Free AI Stack for Student Projects: Build, Deploy

When you’re a student, the biggest hurdle in turning a cool AI idea into a working project is often the stack itself. You want tools that are powerful yet free, easy to set up, and can scale from a local notebook to a cloud‑hosted demo. In this guide we’ll walk through a complete, cost‑free AI stack that covers data collection, model training, API serving, and deployment—all using services and libraries that students can access right now.

1. Data Collection & Preparation

Before any model can learn, you need clean, well‑structured data. For most student projects, public datasets and lightweight annotation tools are sufficient.

1.1 Public Datasets

• Kaggle – Thousands of ready‑to‑download CSVs, images, and text corpora.
• UCI Machine Learning Repository – Classic tabular datasets for classification and regression.
• Hugging Face Datasets – A unified API to pull text, audio, and vision datasets directly into Python.

1.2 Quick Annotation with Label Studio

Label Studio is an open‑source annotation tool you can run locally with a single Docker command. It supports image bounding boxes, text classification, and audio segmentation—perfect for small‑team projects.

# Start Label Studio (requires Docker)
!docker run -d -p 8080:8080 \
    -e LABEL_STUDIO_HOST=0.0.0.0 \
    heartexlabs/label-studio:latest

Once the UI is up at http://localhost:8080, you can import a CSV, label a few hundred samples, and export the results in JSON or COCO format.

2. Model Development

Python remains the lingua franca for AI research, and the ecosystem offers both high‑level abstractions and low‑level control. Below we’ll cover a lightweight deep‑learning stack that runs on free compute resources.

2.1 Framework Choice: PyTorch vs. TensorFlow

• PyTorch – Intuitive, dynamic graphs; excellent for rapid prototyping.
• TensorFlow/Keras – Mature deployment tools (TensorFlow Lite, TensorFlow.js).

For most student projects, PyTorch paired with torchvision or transformers offers the fastest path from idea to result.

2.2 Example: Image Classification with a Pre‑trained ResNet

Instead of training a model from scratch, we’ll fine‑tune a ResNet‑18 on a custom dataset of handwritten digits. This approach reduces training time to minutes on a free GPU instance (e.g., Google Colab).

import torch, torchvision, torch.nn as nn, torch.optim as optim
from torchvision import datasets, transforms, models

# 1️⃣ Transformations – resize, normalize, and convert to tensor
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 2️⃣ Load custom dataset (assume folder structure: data/train/0, data/train/1, ...)
train_dataset = datasets.ImageFolder(root='data/train', transform=transform)
train_loader   = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)

# 3️⃣ Load pre‑trained ResNet‑18 and replace final layer
model = models.resnet18(pretrained=True)
model.fc = nn.Linear(model.fc.in_features, len(train_dataset.classes))
model = model.to('cuda' if torch.cuda.is_available() else 'cpu')

# 4️⃣ Training loop
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=1e-4)

for epoch in range(5):
    model.train()
    running_loss = 0.0
    for imgs, labels in train_loader:
        imgs, labels = imgs.to('cuda'), labels.to('cuda')
        optimizer.zero_grad()
        outputs = model(imgs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch+1}, Loss: {running_loss/len(train_loader):.4f}')

After just a few epochs, the model typically reaches >95% accuracy on a simple digit set, demonstrating the power of transfer learning.

3. Model Serialization & Versioning

Once you have a trained model, you need a reproducible way to store and share it. Two tools dominate the free landscape: ONNX for format‑agnostic serialization and DVC for data/model version control.

3.1 Export to ONNX

ONNX files can be loaded by many runtimes (e.g., TensorFlow Serving, ONNX Runtime, or even JavaScript via ONNX.js). This makes deployment platform‑agnostic.

dummy_input = torch.randn(1, 3, 224, 224).to('cpu')
torch.onnx.export(model, dummy_input, 'resnet_digit.onnx',
                  input_names=['image'], output_names=['logits'],
                  dynamic_axes={'image': {0: 'batch_size'}})

3.2 Tracking with DVC

DVC turns a regular Git repository into a data‑aware workflow. You can push model files to a free remote storage like Google Drive or GitHub LFS.

