Complete MLOps Bootcamp | From Zero to Hero in Python 2022

Explore the evolution from artificial intelligence to machine learning and deep learning, and examine production challenges addressed by MLOps, automated workflows, and model versioning.

Explore the high-impact benefits of machine learning as organizations deploy models to production, boosting profit margins from 3% to 15% and driving rapid, exponential growth from 2019 to 2025.

Navigate the foundations of lops, focusing on scalable, collaborative, and reproducible model development across five parts: discovery of use cases, data engineering, pipeline development, production, and startup and monitoring.

Explore DevOps and data ops fundamentals for machine learning, highlighting continuous integration, deployment, data quality, feedback loops, monitoring, and the evolving roles in MOPS ecosystems.

Explore how MLOps addresses versioning of models and data, enables reproducible experiments, monitors performance to detect data drift, and accelerates feature engineering by reusing functions.

Explore the essential MLOps components, from feature stores and data versioning to model registration and deployment. Learn how monitoring, retraining, and CI/CD ensure reproducibility, governance, and scalable ML workflows.

Explore essential MLOps libraries for data labeling with V7 or Label Box, data exploration, feature engineering, and model training across the lifecycle using Jupyter notebooks and tracking tools.

Understand the mlops cycle, from versioning data and models and building a feature warehouse with Feast and DVC, to automated deployment, monitoring, and retraining.

Install essential mlops tools with Anaconda to create a Python 3.7+ virtual environment, install Pycaret, scikit, Mlflow, DBC, discuss Docker installation, and use Jupyter Notebook.

Learn the basics of Jupyter Notebook, a web application for creating and sharing code documents in Python, R, and Scala, with interactive outputs and easy kernel switching.

learn to install docker and ubuntu on windows using wsl, enable virtualization in bios, install the linux kernel, and verify the setup with ubuntu and docker desktop.

Learn to standardize machine learning projects with cookie cutter, generating a structured template—data, models, notebooks, and results folders—driven by templates, metadata, and automation.

Learn to manage projects with poetry for dependencies, Hydra for configuration, pre-commit for automated reviews, and DVC for data versioning, while using Git and Dock for documentation.

Learn poetry for dependency management as an alternative to pip, separating main and sub dependencies into readable files. Use poetry to create projects and manage libraries.

Learn to create a Makefile to automate tasks, configure the environment with poetry, activate the virtual environment, and initialize git using targets like activate, setup, and install.

Learn how Hydra manages YAML configuration files to avoid hard-coded parameters, enabling dynamic data paths, configurable input variables, and easy switching between model configurations and execution records.

Apply Hydra to a machine learning project by configuring preprocessing, feature transformation, and a pipeline for a logistic regression model, training, evaluating, and saving predictions with config-driven data loading.

Automatically verify and fix Python code before git commits using pre commit plugins like black, Flake eight, escort, and interrogate to enforce pep eight, import order, and doc strings.

Learn to set up pre-commit, install and activate it in your environment, and apply Black for Python formatting and Flake8 to enforce PEP 8, automating code quality checks.

Learn to automate code review before commits by using pre-commit plugins like isort and interrogate, organizing imports alphabetically and by type, and enforcing docstrings and code quality via a config.

Learn to automatically generate code documentation for machine learning projects using doc strings, Makefile, and Doc, to create API docs that are viewable locally and savable.

Learn how to design and prioritize Mlops use cases, verify data and metrics, and apply the Foliar template to guide problem analysis, ethical and legal considerations, and production deployment.

Explore automated model development with AutoML and PyCaret, enabling data preparation, feature engineering, model training, hyperparameter tuning, and production transition with tools like scikit-learn, XGBoost, and MLflow.

Learn to build a regression model from a traffic dataset using pycaret, including data setup, preprocessing, compare models, tune and finalize, then save, load, and predict.

Develop a regression model to predict diamond prices using diamond dataset, with exploratory analysis via pandas profiling and Picard preprocessing that converts categoricals to numerical and uses a 10% holdout.

Train and optimize advanced models in PyCaret, including XGBoost, CatBoost, and LightGBM, using tune model and custom hyperparameter grids, then evaluate plots and test data to assess overfitting.

Deploy models with pycaret to production by training on full dataset, saving as a pickle, and evaluating on unseen data using a normalization and transformation pipeline with r-squared of 0.98.

Register and version machine learning models with MLflow to ensure reproducibility and track experiments. Explore MLflow components—the tracking server, project, registry, and model deployment—and view artifacts like feature importance plots.

Learn to register and version machine learning models with Mlflow by building an elastic net model for wine quality, logging parameters, metrics (rmse, mse, r2), and artifacts.

Register a PyCaret regression model with Mlflow and enable log experiments under a named experiment. Load the model via the load_model function and predict using code or Mlflow UI.

Learn to version datasets with the DVC library to enable reproducible experiments, compare models, and manage data with storage independence and lightweight meta files.

Learn to run DVC pipelines and manage data with Git in a data science project, using DVC init, add, cache, and remote storage, plus push and pull workflows.

Initialize and configure dvc within a git workflow, track data with dvc add and commit, set up a remote, and push changes to the remote repository.

Chain end-to-end machine learning steps with DVC repro, linking stages from prepare to train to evaluate and generating outputs like train.csv, test.csv, and model files.

Explore creating a remote, reusable code repository with DagsHub, integrate Git and DVC for model and data versioning, and train and compare multiple experiments to identify the best performing experiment.

Perform exploratory data analysis (eda) and data preprocessing to understand distributions, engineer features, and prepare a first classification model for a machine learning task using Pandas profiling.

Split the data into train and test sets, apply normalization with a min-max scaler and power transformer, and build a vectorized text logistic regression model evaluated by AUC.

Learn to set up a DagsHub account, create and configure a project repository, and upload a trained model and its data to the hub using Git.

Create python virtual environment inside project folder, activate it, and install libraries from a requirements file; then configure data folder, add it to gitignore, and prepare data for dvc tracking.

Deploy the local project to Dash Hub via git push, stage with git add, commit with a descriptive message, then push to the remote master for collaboration, issues, and comments.

Version and track a machine learning model and data with DVC and Git; train a logistic regression classifier on text features and commit changes to the local hub.

Ensure reproducible data with a separated train-test split and cross validation, plus a consistent random seed, for production-ready models. Save trained models and separate training from inference to enable deployments.

Version your datasets and models with dvc integrated with git, tracking data versions, and learning to initialize dvc, add data and outputs, and commit changes while handling common setup issues.

train and deploy a new model version with dvc, track datasets and models, compare with previous versions, and push updates to dagsHub using git and dvc tokens.

Experiment with multiple hyperparameter sets using the dash hub logger to register metrics and hyperparameters, then compare experiments in the dash hub desktop to select the best performing model.

Train multiple experiments and register them in DagsHub to compare performance and select the best model by F1.