Evidence · Training labs
Training projects
These entries describe academy training projects tied to published course curricula and learning outcomes. They are not confidential client case studies and do not invent live demos or GitHub links.
Related coursesFull-stack app with JWT authentication
Learners need a deployable MERN application that proves they can design REST APIs, secure routes with JWT, and ship a React UI that talks to MongoDB-backed services.
Course: MERN Stack Development
Project detailsReact dashboard with state management
Hiring panels often ask for a UI that manages lists, forms, and shared state cleanly—without a chaotic component tree.
Course: MERN Stack Development
Project detailsDocumented REST API with validation
Backend interviews expect clear resource modeling, middleware, error handling, and readable API docs—not only CRUD happy paths.
Course: MERN Stack Development
Project detailsCI/CD pipeline with automated tests
Teams expect engineers who can wire build → test → deploy so releases are repeatable, not manual copy-paste to servers.
Course: DevOps Engineering
Project detailsContainerized app on Kubernetes
Learners must show they can package an app and run it under orchestration with services, configs, and basic networking.
Course: DevOps Engineering
Project detailsAWS infrastructure with Terraform
Cloud roles require Infrastructure as Code and awareness of core AWS services, IAM, and cost/ops trade-offs.
Course: DevOps Engineering
Project detailsDjango web application with PostgreSQL
Python full-stack roles expect a maintainable Django app with models, views, auth, and a relational schema—not only scripts.
Course: Python Django Development
Project detailsFlask microservice API
Many teams split smaller services; learners practice Flask blueprints, SQLAlchemy, and clear HTTP boundaries.
Course: Python Django Development
Project detailsRAG assistant over document corpus
Product teams want assistants grounded in private docs, not unconstrained LLM answers—retrieval quality matters.
Course: Generative AI
Project detailsLangChain workflow with memory and tools
Interview demos need more than a single prompt: chains, memory, and tool calls that solve a multi-step task.
Course: Generative AI
Project detailsMulti-agent system for a complex task
Autonomous workflows need planning, role split across agents, and clear hand-offs—not a single unbounded loop.
Course: Agentic AI
Project detailsTool-using agent with planning
Agents must call external tools safely and recover when steps fail—core interview and portfolio themes for this track.
Course: Agentic AI
Project detailsSEO and paid-campaign training plan
Marketers need a coherent plan tying keyword research, on-page SEO, and measurable Ads/Analytics tracking—not isolated tips.
Course: Digital Marketing
Project detailsInterview and presentation practice portfolio
Technical skill alone is not enough; learners need evidence of clear presentations, written communication, and interview storytelling.
Course: Communication Skills
Project detailsReal-time API with WebSockets
Backend roles often require event-driven features (chat, live status) alongside solid REST design.
Course: Node.js Developer
Project detailsMicroservices-style Node backend
Learners practice splitting services and defining clear API contracts rather than one monolithic server file.
Course: Node.js Developer
Project detailsClassical ML baseline notebook
Before deep learning, learners must show data prep, classical algorithms, and honest metrics on a defined dataset.
Course: AI Foundation
Project detailsDeep learning classification lab
AI foundation outcomes include implementing and explaining a neural model—not only calling a hosted API.
Course: AI Foundation
Project detailsBI dashboard from cleaned dataset
Analysts must turn messy tabular data into decision-ready visuals with clear KPIs.
Course: Data Analytics
Project detailsSQL + Python exploratory analysis
Interview cases often mix SQL extraction with Python analysis and a short business summary.
Course: Data Analytics
Project detailsParameter-efficient LLM fine-tuning lab
Learners practice dataset prep and LoRA/PEFT-style fine-tuning rather than only prompting a frozen model.
Course: LLM Fine-Tuning
Project detailsEnd-to-end RAG pipeline
RAG courses culminate in a working retrieve-then-generate path with chunking and vector search choices made explicit.
Course: RAG System Development
Project detailsMultimodal vision / generation lab
Multimodal work requires demonstrating text↔image (and related) pipelines with clear limits and evaluation notes.
Course: Multimodal AI
Project detailsModel serving with FastAPI and Docker
Deploying models means packaging inference behind an API with a repeatable container and CI story.
Course: AI Model Deployment
Project detailsQiskit foundational algorithm lab
Quantum literacy requires implementing small algorithms with resource notes—not slide-only theory.
Course: Quantum Computing
Project detailsNoise and mitigation experiment
NISQ reality means documenting noise, mitigation plots, and when classical baselines win—honest technical storytelling.
Course: Quantum Computing
Project details