Mastering MLOps: Complete course for ML Operations
Data Bootcamp
10:03:53
Description
Advanced hands-on bootcamp of MLOps with MLFlow, Scikit-learn, CI/CD, Azure, FastAPI, Gradio, SHAP, Docker, DVC, Flask..
What You'll Learn?
- MLOps fundamentals
- MLOps toolbox
- Model versioning with MLFlow
- Data versioning with DVC
- Auto-ML and Low-code MLOps
- Model Explainability, Auditability, and Interpretable machine learning
- Containerized Machine Learning WorkFlow With Docker
- Deploying ML in Production through APIS
- Deploying ML in Production through web applications
- MLOps in Azure Cloud
Who is this for?
What You Need to Know?
More details
DescriptionAre you interested in leveraging the power of Machine Learning (ML) to automate and optimize your business operations, but struggling with the complexity and challenges of deploying and managing ML models at scale? Look no further than this comprehensive MLOps course on Udemy.
In this course, you'll learn how to apply DevOps and DataOps principles to the entire ML lifecycle, from designing and developing ML models to deploying and monitoring them in production. You'll gain hands-on experience with a wide range of MLOps tools and techniques, including Docker, Deepchecks, MLFlow, DVC, and DagsHub, and learn how to build scalable and reproducible ML pipelines.
The course is divided into diferent sections, covering all aspects of the MLOps lifecycle in detail.
What does the course include?
MLOps fundamentals. We will learn about the Basic Concepts and Fundamentals of MLOps. We will look at traditional ML model management challenges and how MLOps addresses those problems to offer solutions.
MLOps toolbox. We will learn how to apply MLOps tools to implement an end-to-end project.
Model versioning with MLFlow. We will learn to version and register machine learning models with MLFlow. MLflow is an open source platform for managing the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.
Data versioning with DVC. Data Version Control (DVC) lets you capture the versions of your data and models in Git commits, while storing them on-premises or in cloud storage. It also provides a mechanism to switch between these different data contents.
Create a shared ML repository with DagsHub, DVC, Git and MLFlow. Use DagsHub, DVC, Git and MLFlow to version and registry your ML models.
Auto-ML and Low-code MLOps. We will learn to automate the development of machine learning models with Auto-Ml and Low-code libraries such as Pycaret. Pycaret automates much of the MLOps cycle, including model versioning, training, evaluation, and deployment.
Explainability, Auditability, and Interpretable machine learning. Learn about model interpretability, explainability, auditability, and data drift with SHAP and Evidently.
Containerized Machine Learning WorkFlow With Docker. Docker is one of the most used tools to package the code and dependencies of our application and distribute it efficiently. We will learn how to use Docker to package our Machine Learning applications.
Deploying ML in Production through APIS. We will learn about deploying models to production through API development with FastAPI and Flask. We will also learn to deploy those APIs in the Azure Cloud using Azure containers.
Deploying ML in Production through web applications. We will learn to develop web applications with embedded machine learning models using Gradio. We will also learn how to develop an ML application with Flask and HTML, distribute it via a Docker container, and deploy it to production in Azure.
BentoML for automated development of ML services. You will learn about BentoML, including introduction to BentoML, generating an ML service with BentoML, putting the service into production with BentoML and Docker, integrating BentoML and MLflow, and comparison of tools for developing ML services.
MLOps in Azure Cloud. Finally, we will learn about the development and deployment of models in the Cloud, specifically in Azure. We will learn how to train models on Azure, put them into production, and then consume those models.
Deploying ML services in Heroku. Including fundamentals of Heroku and a practical lab on deploying an ML service in Heroku.
Continuous integration and delivery (CI/CD) with GitHub Actions and CML. You will learn about GitHub Actions and CML, including introduction to GitHub Actions, practical lab of GitHub Actions, Continuous Machine Learning (CML), and practical lab of applying GitHub Actions and CML to MLOps.
Model Monitoring with Evidently AI. You will learn about model and service monitoring using Evidently AI and how to use it to monitor a model in production, identify data drift, and evaluate the model quality.
Model Monitoring with Deepchecks. You will learn about the components of Deepchecks, including checks, conditions, and suites, and get hands-on experience using Data Integrity Suite, Train Test Validation Suite, Model Evaluation Suite, and Custom Performance Suite.
Complete MLOps Project. You will work on a complete MLOps project from start to finish. This includes developing an ML model, validating code and pre-processing, versioning the project with MLFlow and DVC, sharing the repository with DagsHub and MLFlow, developing an API with BentoML, creating an app with Streamlit, and implementing a CI/CD workflow using GitHub Actions for data validation, application testing, and automated deployment to Heroku.
