DevOps lifecycle, from containers and orchestration to CI/CD and cloud infrastructure, and finish with projects you can show employers.
- Level
- Beginner to advanced, in three levels
- Format
- Live online, weekend batches of up to ten
- Live hours
- 20, 50 or 120 hours, by level
- Modules
-
- 01 Linux & Shell Scripting
- 02 Git & Docker
- 03 Kubernetes Orchestration
- 04 CI/CD Pipelines
- 05 Terraform & Infrastructure as Code
- 06 Cloud & Monitoring
- Project
- Capstone: a complete DevOps pipeline for a full-stack application
- Fees
- ₹15,000 · ₹35,000 · ₹70,000 for levels 1, 2 and 3
Fees are one-time, per level. Placement assistance is included; it is support, not a job guarantee.
View Program Details
Fig. — capstone delivery pipeline
Delivery path
git pushfeature branch, pull request, review
ci · jenkinsbuild, test and scan on every push
image · dockerversioned and pushed to a registry
argocdGitOps sync
eksrolling deploys
terraformVPC, nodes, IAM
Cross-cutting
- helm — packaged, versioned releases
- secrets — ConfigMaps and Secrets
- metrics — Prometheus scrapes every service
- dashboards — Grafana panels and alerts
- ansible — repeatable server configuration
Jenkinsfile
Module 04
1pipeline {2 agent any3 stages {4 stage('Build') { steps { sh 'docker build -t shop-api .' } }5 stage('Deploy') { steps { sh 'kubectl apply -f k8s/' } }6 }7}Illustrative excerpt
03 Learning paths
Choose your learning path.
Four live programs built on the same foundations. Pick the kind of work you want to do; each path sets out its stages, levels and fees.
Book a free counselling callAI & Machine Learning
Route · 5 stages
-
01
Python & Maths
NumPy, Pandas, linear algebra and statistics
-
02
Machine Learning
Supervised and unsupervised learning, XGBoost, evaluation
-
03
Deep Learning
TensorFlow, PyTorch, CNNs and Transformers
-
04
Generative AI
LLMs, prompt engineering, RAG and LoRA fine-tuning
-
05
Deploy & MLOps
AWS SageMaker, Docker, Kubernetes and CI/CD
Who it's for
Fresh graduates, career switchers and working professionals. No coding background needed: Python is taught from scratch.
Suggested pace
Three levels of 40, 80 or 120 live hours, in weekend batches.
DevOps & Cloud
Route · 5 stages
-
01
Linux & Shell
Linux fundamentals, bash scripting and networking
-
02
Git & Docker
Version control, containers and Docker Compose
-
03
Kubernetes
Deployments, services, ConfigMaps and Helm
-
04
CI/CD
Jenkins, GitHub Actions, ArgoCD and GitOps
-
05
Terraform & Cloud
AWS, Azure, Prometheus and Grafana
Who it's for
Anyone starting or switching into DevOps: fresh graduates, support and testing professionals, and developers. No prior experience required.
Suggested pace
Three levels of 20, 50 or 120 live hours, in weekend batches.
Data Engineering
Route · 5 stages
-
01
SQL & Python
PostgreSQL, window functions, Pandas and APIs
-
02
ETL on AWS
S3, Glue, Lambda and Amazon Redshift
-
03
Big Data
Apache Spark, PySpark and Airflow
-
04
Streaming
Kafka, CDC with Debezium, Structured Streaming
-
05
Modern Platforms
Databricks, Snowflake and dbt
Who it's for
People switching from BPO, manual testing, support or non-IT roles. Level 1 starts from "what is a table".
Suggested pace
Two levels of two months each, 32 and 48 live hours. Weekend and weekday batches.
Generative AI
Route · 5 stages
-
01
LLM Fundamentals
Tokens, context windows and choosing a model
-
02
Prompt Engineering
Structured prompts, few-shot and evaluation
-
03
RAG & Vector DBs
Chunking, embeddings and grounded answers
-
04
LangChain Apps
Tool calling, memory and an intro to agents
-
05
Fine-tune & Deploy
LoRA, FastAPI, Docker and AWS
Who it's for
Developers who have called an LLM API, data analysts, and freshers with some Python who want deployed projects.
Suggested pace
Ten weeks of live weekend classes. Plan for 10 to 15 hours a week, including class.
Levels and fees
04 Learn by building
Your portfolio should prove what you can build.
Every program ends in capstone projects you deploy and document. The diagrams here are illustrative.
See the capstone list
Fig. — CI/CD pipeline
Build · on every push
commitfeature branch
builddocker image
testunit + lint
scanimage checks
pushto registry
shared registry
Deploy · on merge
syncargocd
rollouteks, rolling
probehealth checks
observeprometheus
liverelease + dashboards
P.01
DevOps
Capstone
Complete DevOps Pipeline
Take a full-stack application from a git push to production: automated builds and tests, container images, GitOps deployment to Kubernetes, and monitoring you can show in an interview.
View the DevOps program
Fig. — retrieval pipeline
-
1load documents
-
2chunk
-
3embed + index
-
4retrieve top-k
-
5answer + cite
↩ say "not found" when the answer isn't there
P.02
Generative AI
Capstone
RAG Document Q&A System
Upload a set of documents, ask questions, and get grounded answers with citations back to the source: the pattern most generative AI hiring is about.