# Initialize DVC
git init
dvc init

# Add the ONNX model
dvc add resnet_digit.onnx

# Commit metadata
git add resnet_digit.onnx.dvc .gitignore
git commit -m "Add serialized model"

# Configure remote (Google Drive example)
dvc remote add -d myremote gdrive://
dvc push

Pro tip: Keep a requirements.txt alongside your DVC files. When you clone the repo later, a single pip install -r requirements.txt && dvc pull restores the entire environment.

4. Building an API with FastAPI

FastAPI has become the go‑to framework for serving ML models because of its async nature, automatic OpenAPI docs, and minimal boilerplate. Below we’ll wrap the ONNX model in a REST endpoint that accepts base64‑encoded images.

4.1 Project Structure

my_ai_project/
│
├─ app/
│   ├─ main.py
│   └─ model.py
├─ models/
│   └─ resnet_digit.onnx
├─ requirements.txt
└─ dvc.yaml   # optional for CI pipelines

4.2 model.py – Loading ONNX Runtime

import onnxruntime as ort
import numpy as np
from PIL import Image
import io, base64

# Load once at import time
session = ort.InferenceSession('models/resnet_digit.onnx')
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name

def preprocess(image_bytes: bytes) -> np.ndarray:
    img = Image.open(io.BytesIO(image_bytes)).convert('RGB')
    img = img.resize((224, 224))
    arr = np.array(img).astype('float32') / 255.0
    # Normalize using ImageNet stats
    mean = np.array([0.485, 0.456, 0.406])
    std  = np.array([0.229, 0.224, 0.225])
    arr = (arr - mean) / std
    # Change to NCHW
    return arr.transpose(2, 0, 1)[None, ...]

def predict(image_b64: str) -> int:
    image_bytes = base64.b64decode(image_b64)
    tensor = preprocess(image_bytes)
    logits = session.run([output_name], {input_name: tensor})[0]
    return int(np.argmax(logits, axis=1)[0])

4.3 main.py – FastAPI Endpoints

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from .model import predict

app = FastAPI(title="Student AI Demo API")

class ImagePayload(BaseModel):
    image_base64: str

@app.post("/predict")
def get_prediction(payload: ImagePayload):
    try:
        label = predict(payload.image_base64)
        return {"predicted_label": label}
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))

@app.get("/health")
def health_check():
    return {"status": "alive"}

Run the service locally with uvicorn app.main:app --reload. FastAPI automatically generates Swagger UI at http://127.0.0.1:8000/docs, letting you test the endpoint without writing a separate client.

Pro tip: Use uvicorn --workers 4 when you move to production. It spawns multiple processes, leveraging multi‑core CPUs on free tier VMs.

5. Free Cloud Deployment Options

Now that the API works locally, let’s push it to a free cloud environment so classmates, mentors, or potential employers can interact with it.

5.1 Render (Free Tier)

• Supports Docker, Python, and static sites.
• Automatic HTTPS, zero‑config DNS.
• Free tier includes 750 hours/month – enough for a continuously running demo.

Steps:

1. Write a Dockerfile that copies the app and installs dependencies.
2. Connect the GitHub repo to Render and select “Web Service”.
3. Set the start command to uvicorn app.main:app --host 0.0.0.0 --port $PORT.

5.2 Google Cloud Run (Free 2 M requests/month)

Cloud Run runs containers on a fully managed serverless platform. The free tier is generous for student demos.

# Build container
docker build -t gcr.io/$PROJECT_ID/ai-demo .

# Push to Artifact Registry
docker push gcr.io/$PROJECT_ID/ai-demo

# Deploy
gcloud run deploy ai-demo \
  --image gcr.io/$PROJECT_ID/ai-demo \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

5.3 Hugging Face Spaces (Gradio UI)

If you prefer a quick UI rather than a raw API, Hugging Face Spaces lets you host a Gradio interface for free. The same ONNX model can be loaded inside a Gradio demo with just a few lines of code.

import gradio as gr
from app.model import predict

def infer(image):
    # Convert PIL image to base64
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    b64 = base64.b64encode(buffered.getvalue()).decode()
    return predict(b64)

iface = gr.Interface(fn=infer,
                     inputs=gr.Image(type="pil"),
                     outputs="label",
                     title="Digit Classifier")
iface.launch()

Push the app/ folder and requirements.txt to a new Hugging Face Space, select “Gradio”, and the platform builds and serves it automatically.

