AI & ML Engineering
Learn to work with real data, establish baselines, train and evaluate appropriate models, then progress into deep learning, embeddings, retrieval and useful AI-enabled applications — with engineering judgment before complexity.
Focused Pathway
3-Month Program
₹8,999
Learning & Development
8 Weeks
Internship Experience
4 Weeks
Deeper Pathway
6-Month Program
₹14,999
Learning & Development
12 Weeks
Internship Experience
12 Weeks
What you'll learn
Use Python, NumPy and Pandas to prepare, inspect and reason about real data before choosing a model.
Frame problems, establish baselines, train classical models and evaluate them with metrics that match the problem.
Understand tensors, neural networks, training and validation through PyTorch without treating deep learning as the default solution.
Work with pretrained models, embeddings, semantic retrieval, RAG and model/LLM APIs after learning the underlying mechanics.
Analyse errors, limitations, hallucination and retrieval quality while considering privacy, bias, provenance and appropriate use.
Turn useful models or AI features into lightweight services with FastAPI, container fundamentals and reproducible engineering workflows.
AI & ML Engineering Stack
The stack is deliberately focused: data before models, classical ML before deep learning, and embeddings/retrieval before RAG abstractions.
Data Foundation
Machine Learning
Deep Learning
Modern AI
Engineering & Delivery
Learning progression
Python → Data Preparation → EDA & Statistics → Problem Framing → Classical ML → Baselines → Evaluation & Error Analysis → Feature Pipelines → Unsupervised Learning → Deep Learning Foundations → PyTorch → Pretrained Models → Embeddings → Vector Search → RAG → AI Application Evaluation → Responsible AI → FastAPI Serving → MLOps Fundamentals
A recurring rule throughout the track: establish a baseline before complexity.
Curriculum framework
The public framework shows the progression without exposing the internal week-by-week handbook.
Build Python fluency, work with arrays and DataFrames, clean data, explore distributions and visualise patterns.
Frame supervised and unsupervised problems, establish baselines, train models, use appropriate metrics, cross-validation, pipelines and error analysis.
Move into tensors, neural-network concepts, training and validation through PyTorch when the problem justifies added model complexity.
Use focused Hugging Face tooling, tokenization concepts and Sentence Transformers to understand pretrained inference and semantic representations.
Build from local vector-search concepts into retrieval, context construction, RAG and provider-neutral model/API integration.
Evaluate AI-system behavior, limitations and retrieval quality, then introduce FastAPI serving, deployment, reproducibility and responsible AI.
Choose your pathway
Both pathways build the same core capability. The longer pathway earns its difference through deeper experimentation, evaluation, engineering judgment and ownership.
Focused Pathway
₹8,999
8 Weeks Learning & Development
+ 4 Weeks Internship Experience
Develop practical capability to work with real data, train and evaluate appropriate models, and build and explain a useful AI-enabled application with structured guidance.
8 Weeks L&D → Readiness Checkpoint → 4 Weeks Internship → Final Evaluation
Applications not yet openDeeper Pathway
₹14,999
12 Weeks Learning & Development
+ 12 Weeks Internship Experience
Develop deeper AI/ML engineering capability through stronger experimentation, PyTorch work, retrieval/RAG evaluation, serving, reproducibility and greater independent ownership.
12 Weeks L&D → Readiness Checkpoint → 12 Weeks Internship → Final Engineering Review
Data → Experimentation → Evaluation → Engineering → Ownership → Professional Experience
Final Engineering Review
Present and explain data choices, preprocessing, baselines, model selection, metrics, errors, deep-learning or retrieval decisions, responsible-AI considerations, serving/deployment approach, limitations and technical trade-offs.
Before applying, review the Refund & Cancellation Policy.
Projects you'll build
Project briefs can vary, but every final project should connect data, modeling or retrieval, evaluation, application behavior and explanation.
Prepare real data, establish a baseline, compare appropriate classical models and explain the metric and error profile.
Transform, analyse and model structured data while documenting assumptions, limitations and reproducible preparation steps.
Use embeddings and local vector search to retrieve meaningfully related content and evaluate retrieval quality.
Build a small retrieval-augmented application with explicit source context, failure analysis and evaluation thinking.
Expose a model or AI feature through a lightweight API with validation, configuration and responsible handling.