View the Generative AI course
Fig. — streaming layout
Illustrative
kafkaevent topics
bronzeraw events
silvercleaned, deduplicated
goldbusiness tables
power bidashboards
spark structured streaming
✓ quality checks
airflow — schedules, retries and SLA alerts across every layer
P.03
Data Engineering
Level 2
Real-Time Kafka to Delta Lake Pipeline
Stream events through Kafka into Spark Structured Streaming, land them in Delta Lake with a medallion layout, and add orchestration and data-quality checks you can defend in an interview.
View the Data Engineering program05 The learning method
Learn it the way you'll do it at work.
Every live session follows the same loop, and every stage leaves you with something you keep.
-
01
Watch it built
Your instructor shares a terminal and builds the real thing, whether a pipeline, a cluster or a retrieval setup, explaining each decision.
Artifact: Session recording
-
02
Build it alongside
You build the same thing on your own machine in the same session, with the instructor watching screens in a batch of ten.
Artifact: Working lab
-
03
Debug what breaks
When something fails mid-session, you fix it together. It is often the most useful twenty minutes of the class.
Artifact: Debugging notes
-
04
Ship and get reviewed
Finish capstone projects, get your code reviewed, and prepare for interviews with resume help and mock interviews.
Artifact: Portfolio project
06 Meet the mentors
Taught by engineers who ship for a living.
Every batch is taught by one of two working engineers, with no rotating trainers. The person in your demo class is the person in your sessions.
TCS · 8+ years
DevOps & Cloud track
Firoz Ahmed
Senior AWS DevOps Engineer, TCS
A working DevOps engineer, not a full-time trainer. Firoz is hands-on with multi-region AWS infrastructure: more than 38 Java microservices on EKS, managed with Terraform, ArgoCD and Prometheus in production.
How he teaches
Every concept comes from a system that is actually running, with real failures, real cost pressure and real on-call. He personally reviews student code in the weekend batches.
- Docker, Kubernetes and Helm
- CI/CD with Jenkins and ArgoCD
- Terraform and AWS infrastructure
AI & Generative AI track
Sayeed Amaan
AI Lead at SophyXis Technologies, building intelligent systems with Python, TensorFlow and PyTorch. He teaches what matters in production: retrieval, evaluation, fine-tuning and deployment.
4+ years LinkedIn
10
Max per batch
Free demo class
Meet your instructor first
The demo class is taught by the same instructor who takes the paid batch, so you can judge the teaching before you pay.
07 From the blog
Notes from working engineers.
Practical guides on the tools and decisions behind DevOps, AI and data careers, written to be read with a terminal open.
Explore All Articles
Fig. — which to learn first
| Requirement | Docker | Kubernetes |
|---|---|---|
| Package an app with its dependencies | ||
| Run containers across many machines | ||
| Replace failed containers automatically | ||
| Run it on a laptop while you learn |
● strong fit · ◐ partial · ○ weak
Docker vs Kubernetes: Which Should You Learn First?
Confused between Docker and Kubernetes? This guide explains the differences and which one to learn first as a beginner.
Published January 2026
Fig. — pipeline stages
- 1 checkout
- 2 build
- 3 test
- 4 deploy
Jenkins CI/CD Pipeline Tutorial for Beginners
Create your first Jenkins pipeline, with examples, best practices and common troubleshooting tips.
Published January 2026
Fig. — core objects
- 1 deployment
- 2 replicaset
- 3 pods × 3
- 4 service
↩ reconcile until desired = actual
Kubernetes Complete Guide: From Basics to Production
Pods, deployments, services, Helm, monitoring, security and auto-scaling, with real-world examples.
Published January 2026
08 Questions
Straight answers before you start.
These answers match our published terms and refund policy. Something else on your mind?
Contact the academyNot for DevOps, which starts from fundamentals. For AI, basic programming helps but is not required, because Python is taught from the ground up. Data Engineering Level 1 is designed for people who have never written code.
Live. Every session is taught in real time, so you can interrupt, ask why something broke and get an answer in the moment. Sessions are recorded so you can revise or catch up, but the recordings support the class rather than replace it.
Most batches run at weekends so you can keep your job, and Data Engineering also offers weekday batches. Session times vary by program, and AI batches are also timed for evenings in the US and UK. The team confirms your batch timing before you enrol.
Yes. Every program includes hands-on labs and ends in capstone projects that you deploy and document on GitHub, so you have real work to walk an interviewer through.
You receive a certificate from ShiftToTech Academy when you successfully complete the program and its projects. Placement assistance, including resume building, LinkedIn optimisation, mock interviews and referrals to hiring partners, is included. It is support, not a guarantee: we cannot promise a job offer or a particular salary.
If the course is not right for you, you can ask for a full refund within 7 days of enrolling, provided you have not completed more than 20% of the course. If you cancel before your batch starts, you are refunded in full.
Read the full refund policyMove from thinking about tech to working in it.
Join live classes with working engineers, build projects you can show, and prepare for interviews with placement assistance.