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Mastering MLOps: Complete Course on ML Operations Course Overview

Mastering MLOps: Complete Course on ML Operations Course Overview

The "Mastering MLOps: Complete Course on ML Operations" is an extensive machine learning operations course designed to equip learners with the skills necessary to manage and operationalize machine learning models effectively. Throughout the course, participants will delve into the fundamentals of machine learning operations and be introduced to a suite of tools and practices that streamline the entire ML lifecycle. From model and data versioning using MLFlow and DVC to deploying ML models via APIs and web applications, the course covers a broad range of topics. Learners will also get hands-on experience with Auto-ML, containerization with Docker, and CI/CD processes using GitHub Actions. By the end of the course, they will have completed a full MLOps project, which will solidify their understanding and prepare them to tackle real-world machine learning operational challenges.

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Course Prerequisites

To ensure that you can fully benefit from the Mastering MLOps: Complete Course on ML Operations, we recommend that you have the following minimum prerequisites:


  • Basic understanding of machine learning concepts and workflows
  • Familiarity with Python programming, as it is commonly used in machine learning projects
  • Experience with Git version control for managing and sharing code
  • Some exposure to data handling and preprocessing techniques
  • Knowledge of machine learning frameworks such as scikit-learn or TensorFlow
  • An understanding of basic command-line interface (CLI) operations

Please note that while these prerequisites are intended to set you up for success, we have designed our course materials to be as inclusive as possible. Our instructors are committed to helping all students, regardless of their starting skill level, to grasp the concepts and practices of MLOps.


Target Audience for Mastering MLOps: Complete Course on ML Operations

Mastering MLOps: Complete Course on ML Operations is designed for professionals seeking to streamline ML workflows and enhance model deployment and management.


  • Machine Learning Engineers
  • Data Scientists
  • DevOps Engineers
  • AI/ML Researchers
  • IT Professionals with a background in data and machine learning
  • Software Developers interested in MLOps
  • Technical Project Managers overseeing ML projects
  • Data Engineers looking to specialize in machine learning pipelines
  • ML Product Managers
  • Cloud Engineers focusing on ML deployment
  • Technical Leads and Architects designing ML systems
  • Operations Analysts involved in ML processes
  • Professionals aiming for a career transition into MLOps
  • Academics and students in computer science with a focus on machine learning


Learning Objectives - What you will Learn in this Mastering MLOps: Complete Course on ML Operations?

Introduction to Course Learning Outcomes

Gain mastery over MLOps with comprehensive training on tools, practices, and automation for efficient ML model management, deployment, and continuous integration and delivery.

Learning Objectives and Outcomes

  • Understand the significance of MLOps and how it solves challenges faced in traditional ML model management.
  • Apply MLOps tools and practices to manage end-to-end ML projects, ensuring efficient versioning, collaboration, and deployment.
  • Master the use of MLFlow for model versioning, lifecycle management, and integration with other MLOps tools.
  • Learn data versioning techniques with DVC, including integration with different storage options.
  • Create and manage a shared ML repository using tools like DagsHub, DVC, Git, and MLFlow for collaborative model development.
  • Automate the ML model development process using Auto-ML tools like Pycaret and streamline model lifecycle phases.
  • Gain insights into model interpretability, explainability, and auditability, with hands-on experience in SHAP and Evidently.
  • Package and distribute machine learning applications efficiently using Docker for a containerized ML workflow.
  • Deploy ML models through API development with FastAPI and Flask, and understand the deployment process on Azure Cloud.
  • Develop web applications with embedded ML models using Gradio, and deploy production-ready apps using Docker.
  • Integrate BentoML with MLFlow for automated ML service development and learn deployment strategies using Docker.
  • Implement Continuous Integration and Delivery (CI/CD) in MLOps with GitHub Actions and Continuous Machine Learning (CML).
  • Monitor and evaluate machine learning models in production using tools like Evidently AI and Deepchecks for quality assurance.
  • Execute a capstone MLOps project that encompasses model development, versioning, API building, web app creation, and CI/CD implementation.

Technical Topic Explanation

CI/CD processes

CI/CD stands for Continuous Integration and Continuous Deployment, which are key processes in software development. Continuous Integration involves merging all developers' working copies to a shared mainline several times a day, ensuring that code changes are automatically tested and reported. This process decreases integration problems, allowing teams to develop software more rapidly. Continuous Deployment automates the delivery of applications to selected infrastructure environments. This sequence enables developers to release robust software at a faster pace by automating testing and deployment, which increases productivity and reduces the risk of human errors in manual processes.

MLFlow

MLFlow is a platform designed to manage the complete machine learning lifecycle, including experimentation, reproducibility, and deployment. It helps teams track and share experiments, package code into reproducible runs, and deploy models. MLFlow integrates with the broader MLOps framework to streamline machine learning operations (MLOps), enhancing collaboration between data scientists and operations teams. This makes it easier to bring machine learning projects from development to production swiftly and efficiently, ultimately reducing time and improving outcomes in machine learning projects.

Model and data versioning

Model and data versioning in machine learning operations (MLOps) involve tracking and managing changes to code, models, and datasets used in machine learning projects. This allows teams to roll back to previous versions when needed, ensuring reproducibility and consistency in ML workflows. This practice is fundamental in ML operations, helping teams efficiently update and maintain their machine learning systems while preventing conflicts or loss of valuable data. It is a critical skill taught in advanced machine learning operations courses, aimed at streamlining and optimizing the lifecycle of machine learning models.

