AI/ML Engineer - Expert Guide

Build intelligent, production-grade AI systems with machine learning excellence.

🎯 Expert Coverage

Machine Learning Foundations

• Supervised learning (classification, regression, ranking)
• Unsupervised learning (clustering, dimension reduction)
• Reinforcement learning and policy networks
• Transfer learning and few-shot learning
• Ensemble methods and stacking
• Anomaly detection and outlier handling

Deep Learning Architecture

• Neural network design and initialization
• Convolutional Neural Networks (CNNs) for vision
• Recurrent Networks (RNNs, LSTMs, GRUs) for sequences
• Transformer architecture deep dive
• Attention mechanisms and self-attention
• Generative models (GANs, VAEs, Diffusion)

Large Language Models (LLMs)

• Transformer-based models (GPT, BERT, T5)
• Fine-tuning strategies (LoRA, QLoRA, adapter)
• Prompt engineering best practices
• In-context learning and few-shot prompting
• Retrieval-Augmented Generation (RAG)
• Vector embeddings and semantic search
• LLM evaluation and benchmarking

Computer Vision

• Image classification architectures
• Object detection (YOLO, Faster R-CNN, EfficientDet)
• Image segmentation (semantic, instance, panoptic)
• Pose estimation and landmark detection
• 3D vision and depth estimation
• Video understanding and action recognition

Natural Language Processing

• Text preprocessing and tokenization
• Word embeddings (Word2Vec, GloVe, FastText)
• Sequence labeling (NER, POS tagging)
• Text classification and sentiment analysis
• Machine translation and sequence-to-sequence
• Question answering and reading comprehension
• Summarization and paraphrasing

Data Processing Pipeline

• Data collection and labeling strategies
• Data cleaning and validation
• Feature engineering and selection
• Handling imbalanced datasets
• Data augmentation techniques
• Version control for data (DVC, Pachyderm)

Model Development

• Experiment tracking (MLflow, Weights & Biases)
• Hyperparameter optimization (Optuna, Ray Tune)
• Cross-validation and evaluation strategies
• Preventing overfitting (regularization, early stopping)
• Model interpretability (SHAP, LIME, attention viz)
• Bias detection and fairness metrics

MLOps & Deployment

• Model serving (FastAPI, BentoML, TensorFlow Serving)
• Containerization with Docker
• Kubernetes orchestration
• Model monitoring and drift detection
• Retraining pipelines and automation
• A/B testing and canary deployments
• Feature stores and feature management

AI Safety & Ethics

• Responsible AI principles
• Fairness, accountability, transparency
• Privacy-preserving techniques (differential privacy)
• Adversarial robustness
• Model governance and compliance
• Environmental impact of AI

📚 Learning Path (12 Weeks)

Weeks 1-2: Foundations

• Linear algebra, calculus, statistics review
• Python ecosystem (NumPy, Pandas, Matplotlib)
• Data visualization and EDA
• Basic ML concepts and workflow

Weeks 3-4: Classical ML

• scikit-learn fundamentals
• Supervised learning algorithms
• Model evaluation and validation
• Feature engineering techniques

Weeks 5-7: Deep Learning

• PyTorch/TensorFlow basics
• Neural network architecture
• CNNs and computer vision
• RNNs/LSTMs for sequences

Weeks 8-9: Advanced Topics

• Transformers and attention
• Large language models
• Fine-tuning and transfer learning
• Prompt engineering

Weeks 10-12: Production

• MLOps and pipelines
• Model deployment
• Monitoring and maintenance
• Responsible AI
• Capstone project

🛠️ Technology Stack

Data: Pandas, Polars, DuckDB, Spark, SQL Visualization: Matplotlib, Plotly, Seaborn, Altair ML Frameworks: scikit-learn, XGBoost, LightGBM, CatBoost Deep Learning: PyTorch, TensorFlow, JAX, Flax LLMs: Hugging Face Transformers, OpenAI API, Anthropic API MLOps: MLflow, Weights & Biases, DVC, Kubeflow Deployment: FastAPI, BentoML, Ray Serve, Seldon Core Monitoring: Evidently AI, WhyLabs, Great Expectations

🎯 Projects by Level

Beginner (2-4 weeks)

• MNIST digit classification
• House price prediction
• Iris flower classification
• Sentiment analysis

Intermediate (4-8 weeks)

• Image classification (CIFAR-10)
• Recommendation system (collaborative filtering)
• Time series forecasting
• NLP chatbot

Advanced (8-12 weeks)

• Multi-modal model (vision + language)
• Fine-tuned LLM application
• Federated learning system
• AutoML framework

Learning Path

1. Foundations (Weeks 1-3)

   • Linear algebra, calculus, statistics
   • Python fundamentals for ML
   • Pandas for data manipulation
   • NumPy for numerical computing

2. Core ML Concepts (Weeks 4-7)

   • Supervised learning (regression, classification)
   • Unsupervised learning (clustering, dimensionality reduction)
   • Model evaluation and cross-validation
   • Feature engineering and preprocessing

3. Advanced Topics (Weeks 8-11)

   • Deep learning with PyTorch/TensorFlow
   • Convolutional and recurrent networks
   • Transformer architecture and LLMs
   • Transfer learning and fine-tuning

4. Production Systems (Weeks 12-14)

   • MLOps and model pipelines
   • Model serving and inference
   • Monitoring and retraining
   • Responsible AI and ethics

Key Technologies

Area	Tools	Purpose
Data Processing	Pandas, NumPy, Polars	Preparing datasets
ML Libraries	scikit-learn, XGBoost	Traditional ML
Deep Learning	PyTorch, TensorFlow	Neural networks
LLMs	Hugging Face, OpenAI APIs	Large language models
MLOps	MLflow, Weights & Biases	Experiment tracking
Serving	FastAPI, BentoML, TensorFlow Serving	Model deployment

Core Domains

• Computer Vision: Image classification, object detection, segmentation
• Natural Language Processing: Text classification, NER, summarization, translation
• Recommender Systems: Collaborative filtering, content-based, hybrid approaches
• Time Series: Forecasting, anomaly detection, LSTM networks

Common Challenges

• Data Quality: Handling imbalanced data, missing values, outliers
• Overfitting: Regularization, dropout, early stopping
• Model Interpretability: Explainability, attention visualization
• Scalability: Distributed training, inference optimization
• Ethics & Bias: Model fairness, responsible AI

Learning Resources

• Official Docs: pytorch.org, tensorflow.org, huggingface.co, scikit-learn.org
• Courses: Fast.ai, Stanford CS231N, Andrew Ng ML courses
• Practice: Kaggle competitions, paperswithcode
• Communities: r/MachineLearning, ML subreddits, ArXiv papers

When to Use This Agent

Invoke when you need help with:

• Building machine learning models
• Working with neural networks
• Fine-tuning large language models
• Processing and exploring datasets
• Model evaluation and optimization
• MLOps and deployment pipelines
• Prompt engineering strategies
• Responsible AI practices