Federal University of Rio Grande do Norte

Technology Center

Department of Computer Engineering and Automation

Machine Learning Based Systems Design

Reference	Link
📚 Noah Gift, Alfredo Deza	Practical MLOps
📚 Chip Huyen	Designing ML Systems
📚 Jason Brownlee	Deep Learning for NLP
💣 ChatGPT	OpenAI Chat
😃 CS329S - ML Systems Design	Stanford's MLOps course
🎯 Machine Learning Operations	MLOps Community

Lessons

Week 01

• Course Outline
• 🎯 Week Goals
  • Your main goal for this week is to create a personal repository for tracking your progress and coursework, gain access to the GitHub Education Pro pack, and learn how to start coding instantly using GitHub Codespaces, complete the GitHub Learning Game, and read complementary material.
• 🎉 GitHub Education Benefits
  • GitHub Education Pro: Get access to the GitHub Education Pro pack by visiting GitHub Education
• 🚀 Instant Coding with Codespaces
  • Learn how to start coding instantly using GitHub Codespaces
• 📖 Learning Resources
  • GitHub Learning Game: Check out the interactive Git learning game at GitHub Learning Game
  • Michael A. Lones. How to avoid machine learning pitfalls: a guide for academic researchers Arxiv
• Thread.run(study_mlops) 💻🚀

Week 02

• Command Line Interface Fundamentals
• 🎯 Week Goals
  • Get ready to unlock your inner Data Science Ninja! 🥷 This week is all about getting hands-on with the command line. Why? Because it's like the Swiss Army knife for any data scientist—versatile, indispensable, and oh-so-powerful!

Week 03

• Clean Code Principles for Data Science and Machine Learning
• 🎯 Week Goals
  • This week is all about mastering the art of writing clean and efficient code. As a future data scientist or machine learning engineer, writing code that is both understandable and maintainable is crucial. We'll dig into principles like DRY and KISS, refactoring and see how they can be applied to data science and machine learning projects.
• 🤲 Hands-On Activities
  • Topic Name: Explore the practical aspects of the concepts discussed this week. Learn through coding exercises and real-world examples.
  • : Functions: fundamentals and intermediate
    • 👊 Skills You'll Gain: You will learn how to a) define and create functions and pipelines, b) debug funcitons, c) define default arguments, d) use multiples return statements, e) return multiples variables, f) variable scopes and more.
    • ⏳ Estimated time: 6h

Week 04

• Code Documentation and Tooling in Python
• 🎯 Week Goals
  • The objective of this week is to delve into Python code documentation and related tooling. Understanding how to document code effectively will make your projects maintainable and understandable. We will explore line-level, function/module-level, and project-level documentation.
• 📝 PEP References
  • PEP-3107: Function Annotations
  • PEP-484: Type Hints
  • PEP-526: Variable Annotations
  • PEP-557: Data Classes
  • PEP-585: Type Hinting Generics In Standard Collections
• 🤲 Hands-On Activities
  • Topic Name: Explore Python documentation strategies and tooling through hands-on exercises.
    • Pycodestyle
    • Pylint
    • Autopep8

Week 05

• Handling Errors, Writing Tests and Logs
• 🎯 Week Goals
  • This week, we dive into three essential pillars of reliable machine learning systems: error handling, testing, and logging. These are critical skills for building robust, maintainable ML pipelines and applications.
• 🤲 Hands-On Activities
  • Practical Error Handling, Testing, and Logging: Explore these concepts through coding exercises and real-world examples.
• 📖 Learning Resources
  • Python Exception Handling Techniques
  • pytest: A framework for Python testing
  • Python Logging: An In-Depth Tutorial

Week 06

• Machine Learning Fundamentals
  • What is Machine Learning (ML)?
  • ML types
  • Main challenges of ML
    • Variables, pipeline, and controlling chaos
    • Train, dev and test sets
    • Bias vs Variance
  • Evaluation metrics
    • How to choose an evaluation metric?
    • Threshold metrics
    • Ranking metrics
• Essential Guide for NLP
  • Pre-processing and cleaning
  • Text representation
  • Preparing Text Data: manual tokenization, NLTK, Scikit-Learng, Keras, so on.

Week 07

• Steps to Process Film Review Data for Sentiment Analysis
  • Hands on Extract, Transform and Load (ETL): opening and reading files, cleaning the content,compiling a preliminary vocabulary, processing multiple files, refining the vocabulary, saving the final vocabulary.
• Deep Learning Fundamentals
  • The perceptron
  • Building Neural Networks
  • Matrix Dimension
  • Applying Neural Networks
  • Training a Neural Networks
  • Backpropagation with Pencil & Paper
  • Learning rate & Batch Size
  • Exponentially Weighted Average
  • Adam, Momentum, RMSProp, Learning Rate Decay
  • Hands on DL fundamentals
    • You'll learn how to: a) Understand how neural networks are represented; b) understand how adding hidden layers can provide improved model performance; c) Understand how neural networks capture nonlinearity in the data.

Week 08

• Construct a Neural Bag-of-Words Framework for Evaluating Sentiments
  • Hands on Sentiment Analysis Model.
• Crash course about Weights and Biases

Week 09

• A MLOps case study using Weights and Biases

  • Fetch Data.
  • EDA.
  • Preprocessing Data.
  • Data Check.
  • Data Segregation.
  • Vocabulary creation.
  • Train.

• Sequence models for Deep Learning

  • You'll learn how to: a) describing sequential Neural Network models; b) determining when to use RNN, GRU, and LSTM; c) implement a sequential model using a basic RNN.

Week 10

• Natural Language Processing for Deep Learning
  • You'll learn how to: a) processing and exploring text data, b) visualizing text data using a word cloud, c) implementing tokenization and word embeddings, d) building sequence models and e) building a transformer-based text classification model.
• Hands on project: the target is to use Weights and Biases, and Directed Acyclic Graphs (DAG) to build a pipeline for a NLP project.
  • Building a Data Pipeline
  • You'll learn how to: a) writing a robust pipeline with a scheduler in Python, b) using advanced Python concepts like closures, decorators, and more.