Starway to Orione DoAI

DoAI is the Development Operation and Artificial Intelligence

This is the learning path every new Cloud Data Architect has to follow when joining the XPeppers Cloud team. This path reflects our team's culture and values, which have their roots in the agile values and principles.

• Flat Organizations:
  • The Flat Way #onboarding
  • Teal Is The New Black: Self-management and the future of work
  • Collaborative decision making in self-organizing teams
• Read chapters 1, 4, 5, 7 of XP Explained #onboarding
• Read chapters 2, 6 of XP Explained
• Iterative and Incremental Development:
  • Waterfall #onboarding
  • Agile #onboarding
  • Transition
• For italian speakers, Watch "Perché è così difficile fare Extreme Programming" by Matteo Vaccari #onboarding
• Pair Programming #onboarding
• Agile Mindset:
  • What Exactly is the Agile Mindset? #onboarding
• Read The Pomodoro Technique paper
• Read first chapter of "Applying UML and Patterns"
  • Try to estimate the time needed to study that chapter (using the pomodoro technique)
  • Answer (for example on the team's wiki pages)
    • What is analysis?
    • What is design?
    • What's the difference between them?
    • What is design for?
      • in other words, how would you reply to the following statement: "I just need to understand what to do (analysis) and then do it (coding). Everything else does not matter!"

Table of Contents

Table of Contents

1. Programming
2. Maths & Statistics
3. Toolboxes
4. Machine Learning
5. Deep Learning
6. Cloud Operation and DevOps
7. Machine Learning Operations (MLOps)
8. NLP & NLU
9. Recommendation System
10. Reinforcement Learning

Please feel free to fork and contribute, add materials, fix the existing ones and propose new stuff.

During all the plan read The Phoenix Project.

Programming

• Basic Python
• Object-oriented Programming
• Python course by Analytics Vidhya
• Python Regular Expressions
• RE Cheat Sheet
• Git
• Shell Script
• Competitive: Starway to Orione Dev

🠥🠥 Back to Table of Contents 🠥🠥

Maths & Statistics

• Linear Algebra
• Calculus
• Descriptive Statistics
• Data Distributions
• Convolutions
• Exploratory Data Analysis
• Regression

🠥🠥 Back to Table of Contents 🠥🠥

Toolboxes

• Evaluating and Exploring data in Python
  • Jupyter
  • Anaconda #optional
  • Zeppelin #optional
  • Notebook Alternative #optional
• Scientific libraries in Python
  • Numpy
  • Pandas
  • Scipy
  • Scikit-learn
  • Matplotlib
  • Seaborn
  • Dask #optional
  • Apache Spark #optional
  • Spark & Hadoop Developer #optional
  • Statsmodels #optional

🠥🠥 Back to Table of Contents 🠥🠥

Machine Learning

• Google Crash Course
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• Interpretable Machine Learning #optional
• Blog & Articles:
  • Workflow of a Machine Learning project
  • Machine Learning for Everyone
  • Introduction to Data Visualization in Python
  • Machine learning in Python with Scikit-learn
• Algorithms
  • Naive Bayes, Support Vector Machine, Random Forest
  • AdaBoost, Gradient Boosting, XGBoost, CatBoost
  • Bagging Classifier, Voting Classifier, Stacking Classifier
  • K-NN, K-Means Clustering
  • SageMaker Built-in
  • SageMaker Examples
• Evaluation Metrics
  • Confusion Matrix, Accuracy, Precision, Recall, F Score
  • MAE, MSE, ROC, ROC AUC
  • Hamming Loss, Top K Accuracy, MRR
  • DCG, NDCG, PSNR, SSIM, IoU
• Homeworks #optional
• Extra:
  • Machine Learning, Data Science and Deep Learning with Python #optional
  • Standardization in case of real-time predictions #optional

🠥🠥 Back to Table of Contents 🠥🠥

Deep Learning

• Dive into Deep Learning
• Practical Deep Learning for Coders
• Activation Functions
• Linear Layer
• CNN (Convolutional Neural Networks)
• RNN (Recurrent Neural Networks)
• Optimization
• Loss Functions / Objective Functions
• Dropout
• Batchnorm
• Learning Rate Scheduler
• Frameworks:
  • MXNet
  • PyTorch
  • Tensorflow
  • Keras

🠥🠥 Back to Table of Contents 🠥🠥

Cloud Operation and DevOps

• Machine Learning Lens
• Book: Data Science on AWS
• AWS Machine Learning Stack
  • ML Frameworks & Infrastructure
  • Amazon SageMaker
  • AI Services
• More AWS Services:
  • AWS Lambda
  • CI/CD CodePipeline
  • Step Functions
  • Elastic File System
  • Fargate
  • AWS Batch
• Utils:
  • Python for DevOps
  • Boto3
  • CloudFormation
  • SAM
  • Data Science SDK
  • Scala Data Quality | Python Data Quality
  • Terraform #optional
  • Troposhpere #optional
  • Goformation #optional
  • Aws Data Wrangle #optional
• Training and Certification
  • AWS Technical Essentials
  • Machine Learning on AWS
  • Data Analytics
• Competitive: Starway to Orione Cloud

🠥🠥 Back to Table of Contents 🠥🠥

Machine Learning Operations (MLOps)

• What is MLOps
• MLOps Overview
• Feature Store:
  • What is a Feature Store
  • MLOps with a Feature Store
  • Why Feature Stores They Critical for Scaling Data Science
• Version Control System for Machine Learning
• SageMaker MLOps
• DevOps for Machine Learning
  • Automate MLOps with SageMaker Projects
  • Create ML workflows with Amazon SageMaker Pipelines
  • A/B testing ML with Amazon SageMaker
• Workshop:
  • MLOps with SageMaker
• Competitive: Awesome MLOps

🠥🠥 Back to Table of Contents 🠥🠥

Machine Learning Applications

A more specific section about some Machine Learning Fields.

NLP & NLU

• Introduction to NLP
• Reading Comprehension
• Question Answering on the SQuAD Dataset
• BERT Explained
• Transformers
• SpaCy
• Learning to Rank
• Natural Language Processing with Python
• Unsupervised Translation of Programming Languages
• Perplexity and BLEU Score metrics

🠥🠥 Back to Table of Contents 🠥🠥

Recommendation System

• What Are Recommender Systems
• Matrix Factorization
• How does Netflix recommend movies using Matrix Factorization
• Collaborative filtering

🠥🠥 Back to Table of Contents 🠥🠥

Reinforcement Learning

• Reinforcement Learning Introduction
• Videos:
  • Getting started with OpenAI Gym
  • Q-Learning
  • Q Learning Intro/Table
  • Q Learning Algorithm and Agent
  • Q-Learning Agent Analysis
  • Creating A Reinforcement Learning (RL) Environment
  • Deep Q Learning w/ DQN
  • Training & Testing Deep reinforcement learning (DQN) Agent
• Reinforcement Learning on AWS:
  • SageMaker DeepRacer
  • Deep Reinforcement Learning Models: Tips & Tricks for Writing Reward Functions
  • Proximal Policy Optimization #optional

🠥🠥 Back to Table of Contents 🠥🠥