Mastering MLOps: Complete course for ML Operations

Explore the evolution of ai from early models to deep learning, and examine deployment challenges and the mlops trend that streamlines production.

Machine learning boosts profits as models go into production, with profit margins rising 3 to 15%; Mlops accelerates rapid adoption and exponential gains by 2025.

Explore mlops fundamentals, enabling scalable, collaborative, and reproducible model development across discovery, data engineering, pipeline development, production, and monitoring with automated deployment and retraining.

Learn how devops and data ops enable iterative mlops deployment with continuous integration, delivery, and monitoring. Understand data quality and the data feedback loop across data scientists and other roles.

Discover how MLOps tackles versioning of code, data, and artifacts to ensure reproducible experiments, monitor model performance to address data drift, and speed up feature engineering by reusing functions.

Explore core mlops components, from feature stores and data versioning to metadata, model versioning and registration; deploy, monitor, and retrain models with CI/CD and scalable endpoints.

Explore essential mlops libraries for data exploration, labeling, visualization, feature engineering, training, debugging, tracking, packaging, serving, and monitoring, including V7, label Box, MLflow, Tensorboard, Kubeflow, Onnx, Bento, Streamlit, and DVC.

Delineate the mlops stages from data collection and preparation to model deployment and monitoring. Emphasize version control for model and data, automated workflows, and a feature warehouse integration.

Learn to set up ml ops environments by installing Anaconda with Python 3.7+, creating conda virtual environments, installing pycaret, scikit, mlflow, dbc, Docker, using a requirements.txt, and launching Jupyter notebook.

Explore the basics of Jupyter Notebook, a web-based environment for Python, R, and Scala that supports interactive outputs, graphs, markdown, and kernel switching in a Mlops virtual environment.

Follow step-by-step guidance to install docker and ubuntu, enable bios virtualization, install windows subsystem for linux and linux kernel, then ubuntu from the microsoft store, and run a test image.

Use the Foliar template to define the MLOps design stage, prioritize use cases, and specify data needs, ethics, and compliance for model development.

Master automated model development with auto machine learning using Pycaret, covering data preparation, feature engineering, training multiple models, model selection, hyperparameter tuning, evaluation, and production transition.

Explore the PyCaret library to build and optimize a basic regression model predicting traffic volume from weather features, using setup, compare models, tune, finalize, save, and load for production.

Explore eda and advanced preprocessing for a diamond price regression model using Picard and Pandas Profiling, featuring normalization, target transformation, rare labels, and multicollinearity removal with holdout data.

Train and compare models (XGBoost, CatBoost, LightGBM) with PyCaret, identify CatBoost as best, optimize it with tune_model, and train LightGBM via create_model using a custom grid (branches, depth, learning rate).

Deploy pycaret models to production by training on the full dataset, saving the pipeline as a pickle with save_model, and evaluating unseen data with predict_model, achieving an R-squared of 0.98.

Learn how Mlflow enables model registration and versioning, tracking experiments, and deploying models, preserving metadata, metrics, and artifacts to support reproducibility and selection of best models.

Register a scikit-learn elastic net model with MLflow, log metrics like rmse, mse, and r2, and store artifacts for versioning and model deployment.

Register a regression model with Mlflow via Pycaret by enabling log experiments in setup, saving artifacts locally and in Mlflow, then load the model and make predictions from the pipeline.

Learn to integrate pycaret with Dagshub, logging experiments to a remote mlflow server via DAX half and using dvc for data versioning and storage.

Register and track a diamond price model using PyCaret 3.0 and Dash Hub in Colab, logging data, models, and experiments, then compare models and collaborate with the team.

Learn how data versioning with DVC enables reproducible ML experiments by storing changes as meta files and pointers, reconstructing dataset versions across storage backends and pipelines built on dependency graphs.

Explore how DVC commands mirror Git operations to manage data, pipelines, and code across local and remote environments, including adding data, caching, pushing to remotes, and pulling updates.

Learn to use DVC to manage and restore data in a remote repository, from init and add to push, with git integration and versioning via checkout.

Explore how to build and run end-to-end DVC pipelines for machine learning, defining stages, dependencies, and outputs, and using DVC repro to orchestrate preprocessing, training, and evaluation.

Discover how to set up a DagsHub repository, version data with dvc, track experiments with mlflow and Dash Hub graphs, and develop a classification model on the coverflow dataset.

Perform exploratory data analysis and preprocessing to prepare a dataset for a small machine learning classifier, including date transformation, title and body length features, missing-value handling, and pandas profiling.

Learn how to deploy and version a local machine learning project to Dash Hub using Git workflows, including status checks, staging, commits, and pushes, with collaboration features.

Create a DagsHub account, initialize a new repository, and configure git remotes, then create data and output folders to upload your model and training data.

Create a project-specific Python virtual environment, install dependencies from a custom requirements file, and configure gitignore and data handling with DVC while preparing the dataset.

Deploy the model project to DagsHub via git by staging changes, committing with a descriptive message, and pushing to the remote repository while managing requirements and enabling collaboration.

Version and manage model data with DVC, and upload and document a project to the hub. Build a text-based classifier with logistic regression, including feature engineering and model evaluation metrics.

Apply production-ready ml practices by ensuring reproducible data, separating train-test splits, using cross-validation for unbalanced classes, fixing random seeds, saving the trained model, and separating training from production.

Learn to version data and models with DVC, integrate with Git, initialize DVC, track datasets and outputs, handle errors, and commit changes.

Train a new model version with feature engineering, evaluate with updated metrics, and deploy the model and data to DagsHub via DVC and Git, tracking versions and remote pushes.

Explore running multiple experiments with different hyperparameters in Dash Hub, register metrics and hyperparameters, and compare results across models using Git and DVC versioning.

Train experiments and register them in DagsHub, then select best by metrics like F1 with sorting and labels. Compare hyperparameters and results, and integrate with MLflow for versioning and deployment.

Develop a churn prediction classifier using Pycaret, apply data preprocessing, and run a local mlflow experiment with DVC integration to register the model, referencing the dataset path and notebook solution.

Develop a churn prediction model with PyCaret and MLflow, from dataset ingestion to preprocessing, training, evaluating with the area under the curve and confusion matrix, and saving the model.

Generate a remote repository for a model using DagsHub and git, configure access, initialize local git, add model files and notebook, and synchronize offline and online work while comparing solutions.

Set up the git environment and initialize a Dash Hub repository. Configure access, push changes, and register the dataset with DVC and Mlflow for notebooks.

Version data with DVC to ensure reproducible experiments. Ignore data folder in Git, download raw data, save as csv in data, and push versioned datasets to the DVC hub.

Learn to version datasets and models with DVC by initializing DVC, adding data and output folders, and pushing changes to Dash Hub via Git and DVC push.

Register models and log experiments in a shared MLflow environment on the Dash Hub server, configure access credentials, and identify top models for your data science team.

Register and compare models on a remote mlflow server via dash hub, logging experiments with environment variables, and choose the best by area under the curve and f1.