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CDSA - Cross Domain Solution Architect profile image Vernard Sharbney
Vernard Sharbney for CDSA - Cross Domain Solution Architect

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The AI/ML Learning Path (Sprints-Based, Zero-Cost, Industry-Solid) Track

#ai #machinelearning #agriculture #aws

AI/ML Engineering Track

If you want to learn AI, ML, Deep Learning, Computer Vision, Deployment, APIs, fast, AND build a real project that actually matters… welcome home.

🔥 The AI/ML Learning Path (Sprints-Based, Zero-Cost, Industry-Solid)

Built to turn beginners into legit AI engineers with a portfolio that hits.

🧭 Sprint 1 — Get the Basics Locked (Python + Data Analysis + Git)

Goal: Understand Python, data, and version control.
Tools: Kaggle, CognitiveClass.ai, GitHub.

📚 Must-Do Resources

🧩 AgriScan Build Task

Analyze a sample soil dataset:

  • Calculate pH ranges
  • Classify soil types
  • Visualize nutrient distribution

Push your notebook to GitHub.

🔥 Sprint 2 — Machine Learning Core (Scikit-Learn Mastery)

Goal: Build prediction models confidently.
Tools: Scikit-Learn.

📚 Must-Do Resources

🧩 AgriScan Build Task

Build a basic Crop Recommendation Model:
Input: soil features (pH, potassium, nitrogen, moisture)
Output: crop suggestion

Train → Test → Evaluate → Push to GitHub.

🧠 Sprint 3 — Deep Learning & Computer Vision (The AgriScan Engine)

This is where we build the actual brain of AgriScan.

📚 Must-Do Resources

🌍 External Data Resources

🧩 AgriScan Build Task

Build a CNN model to classify crop diseases.
Start simple → build up to an AgriScan-level architecture.

Reference research:

(Don’t stress — we simplify it.)

🏗️ Sprint 4 — Deploy Models as APIs (FastAPI + Containers + Cloud)

Real AI engineers deploy, not just train models.

📚 Must-Do Resources

🧩 AgriScan Build Task

Deploy your ML model as a live API:

  • Create a /predict endpoint
  • Accept image uploads
  • Return JSON predictions
  • Containerize with Docker

  • Deploy to:

    • HuggingFace Spaces OR
    • AWS Free Tier OR
    • Railway.app

⚙️ Tools Every AgriScan Engineer Should Touch

These are non-negotiables:

💾 Notebooks

  • Google Colab
  • Jupyter Notebook
  • Kaggle Kernels

⚡ ML / DL Frameworks

  • Scikit-Learn
  • TensorFlow
  • PyTorch
  • Fast.ai

📊 Big Data & Distributed Processing

☁️ Cloud + Architecture Skills

🔚 What Skills You’ll Have at the End

Every member of the CDSA AI/ML Squad will be able to:

🧪 Technical

✔ Build ML models (Regression, Classification, Clustering)
✔ Build CNNs for image classification
✔ Work with PlantVillage + soil datasets
✔ Use Colab for GPU training
✔ Engineer features from raw agricultural data
✔ Train, validate, and tune DL models
✔ Build and deploy APIs using FastAPI
✔ Containerize apps with Docker
✔ Deploy models to cloud (HF Spaces / AWS / Railway)
✔ Version control using Git/GitHub
✔ Use Apache Spark for big-scale processing

🌍 AgriTech Domain

✔ Understand soil science basics
✔ Understand real crop disease patterns
✔ Build recommendation engines
✔ Work with agricultural datasets
✔ Adapt AI solutions for rural environments

💼 Career

✔ A real GitHub portfolio
✔ Multiple CognitiveClass badges
✔ Kaggle projects
✔ A deployed API you can demo
✔ Skills aligned with junior AI Engineer / ML Engineer roles

🚀 Final Notes

This isn’t a “take a course and chill” track.
This is a build-in-public, fast-learning, problem-solving squad.

Every sprint ends with:

  • A GitHub push
  • A demo
  • A micro-deliverable for AgriScan

If you're ready to build something that actually matters to farmers here in SA — jump in.

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