DVC

DVC, or Data Version Control, is a tool that helps manage data and model versions in machine learning projects, facilitating better tracking and collaboration. Key in ML Ops (Machine Learning Operations), DVC allows teams to handle large data sets and manage changes systematically, ensuring that all team members are working with the latest versions. It integrates into existing data pipelines and supports reproducibility, which is critical for efficient machine learning operations. DVC bridges the gap between code version control and data management, making it an essential part of any machine learning operations course or implementation.

Deploying ML models via APIs

Deploying ML models via APIs involves integrating machine learning capabilities into applications by making them accessible over a network. Essentially, once a machine learning model is trained and ready, it's hosted on a server. An API (Application Programming Interface) is created to enable communication between the model and other software. This setup allows external applications to send data to the model and receive predictions in real time. This process is part of Machine Learning Operations (MLOps), which is crucial for the efficient and effective deployment and scaling of ML technologies in production environments.

Auto-ML

Auto-ML, or automated machine learning, simplifies the process of applying machine learning to real-world problems. It automates the time-consuming, iterative tasks of machine learning model development, including data preprocessing, feature selection, model selection, and tuning. Auto-ML allows developers and business analysts with limited machine learning expertise to build models more efficiently, making machine learning more accessible and accelerating the deployment of ML solutions. This tool is crucial in the context of MLOps (machine learning operations), where it enhances the operational aspect of deploying, monitoring, and maintaining ML models in production environments.

Containerization

Containerization is a technology that packages software code and all its dependencies into a 'container' that can run uniformly and consistently on any infrastructure. Similar to how shipping containers allow goods to be transported efficiently by ships, trucks, and trains, software containers help developers and IT professionals deploy and manage applications more easily across different environments. This approach is crucial for improved scalability, efficiency, and security in software deployment, aiding in practices like MLOps (Machine Learning Operations), which streamline the development, deployment, and maintenance of machine learning models.

Docker

Docker is a tool designed to make it easier to create, deploy, and run applications by using containers. Containers allow a developer to package up an application with all the parts it needs, such as libraries and other dependencies, and ship it all out as one package. This ensures that the application will run on any other Linux machine regardless of any customized settings that machine might have that could differ from the machine used for writing and testing the code. This streamlines the process of developing, testing, and running software, making it a popular choice for developers, especially in machine learning operations (MLOps).

GitHub Actions

GitHub Actions is a powerful automation tool that allows developers to automate their workflows in software development directly within GitHub. It enables you to create, test, and deploy code right from your GitHub repository, streamlining machine learning operations (MLOps) by building, testing, and deploying models seamlessly. With GitHub Actions, you can automate your workflow from idea to production without switching tools, facilitating continuous integration and continuous delivery (CI/CD) processes. This functionality is particularly beneficial for teams practicing ML ops, as it helps to accelerate and optimize the lifecycle of machine learning applications.

Target Audience for Mastering MLOps: Complete Course on ML Operations

Mastering MLOps: Complete Course on ML Operations is designed for professionals seeking to streamline ML workflows and enhance model deployment and management.


  • Machine Learning Engineers
  • Data Scientists
  • DevOps Engineers
  • AI/ML Researchers
  • IT Professionals with a background in data and machine learning
  • Software Developers interested in MLOps
  • Technical Project Managers overseeing ML projects
  • Data Engineers looking to specialize in machine learning pipelines
  • ML Product Managers
  • Cloud Engineers focusing on ML deployment
  • Technical Leads and Architects designing ML systems
  • Operations Analysts involved in ML processes
  • Professionals aiming for a career transition into MLOps
  • Academics and students in computer science with a focus on machine learning


Learning Objectives - What you will Learn in this Mastering MLOps: Complete Course on ML Operations?

Introduction to Course Learning Outcomes

Gain mastery over MLOps with comprehensive training on tools, practices, and automation for efficient ML model management, deployment, and continuous integration and delivery.

Learning Objectives and Outcomes

  • Understand the significance of MLOps and how it solves challenges faced in traditional ML model management.
  • Apply MLOps tools and practices to manage end-to-end ML projects, ensuring efficient versioning, collaboration, and deployment.
  • Master the use of MLFlow for model versioning, lifecycle management, and integration with other MLOps tools.
  • Learn data versioning techniques with DVC, including integration with different storage options.
  • Create and manage a shared ML repository using tools like DagsHub, DVC, Git, and MLFlow for collaborative model development.
  • Automate the ML model development process using Auto-ML tools like Pycaret and streamline model lifecycle phases.
  • Gain insights into model interpretability, explainability, and auditability, with hands-on experience in SHAP and Evidently.
  • Package and distribute machine learning applications efficiently using Docker for a containerized ML workflow.
  • Deploy ML models through API development with FastAPI and Flask, and understand the deployment process on Azure Cloud.
  • Develop web applications with embedded ML models using Gradio, and deploy production-ready apps using Docker.
  • Integrate BentoML with MLFlow for automated ML service development and learn deployment strategies using Docker.
  • Implement Continuous Integration and Delivery (CI/CD) in MLOps with GitHub Actions and Continuous Machine Learning (CML).
  • Monitor and evaluate machine learning models in production using tools like Evidently AI and Deepchecks for quality assurance.
  • Execute a capstone MLOps project that encompasses model development, versioning, API building, web app creation, and CI/CD implementation.