Continuous integration and continuous deployment (CI/CD) are crucial in machine learning operations (MLOps) as they automate the integration of ML models into software development. Continuous machine learning (CML) refers to an ML model's ability to learn continuously from a stream of data. In this course, you will build a complete Data Version Control (DVC) machine learning pipeline in preparation for continuous machine learning. You will modularize your machine learning workflow using DVC pipelines, configure DVC remote storage on Google Drive, and set up authentication for DVC to access Google Drive. Next, you will configure CI/CD through CML and use the open-source CML framework to implement CI/CD within your machine learning project. Finally, you will see how for every git push to your remote repository, a CI/CD pipeline will execute your experiment and generate a CML report with model metrics for every GitHub commit. At the end of this course, you will be able to use DVC's integration with CML to build CI/CD pipelines.

Data Version Control (DVC) pipelines empower data practitioners to define, automate, and version complex data processing workflows. By streamlining end-to-end processes, pipelines enhance collaboration, maintain data lineage, and enable efficient experimentation and deployment in data-centric projects. In this course, you will discover the intricacies of machine learning (ML) pipelines within DVC. You will set up a pipeline with data cleaning, training, and evaluation stages and run these stages using the dvc repro command. Then you will use DVC to track the status of the pipeline with the help of the dvc.lock file. Next, you will run and track a DVC pipeline as an experiment using DVCLive and view metrics and artifacts of your pipeline in the Iterative Studio user interface. Finally, you will queue DVC experiments so they can be run later, either in parallel or sequentially. This course gives you an in-depth understanding of DVC pipelines, equipping you to seamlessly orchestrate and manage your ML workloads.

Data Version Control (DVC) is a technology that simplifies and enhances data versioning and management. It provides Git-like capabilities to track, share, and reproduce changes in data while optimizing storage and facilitating collaboration in data-centric projects. In this course, you will discover how DVC simplifies the intricate components of ML projects - code, configuration files, data, and model artifacts. Next, you will embark on hands-on DVC exploration by installing Git locally and establishing a remote repository on GitHub. Then you will install DVC, set up a local repository, configure DVC remote storage, and add and track data using DVC. Finally, you will create Python-based machine learning (ML) models and track them with DVC and Git integration. You will create metafiles pointing to DVC-stored data and artifacts and commit these files to GitHub, tagging different model and data versions. Through Git tags, you will access specific model iterations for your work. This course will empower you with theoretical insights and practical proficiency in employing DVC and Git.

Data Version Control (DVC) offers robust support for deep learning models by effectively managing large model files and their dependencies, allowing versioned tracking of complex architectures. This ensures reproducibility in training, evaluation, and deployment pipelines, even in deep learning projects. In this course, you will discover how to track deep learning models through DVC. Using PyTorch Lightning, you will construct a convolutional neural network (CNN) for image classification. Then you will use DVCLive to log and visualize sample images and use the DVCLiveLogger to monitor model metrics in real time via Iterative Studio. Next, you will undertake deep learning model training with TensorFlow. You will set up a CNN for image classification and train your model while leveraging DVCLive to record and display training-related metrics. Finally, you will use the DVCLiveCallback to dynamically visualize metrics during training. This course will equip you with the expertise to effectively build and track deep learning models within DVC's ecosystem.

Data Version Control (DVC) enables model tracking by versioning machine learning (ML) models alongside their associated data and code, allowing seamless reproducibility of model training and evaluation across different environments and collaborators. MLEM is a tool that easily packages, deploys, and serves ML models. In this course, you will compare ML model performance using DVC. You will create multiple churn-prediction classification models employing various algorithms, including logistic regression, random forests, and XGBoost and you will track metrics, parameters, and artifacts. Then you will leverage the Iterative Studio interface to visually contrast models' metrics and performance graphs and perform comparisons using the command line. Next, you will unlock the potential of hyperparameter tuning with the Optuna framework. You will tune your ML model, compare the outcomes of hyperparameter-tuned models, and select the optimal model for deployment. Finally, you will codify and move your ML model through REST endpoints and Docker-hosted container deployment, solidifying your understanding of serving MLEM models for predictions. This course will equip you with comprehensive knowledge of codifying and serving ML models.

Data Version Control (DVC) pipelines enable the construction of end-to-end data processing workflows, connecting data and code stages while maintaining version control. DVCLive is a Python library for logging machine learning metrics in simple file formats and is fully compatible with DVC. In this course, you will configure and employ pipelines in DVC and modularize and coordinate each step, while leveraging the dvc.yaml file for stage management and the dvc.lock file for project consistency. Next, you will dive into practical DVC utilization with Jupyter notebooks. You will track model parameters, metrics, and artifacts via Python code's log statements using DVCLive. Then you will explore the user-friendly Iterative Studio interface. Finally, you will leverage DVCLive for comprehensive model experimentation. By pushing experiment files to DVC and employing Git branches, you will manage parallel developments. You will pull requests to streamline merging experiment branches and register model artifacts with the Iterative Studio registry. This course will equip you with the foundational knowledge of DVC and enable you to automate the tracking of model metrics and parameters with DVCLive.