π» Developer AI Learning Path
Fast-track from software developer to ML engineer in 4 months with production-ready skills.
π Overview
This accelerated learning path is designed for developers who already know how to code and want to quickly transition into AI and machine learning engineering. You'll learn both the theory and practical implementation, with a strong focus on production deployment and MLOps.
What You'll Learn
- ML fundamentals with scikit-learn and real-world datasets
- Deep learning with TensorFlow and PyTorch
- NLP, transformers, and working with LLMs
- Computer vision with CNNs and modern architectures
- MLOps: Docker, Kubernetes, CI/CD, monitoring
- Building and deploying production ML systems
Prerequisites
- Programming: Comfortable with at least one programming language (Python preferred)
- Git & Terminal: Familiar with version control and command line
- Basic Math: Helpful but not required (we'll cover what you need)
Time Commitment
4 months at 15-20 hours per week. Each month includes lectures, coding exercises, and a capstone project.
Tech Stack
Python
NumPy
Pandas
scikit-learn
Jupyter
Matplotlib
Learning Objectives
- Master supervised learning (regression, classification)
- Understand unsupervised learning (clustering, dimensionality reduction)
- Feature engineering and selection techniques
- Model evaluation, validation, and hyperparameter tuning
- Handling imbalanced data and missing values
- Ensemble methods (Random Forest, XGBoost, LightGBM)
π‘ Pro Tip: Don't skip feature engineering! In production, 80% of ML work is data preparation and feature engineering. Master this and you'll be invaluable to any team.
π― Month 1 Capstone Project
End-to-End ML Pipeline: Build a complete ML project from scratch:
- Problem: Predict customer churn for a telecom company (use Kaggle dataset)
- EDA: Comprehensive exploratory data analysis with visualizations
- Feature Engineering: Create new features, handle categorical variables, scale numerical features
- Modeling: Train and compare multiple models (Logistic Regression, Random Forest, XGBoost)
- Evaluation: Use appropriate metrics (precision, recall, F1, ROC-AUC)
- Interpretation: Feature importance analysis and model explanation
- Documentation: Create a professional README and Jupyter notebook
Deliverable: GitHub repository with complete project + Medium article explaining your approach
β
Month 1 Checkpoint: You should be able to take any tabular dataset, perform feature engineering, train multiple models, and deploy them with confidence.
Tech Stack
TensorFlow
PyTorch
Keras
CUDA/GPU
Weights & Biases
Learning Objectives
- Understand neural network architecture and training
- Master CNNs for computer vision tasks
- Build RNNs and LSTMs for sequence data
- Learn transfer learning and fine-tuning
- Understand attention mechanisms and transformers
- Experiment tracking and model versioning
π‘ Pro Tip: Learn both TensorFlow and PyTorch. TensorFlow dominates production, but PyTorch is preferred for research. Being bilingual makes you more versatile.
π― Month 2 Capstone Project
Computer Vision Application: Build a production-ready image classification system:
- Problem: Medical image classification (chest X-ray or skin lesion detection)
- Data: Use a public medical imaging dataset from Kaggle or NIH
- Model: Use transfer learning with ResNet50, EfficientNet, or Vision Transformer
- Training: Implement data augmentation, learning rate scheduling, early stopping
- Evaluation: Generate confusion matrix, ROC curves, and class activation maps
- Deployment: Build a FastAPI or Flask web service
- Frontend: Create a simple Streamlit or Gradio interface
Deliverable: Dockerized application deployable to cloud with API documentation
β
Month 2 Checkpoint: You should be comfortable building, training, and debugging neural networks for image and sequence tasks using TensorFlow or PyTorch.
Tech Stack
Transformers
Hugging Face
LangChain
OpenAI API
Vector DBs
RAG
Learning Objectives
- Understand transformer architecture in depth
- Fine-tune pre-trained models (BERT, GPT, T5)
- Master prompt engineering for LLMs
- Build RAG (Retrieval-Augmented Generation) systems
- Implement embeddings and semantic search
- Deploy LLM applications at scale
π‘ Pro Tip: Focus on RAG and fine-tuning rather than training models from scratch. In production, you'll almost always use pre-trained models and adapt them to your domain.
π― Month 3 Capstone Project
Enterprise RAG System: Build an intelligent document Q&A system:
- Problem: Create a chatbot that can answer questions from a knowledge base (company docs, research papers, etc.)
- Data Processing: Parse PDFs, split into chunks, create embeddings
- Vector Store: Set up Pinecone, Weaviate, or Chroma for semantic search
- RAG Pipeline: Implement retrieval + generation with GPT-4 or Claude
- Evaluation: Create test questions and measure answer quality
- UI: Build a chat interface with Streamlit or React
- Optimization: Implement caching, rate limiting, cost tracking
Bonus: Fine-tune a smaller model (Llama 2 7B) for your specific domain
Deliverable: Production-ready RAG application with API and frontend
β
Month 3 Checkpoint: You should be able to fine-tune transformers, build RAG systems, and integrate LLM APIs into production applications.
Tech Stack
Docker
Kubernetes
MLflow
GitHub Actions
AWS/GCP
FastAPI
Learning Objectives
- Containerize ML applications with Docker
- Orchestrate deployments with Kubernetes
- Set up CI/CD pipelines for ML projects
- Implement model monitoring and drift detection
- Version control for data and models
- A/B testing and progressive rollouts
π‘ Pro Tip: Start simple β don't over-engineer. Use managed services (AWS SageMaker, GCP Vertex AI) when possible. Kubernetes is powerful but complex; only use it when you truly need it.
π― Month 4 Capstone Project
Production ML System: Build a complete end-to-end ML platform:
- Choose a model: Pick one from your previous projects (Month 1, 2, or 3)
- Containerization: Create optimized Docker images for training and serving
- API Development: Build RESTful API with FastAPI including proper error handling
- CI/CD: Set up GitHub Actions for automated testing and deployment
- Cloud Deployment: Deploy to AWS/GCP with load balancing
- Monitoring: Implement logging, metrics, and alerting (Prometheus + Grafana)
- Model Registry: Use MLflow for experiment tracking and model versioning
- Documentation: API docs (Swagger), architecture diagrams, deployment guide
Deliverable: Live production system with monitoring dashboard and complete documentation
β
Month 4 Checkpoint: You should be able to deploy ML models to production with proper monitoring, versioning, and CI/CD pipelines.
π Building Your ML Engineer Portfolio
After completing this 4-month path, you'll have 4 substantial projects demonstrating end-to-end ML skills. Here's how to maximize their impact:
Portfolio Checklist
- All projects on GitHub with professional READMEs
- Live demos deployed to cloud (Heroku, AWS, GCP)
- Write technical blog posts explaining your approach (Medium, Dev.to)
- Create video walkthroughs for your top 2 projects (YouTube)
- Contribute to open-source ML projects (Hugging Face, scikit-learn)
- Build a personal website showcasing your work
What's Next?
- LLM & Generative AI Path β Specialize in building LLM applications
- Agentic AI Path β Build autonomous AI systems
- Job Search: Apply for ML Engineer, Data Scientist, AI Engineer roles
- Freelancing: Start taking ML consulting projects on Upwork/Toptal
- Kaggle: Compete in competitions to validate your skills
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