A scoped project the participant can demonstrate, evaluate, document and defend during readiness or final review.
Evaluation & Responsible AI
Participants learn to question data quality, metrics, errors, retrieval quality, hallucination, model limitations and whether added complexity actually improves the system.
Start with a simple, measurable reference point before adding more complex models or pipelines.
Use metrics, cross-validation, error analysis and task-specific checks instead of judging systems by impressive-looking output.
Consider privacy, provenance, bias, security, limitations and appropriate human oversight.
Be able to explain the data, model, retrieval, evaluation and deployment decisions behind submitted work.
Professional workflows
Use source control from early work through branches, pull requests, review feedback and collaborative delivery.
Track assumptions, datasets, parameters, metrics and artifacts so results can be reproduced and compared.
Package useful model or AI behavior behind FastAPI endpoints and understand lightweight container deployment.
Document data assumptions, model limitations, evaluation results, API behavior, deployment notes and technical decisions.
Internship Experience
Participants who meet readiness requirements progress into suitable supervised work involving data, experiments, evaluation, model/application integration, testing, documentation or appropriate AI-enabled engineering tasks.
Understand the problem, data context, repository, evaluation expectations, privacy/security requirements and documentation standards.
Complete defined data, experimentation, evaluation, integration, testing or documentation work with review and feedback.
Work through requirements, branches, reviews, experiment results, demonstrations and quality checks.
Take responsibility for larger scoped work as technical judgment, reliability and problem-solving ability develop.
Complete, evaluate, document and explain the assigned deliverable before final review.
What you'll leave with
Career Development
Strengthen how you present AI/ML work through resume review, LinkedIn guidance, GitHub/portfolio guidance, project presentation, technical interview preparation, mock interviews and feedback. Relevant opportunities may be shared where appropriate.
Eligibility & expectations
The track can support beginners and developing learners, subject to cohort entry expectations. What matters is willingness to work carefully with data, think critically about results, build, debug, accept feedback and take responsibility for progress.
Applicants must be aged 16–25 at submission. Ages 16–17 require guardian consent before enrollment.
AI & ML Engineering FAQ
The AI & ML Engineering track uses a focused foundation-first stack and the same readiness-to-internship framework as the wider Industry Training Program.
The track is designed to build from Python, data and machine-learning foundations. Entry expectations may vary by cohort, but the focus is on developing practical capability progressively rather than assuming prior AI specialization.
No. The track starts with Python, data quality, statistics and machine-learning foundations, then progresses into deep-learning and modern AI application concepts. LLM APIs, embeddings and RAG are introduced after participants understand data, evaluation, model limitations and the need to establish a baseline before complexity.
PyTorch is the single focused deep-learning framework in Stack v1 so participants can understand tensors, datasets, training, validation and neural-network workflows without splitting time across multiple competing frameworks.
Both pathways use the same core foundation. The 3-Month Program develops practical capability to work with data, train and evaluate appropriate models, and build and explain a useful AI-enabled application. The 6-Month Program goes deeper into experimentation, PyTorch, pretrained models, retrieval/RAG evaluation, deployment, MLflow/reproducibility, monitoring concepts, technical trade-offs and independent ownership.
Yes. Participants first learn embeddings and local vector-search concepts before using them in retrieval-augmented generation. Deeper retrieval quality, persistent vector infrastructure and evaluation belong primarily to the longer pathway where appropriate.
Mathematics and statistics are taught in context: distributions, sampling, vectors, matrices, gradients, losses and optimization are introduced where they help explain model behavior. The track does not turn into a separate proof-heavy mathematics bootcamp.
Participants learn lightweight serving and deployment concepts using FastAPI and container fundamentals. The longer pathway goes deeper into experiment tracking, reproducibility, versioning, monitoring and lifecycle concepts without turning the program into a full infrastructure or Kubernetes specialization.
The 3-Month AI & ML Engineering program fee is ₹8,999 and the 6-Month program fee is ₹14,999. Internship Experience is part of the applicable program structure and is not charged separately.
No. Applicable HodoLabs credentials are earned by successfully completing the corresponding program requirements and are not sold separately.
AI & ML Engineering
Explore AI & ML Engineering training at HodoLabs. Applications are not yet open.
