Comprehensive MLOps Bootcamp: from Basics to Advanced tools

Explore the evolution of AI and machine learning, the challenges of production, and how MLOps uses automation, versioning, and monitoring to streamline scalable deployment amid rising trends.

Highlight the rapid adoption of MLOps and its impact on production, as profits rise from 23.2 billion in 2019 to 126 billion by 2025.

Explore MLOps fundamentals, emphasizing scalable, collaborative, and reproducible model development, from use-case discovery through data engineering, model development, deployment, and monitoring.

Explore how devops and data ops drive mlops for iterative production of models and data. Emphasize continuous integration, deployment, a data feedback loop, and ongoing monitoring to ensure quality.

Explore the fundamentals of MLOps, its components, and key problems. Ensure data and model artifacts versioning for reproducibility, monitor data drift, and enable reusable feature generation to speed development.

Explore the core MLOps components such as feature store, data versioning, metadata store, logging, and model versioning to enable reproducible, governed model deployment.

Explore the evolution of machine learning and the challenges of deploying models to production, and see how mlops, automation, monitoring, and Gartner trend quadrant insights drive scalable, secure workflows.

Master the full MLOps cycle, from versioning model and data to production deployment and monitoring. Automate development with a feature warehouse and real-time or batch pipelines, retraining automatically for performance.

Install and configure Python tools for MLOps using Anaconda, create and activate a conda environment, and install PyCaret, scikit-learn, MLflow, DVC, and Docker, plus Jupyter Notebook.

Explore Jupyter Notebook, a web app for running and sharing code in Python, R, Scala with interactive graphs. Launch notebooks in a virtual MLOps environment, run cells, and switch kernels.

Learn to install Docker and Ubuntu on Windows by enabling virtualization in BIOS, installing Docker Desktop and the Linux kernel, configuring WSL, and running a test image.

Explore the design and prioritization of use cases in the MLOps process, define technical requirements, verify data, and apply the Valier Template to assess security, performance, usability, and compliance.

Leverage automated model development with AutoML using PyCaret to automate data prep, feature engineering, model training, hyperparameter tuning, and production transition.

Build a regression model with PyCaret on the traffic dataset to predict traffic volume from weather and day features. Cover setup, model comparison, tuning, finalization, and deployment-ready export.

Explore diamond price prediction by building a regression model with PyCaret and Picard, using pandas profiling for EDA, data normalization and transformation, and preprocessing with holdout data and model versioning.

Train and optimize advanced models (xgboost, catboost, lightgbm) with pycaret, tune hyperparameters using a custom grid, and evaluate with residuals and feature-importance plots before production deployment.

Train the final pycaret model on the full dataset, export the pipeline with pickle using save_model, and evaluate on unseen data with predict_model and r_squared for production deployment.

Register and version machine learning models with MLflow to ensure reproducibility and track experiments. Learn MLflow tracking server, registry, deployment across spark or pandas workflows, with artifacts and metrics.

Register and version a scikit-learn model with MLflow, logging rmse, mae, and r2 metrics while training an elastic net on wine quality data.

Register a Pycaret model with MLflow by enabling log experiment and an experiment name in setup, then load and save the model locally and in MLflow, and finally run predictions.

Practice integrating Pycaret with Dask hub in Google Colab, install Pycaret and MLflow, train a diamond price model, log experiments, compare models, and explore datasets, artifacts, and collaboration.

Register a model and dataset with Pycaret and DagsHub in Google Colab, logging experiments with MLflow and comparing models like extra trees regressor and light gradient boosting machine.

Leverage DVC for dataset versioning in MLOps to enable reproducible experiments by storing only meta files and pointers, reconstructing specific data versions without bloating git.

Explore DVC pipelines, mimic Git commands, initialize DVC and Git environments, use DVC add on training data, and push to remote storage with DVC push.

Learn how to use DVC to store data in a remote repository and manage it with commands like DVC init, add, commit, remote add, push, and checkout.

Build end-to-end DVC pipelines for data preparation, model training, and evaluation with DVC repro. Define stages with inputs, outputs, and commands to automate execution.

Learn to create a remote versioned code repository with DagsHub, integrate git, dvc, and mlflow, and run experiments to identify the best model.

Perform exploratory data analysis (EDA) and preprocessing to train a machine learning classifier, using pandas profiling, feature engineering, and cleanup of high-cardinality text features.

Deploy your project to Dash hub via git by creating a local environment, staging changes with git add, committing with a descriptive message, and pushing to the remote. Review status, access requirements, and collaboration features such as notes, issues, and comments to enable teamwork.

Learn to create a DagsHub account, register or log in, initialize git, configure the remote, and set up data and output folders for your project.

Create and activate a python virtual environment in the project folder using python3, install libraries from a requirements file, and configure data handling with gitignore for future Dash hub integration.

Deploy the machine learning project to DagsHub via git, stage and commit changes with descriptive messages, push to the remote, and explore collaboration, branches, and future data or dvc integration.

Version control the model and data with Docker Hub, Dask hub, and DVC; train a logistic regression text classifier with feature engineering and evaluate with AUC and F1.

Enforce reproducible data with separate train-test splits and consistent random seeds for reliable production results. Save trained models with Joblib and apply cross-validation to address class imbalance.

Version data and models with DVC and integrate it with git to manage datasets and trained outputs. Install, initialize, and add data and outputs to DVC, then commit changes.

Train a new classifier version and deploy it with DVC and git, preserving the dataset in DVC, then push the model to DagsHub after evaluating with f1 and bias.

Learn to create and register experiments in DagsHub by varying hyperparameters and logging metrics, then compare results to identify the best performing configuration and version datasets and models.

Train a churn-prediction classification model using PyCaret, apply necessary data preprocessing, and run a local MLflow experiment with DVC integration, guided by a reference notebook to compare solutions.

Develop and evaluate a churn prediction model using Pycaret in a conda environment, train with adaboostclassifier, compare models, generate MLflow logs, and save the final model with artifacts.

Create a remote repository for trained model on DagsHub, configure git access, initialize local git, add model files and notebook, and synchronize with the online repo while comparing solutions.

Set up your git environment and create a repository on Dash hub, then link it to your local folder, add files, ignore m runs and data folders, commit, and push.

Practice data versioning with DVC by downloading raw data, saving it as a CSV in the data folder, configuring git to ignore data folder, and pushing versioned datasets to hub.

Version your dataset with DVC by initializing, adding data and output, committing, configuring a dash hub remote, and pushing; then automate data download and preprocessing in a notebook.

Register and log experiments in a shared MLflow environment on the Dash hub server, configure access credentials, run experiments with a chat model, and select the best models.

Register and compare experiments on a shared MLflow server via Dash hub, log remote runs with PyCaret, and select the best model by AUC, F1, and accuracy.