Train Models. Ship Solutions.AI & Machine Learning

Join Spypro's hands-on AI/ML internship. Build real pipelines, train production models, and deploy intelligent systems that solve genuine business problems ? guided by working ML engineers.

Apply Now Explore Curriculum

Program Overview

Real Data. Real Models.
Real Deployment.

This isn't a Jupyter notebook course. From day one you'll be working on real datasets, building end-to-end ML pipelines, and deploying models to production environments alongside experienced ML engineers and data scientists.

We built this program around what employers actually need: clean feature engineering, rigorous model evaluation, MLOps practices, and the ability to ship ML solutions that work reliably in the real world.

3-5 months

Remote & hybrid

Certificate

Part-time ok

Daily experiments

train_model.py spypro-ml

            import mlflow, optuna
            from sklearn.ensemble import GradientBoostingClassifier
            from sklearn.metrics import roc_auc_score
            

            def objective(trial):
              params = {
                "n_estimators": trial.suggest_int("n_estimators", 100, 500),
                "learning_rate": trial.suggest_float("lr", 1e-3, 0.3)
              }
              with mlflow.start_run():
                model = GradientBoostingClassifier(**params).fit(X_train, y_train)
                auc = roc_auc_score(y_val, model.predict_proba(X_val)[:,1])
                mlflow.log_metric("auc", auc)
                return auc
            

            Trial 42  AUC: 0.9314    best so far
             Model registered in MLflow registry
             Deployed to FastAPI endpoint :8000
            

            $ python evaluate.py 
          

Download Curriculum

Choose your preferred internship duration and download the detailed curriculum to plan your learning journey.

What You'll Learn

Six Core Skill Domains

A curriculum shaped by practicing ML engineers and data scientists from product companies and research labs.

🗄️

Data Preparation & Feature Engineering

Clean, transform, and enrich raw datasets. Build reproducible pipelines for imputation, encoding, scaling, and constructing meaningful features from real-world data.

PandasNumPyScikit-learn

📊

Supervised & Unsupervised Learning

Train classification, regression, and clustering models. Understand bias-variance tradeoffs, apply ensemble methods, and select the right algorithm for the problem.

XGBoostRandom ForestK-Means

🎯

Model Evaluation & Tuning

Evaluate models rigorously with cross-validation, ROC/AUC, precision-recall, and confusion matrices. Tune hyperparameters with Optuna and grid search strategies.

OptunaCross-valSHAP

🧠

Deep Learning & Neural Networks

Build CNNs for computer vision and RNNs/transformers for NLP. Train on real datasets using PyTorch, manage GPU resources, and interpret model outputs.

PyTorchHuggingFaceCUDA

⚙️

MLOps & Deployment

Package models as REST APIs with FastAPI, track experiments in MLflow, containerise with Docker, and deploy to cloud platforms with automated retraining pipelines.

MLflowFastAPIDocker

📈

Insight Communication

Translate complex model results into business-ready reports. Build interactive dashboards, visualise feature importance, and communicate findings to non-technical stakeholders.

MatplotlibPlotlyStreamlit

Program Timeline

Your Journey, Month by Month

A structured ramp from ML fundamentals to deploying production-ready intelligent systems.

MONTH 1

Foundations & Data Engineering

Python for ML, statistics refresher, data wrangling with Pandas, and exploratory data analysis on real datasets. Build your first end-to-end preprocessing pipeline and baseline models. Mentorship kick-off with your assigned ML engineer.

MONTH 2

Classical ML & Model Evaluation

Train and evaluate supervised models on structured data problems. Master cross-validation, hyperparameter tuning with Optuna, feature selection, and model explainability with SHAP values.

MONTH 3

Deep Learning & Specialisation

Build neural networks with PyTorch for computer vision or NLP tasks. Fine-tune pre-trained transformer models from HuggingFace on domain-specific datasets. Track all experiments with MLflow.

MONTHS 4?5

MLOps, Deployment & Capstone

Package your best model as a FastAPI service, containerise with Docker, and deploy to AWS with an automated retraining pipeline. Build a Streamlit dashboard for live inference and present your capstone project.

GRADUATION

Demo Day & Certification

Present your deployed ML system to industry guests, walk through your modelling decisions and production architecture, and receive your verified certificate, LinkedIn endorsement, and referrals to hiring partners.

Tech Stack

Tools You'll Master

Python 3.12

NumPy / Pandas

Scikit-learn

XGBoost / LightGBM

PyTorch

HuggingFace

Optuna

MLflow

FastAPI

Docker

AWS SageMaker

Streamlit

Plotly / Matplotlib

SHAP / LIME

Eligibility

Who Should Apply?

We value mathematical curiosity and coding consistency over existing ML credentials.

Ideal Candidates

CS, IT, maths, or statistics students (bachelor/master)
Python basics - loops, functions, NumPy arrays
Foundational statistics - mean, variance, probability
Basic linear algebra - vectors, matrices, dot products
Completed at least one ML course or Kaggle competition
Genuinely curious about how models learn and generalise

Common Barriers (We Help With)

No prior internship or industry experience required
No deep learning or PyTorch knowledge needed upfront
No certifications mandatory to apply
Non-CS backgrounds (physics, economics) welcome
Part-time track available for working students

Application

Start Your Application

Application Submitted!

Thank you! We've sent a confirmation to your inbox.
Our team will reach out within 2?3 business days.

Full Name

Email Address

Phone Number

Current Qualification

Preferred Track

Select Internship Domain

Start Availability

Why are you right for this internship?

Your information is encrypted and never shared with third parties.

FAQ

Common Questions

Is this internship paid?

Stipends for outstanding performers from month 2. All interns receive a verified certificate, LinkedIn endorsement, and placement support at AI-first companies and research teams.

Can I do this while studying full-time?

Yes ? our part-time track requires around 20 hrs/week and is structured around academic schedules with flexible experiment lab windows and recorded sessions.

What equipment do I need?

A modern laptop (8 GB+ RAM) and stable internet. GPU-intensive training runs on cloud compute we provide ? no expensive local hardware required.

How competitive is selection?

We accept roughly 20% of applicants per cohort, prioritising Python ability, mathematical aptitude, and curiosity about how models work over prior ML job experience.

Will I work on real data and problems?

Yes ? interns work on real datasets from Spypro client projects and internal research problems, with models that get evaluated against real-world performance benchmarks.

What career paths does this open?

ML engineer, data scientist, AI researcher, MLOps engineer, and NLP/CV specialist roles at AI startups, research labs, and enterprise analytics teams.