Pro tip: Turn on “Automatic Scaling” in Cloud Run or Render. The service will spin down to zero when idle, keeping you safely within the free quota.

6. Monitoring & Logging

Even a simple demo benefits from basic observability. Two free tools integrate nicely with the stack above: Prometheus + Grafana for metrics, and Logtail (or GitHub Actions) for log aggregation.

6.1 Adding Prometheus Metrics

from prometheus_client import Counter, generate_latest, CONTENT_TYPE_LATEST
from fastapi.responses import Response

REQUEST_COUNT = Counter('api_requests_total', 'Total API requests', ['endpoint'])

@app.post("/predict")
def get_prediction(payload: ImagePayload):
    REQUEST_COUNT.labels(endpoint="/predict").inc()
    # ... existing logic ...
    return {"predicted_label": label}

@app.get("/metrics")
def metrics():
    return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST)

Deploy a side‑car Prometheus server on Render or Cloud Run, and point Grafana to it for real‑time dashboards.

6.2 Centralized Logging with Logtail

Create a free Logtail account, grab the ingestion token, and add a simple logger to your FastAPI app.

import logging, os
from logtail import LogtailHandler

logger = logging.getLogger("uvicorn.error")
logger.setLevel(logging.INFO)

handler = LogtailHandler(source_token=os.getenv("LOGTAIL_TOKEN"))
logger.addHandler(handler)

@app.post("/predict")
def get_prediction(payload: ImagePayload):
    logger.info("Prediction request received")
    # ... rest of code ...

7. Real‑World Use Cases for Student Projects

Now that the stack is assembled, let’s explore three concrete project ideas that showcase its versatility.

7.1 Campus Navigation Assistant

• Problem: New students struggle to find lecture halls.
• Solution: Train a lightweight object detection model (e.g., YOLO‑v5) on campus signage, expose an endpoint that returns the nearest building name, and embed the API in a mobile app.
• Stack usage: Data from campus photo tours → YOLO fine‑tuning (PyTorch) → ONNX export → FastAPI → Deploy on Render.

7.2 Automated Essay Grader

• Problem: Grading large batches of short answers is time‑consuming.
• Solution: Use a transformer model (DistilBERT) fine‑tuned on a small rubric dataset. Serve the model via an API that returns a score and feedback.
• Stack usage: Hugging Face datasets → Transformers fine‑tune → ONNX → FastAPI → Deploy on Cloud Run.

7.3 Real‑Time Sign Language Translator

• Problem: Accessibility for deaf students in virtual classrooms.
• Solution: Capture webcam frames, run a pose‑estimation model (MediaPipe) followed by a sequence classifier (LSTM) to translate gestures into text.
• Stack usage: MediaPipe for keypoint extraction → LSTM (PyTorch) → ONNX → Gradio UI on Hugging Face Spaces.

Each example reuses the same core components, proving that a single free stack can power a variety of domains—from computer vision to natural language processing.

Conclusion

Building an AI project doesn’t have to mean wrestling with expensive cloud credits or proprietary tools. By leveraging open‑source libraries (PyTorch, FastAPI, ONNX), free version‑control extensions (DVC), and generous cloud tiers (Render, Cloud Run, Hugging Face), students can prototype, ship, and monitor production‑grade models without spending a dime. The key is to keep the workflow modular: collect data, fine‑tune a pre‑trained model, serialize with ONNX, version with DVC, wrap in a FastAPI service, and deploy to a serverless platform. Follow the patterns in this guide, sprinkle in the pro tips, and you’ll have a reusable AI stack that scales from a single notebook to a public demo—ready to impress professors, recruiters, and fellow developers alike.