Module 1 - Fundamentals

Fundamentals Module

This module establishes the foundational knowledge required for ML Engineering, building upon your existing DevOps/SRE expertise.

Learning Objectives

By the end of this module, you will:

• Master Python for ML engineering tasks
• Understand core mathematical concepts used in ML
• Grasp fundamental ML algorithms and their applications
• Get hands-on experience with deep learning frameworks

Week 1: Python for ML Engineering

Topics Covered

• Python Basics for DevOps/SRE - Core Python with infrastructure automation examples
• NumPy for Infrastructure Metrics - Efficient numerical operations for metrics analysis
• Pandas for Log Analysis - Data manipulation and log processing
• Matplotlib for Monitoring Dashboards - Creating professional visualizations

Hands-on Labs

1. Build a data processing pipeline using Python
2. Optimize Python code for performance
3. Create reusable ML utilities

DevOps Connection

Your experience with scripting and automation will accelerate Python mastery. Focus on:

• Writing testable, maintainable code
• Creating robust error handling
• Implementing logging and monitoring hooks

📚 Start Week 1 Learning →

Week 2: Mathematics & Statistics

Topics Covered

• Linear algebra (vectors, matrices, operations)
• Statistics (distributions, hypothesis testing)
• Calculus (derivatives, chain rule for backpropagation)

Practical Applications

• Understanding gradient descent optimization
• Interpreting model metrics
• Feature scaling and normalization

DevOps Connection

Similar to capacity planning and performance analysis, these concepts help you:

• Understand model behavior
• Debug training issues
• Optimize model performance

Week 3: Machine Learning Fundamentals

Core Concepts

• Supervised Learning
  • Linear/Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines
• Unsupervised Learning
  • K-means clustering
  • PCA for dimensionality reduction
• Model Evaluation
  • Accuracy, Precision, Recall, F1
  • ROC curves and AUC
  • Cross-validation strategies

Hands-on Projects

1. Build a classification model for system anomaly detection
2. Create a regression model for resource prediction
3. Implement clustering for log analysis

DevOps Connection

Apply these algorithms to:

• Anomaly detection in metrics
• Capacity forecasting
• Automated incident classification

Week 4: Deep Learning Basics

Framework Introduction

• TensorFlow/Keras basics
• PyTorch fundamentals
• Model architecture patterns

Neural Network Types

• Feedforward networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transformer architecture overview

Practical Exercises

1. Build a neural network for time-series prediction
2. Implement a CNN for image classification
3. Create an RNN for log sequence analysis

DevOps Connection

Deep learning applications in operations:

• Predictive maintenance
• Automated log analysis
• Intelligent alerting systems

Assessment

Week 1-2 Checkpoint

• Python coding assessment
• Mathematical problem set

Week 3-4 Project

Build an ML pipeline that:

• Ingests operational data
• Trains a model
• Evaluates performance
• Provides predictions via API

Resources

Required Reading

• “Python Machine Learning” by Sebastian Raschka (Chapters 1-4)
• “The Elements of Statistical Learning” (Selected sections)

Online Materials

• Fast.ai Practical Deep Learning (Lessons 1-3)
• 3Blue1Brown Neural Network series
• Google’s Machine Learning Crash Course
• Andrew Ng’s Machine Learning Course
• Deep Learning Specialization
• MIT 6.034 Artificial Intelligence
• MIT 6.S191 Introduction to Deep Learning (Alexander Amini)

Tools Setup

# Create virtual environment
python -m venv ml-env
source ml-env/bin/activate

# Install core packages
pip install numpy pandas scikit-learn
pip install tensorflow pytorch
pip install jupyter mlflow

Next Steps

After completing this module, you’ll be ready for Module 2: MLOps Core, where you’ll apply DevOps principles to machine learning workflows.