🔥 MLOps at Scale 🦅

🌟 An end-to-end full-stack Data Science and AI/ML project effectively implementing ML models, MLOps practices, scalable machine learning, and data storytelling. ✨

📚 🛠️ Experiment (Design + Develop) --> 🚀 Production (Deploy + Iterate) ⚙️: Full-Stack Data Science and Production-Grade Machine Learning at Scale are the fastest-growing fields in technology. This repository aims to develop professional and strong advanced analytics skills to compete in the age of digital and AI. 🏁

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🎯 End-to-end full-stack machine learning from experimental (design + development) to production (deployment + iteration) for iteratively building reliable production-grade AI/ML applications.

• 💡 Agile CRISP-DM for Data Science and Machine Learning
  • Cookiecutter Data Science (CCDS) V2: data science tooling and MLOps
  • Agile Implementation of CRISP-DM for Data Science and Machine Learning
• ⚙️ MLOps
  • 💻 DevOps best practices for developing and deploying machine learning models.
  • ⚙️ Build an end-to-end machine learning system by connecting MLOps components such as tracking, testing, serving, and orchestration.
• 🚀 Dev to Prod:
  • 🐙 Develop robust CI/CD workflows to continuously train and deploy better models in a modular way that integrates with any stack.
  • 📈 Scale: ML workloads (data, training, tuning, and serving) are easily scalable, facilitating a quick and reliable transition from development to production without requiring code or infrastructure modifications.

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Deliverables 💎

📆	⏰ Deliverables / Tasks Done	🔗 Reference Links
01	🎓 AWS Certified Data Analytics - Specialty (DAS) (Collecting Streaming Data, Data Collection and Getting Data, Amazon Elastic Map Reduce (EMR), Using Redshift & Redshift Maintenance & Operations, AWS Glue, Athena, and QuickSight, ElasticSearch, AWS Security Services) ✅	A Cloud Guru - DAS & ACG Practice Exam & UDemy Practice Exam
02	🎓 AWS Certified Machine Learning - Specialty (MLS-C01) (Data Preparation, Data Analysis and Visualization, Modeling, Algorithms, Evaluation and Optimization, Implementation and Operations) ☑️	A Cloud Guru - MLS-C01 & ACG Practice Exam & UDemy Practice Exam
03	🛠 Reproducible Local Development for Data Science and Machine Learning projects	Data Science
04	👨‍💻 Analytics-Experience Project: Time Series Forecasting & Machine Learning Prediction	Analytics-Experience Project
05	📚 MLOps	MLOps
06	💹 Analytics Dashboard: Data Insights & Visual Analytics	Visual Analytics
07	🚀 Scalable MLOps MLOps at Production-grade Scale	Scalable MLOps

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Project Organization

🛠 Production-grade project structure for successful data-science or machine-learning projects 🚀

💎 End-to-end Data Science and Advanced Analytics Experience 🚀

├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          🤝 Explain your project and its structure for better collaboration.
├── config/
│   └── logging.config.ini
├── data               🔍 Where all your raw and processed data files are stored.
│   ├── external       <- Data from third-party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, unprocessed, immutable data dump.
│
├── docs               📓 A default docusaurus | mkdocs project; see docusaurus.io | mkdocs.org for details
│
├── models             🧠 Store your trained and serialized models for easy access and versioning.
│
├── notebooks          💻 Jupyter notebooks for exploration and visualization.
│   ├── data_exploration.ipynb
│   ├── data_preprocessing.ipynb
│   ├── model_training.ipynb
│   └── model_evaluation.ipynb
│
├── pyproject.toml     <- Project configuration file with package metadata for analytics
│                         and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            📊 Generated analysis (reports, charts, and plots) as HTML, PDF, LaTeX.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   🛠 The requirements file for reproducing the analysis environment, for easy environment setup.
│
├── setup.cfg          <- Configuration file for flake8
│
├── src                💾 Source code for data processing, feature engineering, and model training.
│   ├── data/
│   │   └── data_preprocessing.py
│   ├── features/
│   │   └── feature_engineering.py
│   ├── models/
│   │   └── model.py
│   └── utils/
│       └── helper_functions.py
├── tests/
│   ├── test_data_preprocessing.py
│   ├── test_feature_engineering.py
│   └── test_model.py
├── setup.py           🛠 A Python script to make the project installable.
├── Dockerfile
├── docker-compose.yml
├── .gitignore
└── analytics          🧩 Source code for use in this project.
    │
    ├── __init__.py    <- Makes analytics a Python module
    │
    ├── data           <- Scripts to download, preprocess, or generate data
    │   └── make_dataset.py
    │
    ├── features       <- Scripts to turn raw data into features for modeling
    │   └── build_features.py
    │
    ├── models         <- Scripts to train models and then use trained models to make predictions.           
    │   ├── predict_model.py
    │   └── train_model.py
    │
    └── visualization  <- Scripts to create exploratory and results-oriented visualizations
        └── visualize.py