Join today and get instant and lifetime access to:
• MLOps Training Guide (PDF e-book)
• Downloadable files, codes, and resources
• Laboratories applied to use cases
• Practical exercises and quizzes
• Resources such as Cheatsheets
• 1 to 1 expert support
• Course question and answer forum
• 30 days money back guarantee
Whether you're a data scientist, machine learning engineer, or DevOps professional, this course will equip you with the skills and knowledge you need to implement MLOps in your organization and take your ML projects to the next level. Sign up now and start your journey to becoming an MLOps expert!
Who this course is for:
- Machine Learning engineers and Data Scientists interested in MLOps
- Machine Learning professionals who want to deploy models to production
- Anyone interested in developing APIs in FastAPI or Flask
- Anyone who wants to learn Docker, Azure, DVC o MLFlow
Are you interested in leveraging the power of Machine Learning (ML) to automate and optimize your business operations, but struggling with the complexity and challenges of deploying and managing ML models at scale? Look no further than this comprehensive MLOps course on Udemy.
In this course, you'll learn how to apply DevOps and DataOps principles to the entire ML lifecycle, from designing and developing ML models to deploying and monitoring them in production. You'll gain hands-on experience with a wide range of MLOps tools and techniques, including Docker, Deepchecks, MLFlow, DVC, and DagsHub, and learn how to build scalable and reproducible ML pipelines.
The course is divided into diferent sections, covering all aspects of the MLOps lifecycle in detail.
What does the course include?
MLOps fundamentals. We will learn about the Basic Concepts and Fundamentals of MLOps. We will look at traditional ML model management challenges and how MLOps addresses those problems to offer solutions.
MLOps toolbox. We will learn how to apply MLOps tools to implement an end-to-end project.
Model versioning with MLFlow. We will learn to version and register machine learning models with MLFlow. MLflow is an open source platform for managing the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.
Data versioning with DVC. Data Version Control (DVC) lets you capture the versions of your data and models in Git commits, while storing them on-premises or in cloud storage. It also provides a mechanism to switch between these different data contents.
Create a shared ML repository with DagsHub, DVC, Git and MLFlow. Use DagsHub, DVC, Git and MLFlow to version and registry your ML models.
Auto-ML and Low-code MLOps. We will learn to automate the development of machine learning models with Auto-Ml and Low-code libraries such as Pycaret. Pycaret automates much of the MLOps cycle, including model versioning, training, evaluation, and deployment.
Explainability, Auditability, and Interpretable machine learning. Learn about model interpretability, explainability, auditability, and data drift with SHAP and Evidently.
Containerized Machine Learning WorkFlow With Docker. Docker is one of the most used tools to package the code and dependencies of our application and distribute it efficiently. We will learn how to use Docker to package our Machine Learning applications.
Deploying ML in Production through APIS. We will learn about deploying models to production through API development with FastAPI and Flask. We will also learn to deploy those APIs in the Azure Cloud using Azure containers.
Deploying ML in Production through web applications. We will learn to develop web applications with embedded machine learning models using Gradio. We will also learn how to develop an ML application with Flask and HTML, distribute it via a Docker container, and deploy it to production in Azure.
BentoML for automated development of ML services. You will learn about BentoML, including introduction to BentoML, generating an ML service with BentoML, putting the service into production with BentoML and Docker, integrating BentoML and MLflow, and comparison of tools for developing ML services.
MLOps in Azure Cloud. Finally, we will learn about the development and deployment of models in the Cloud, specifically in Azure. We will learn how to train models on Azure, put them into production, and then consume those models.
Deploying ML services in Heroku. Including fundamentals of Heroku and a practical lab on deploying an ML service in Heroku.
Continuous integration and delivery (CI/CD) with GitHub Actions and CML. You will learn about GitHub Actions and CML, including introduction to GitHub Actions, practical lab of GitHub Actions, Continuous Machine Learning (CML), and practical lab of applying GitHub Actions and CML to MLOps.
Model Monitoring with Evidently AI. You will learn about model and service monitoring using Evidently AI and how to use it to monitor a model in production, identify data drift, and evaluate the model quality.
Model Monitoring with Deepchecks. You will learn about the components of Deepchecks, including checks, conditions, and suites, and get hands-on experience using Data Integrity Suite, Train Test Validation Suite, Model Evaluation Suite, and Custom Performance Suite.
Complete MLOps Project. You will work on a complete MLOps project from start to finish. This includes developing an ML model, validating code and pre-processing, versioning the project with MLFlow and DVC, sharing the repository with DagsHub and MLFlow, developing an API with BentoML, creating an app with Streamlit, and implementing a CI/CD workflow using GitHub Actions for data validation, application testing, and automated deployment to Heroku.
Join today and get instant and lifetime access to:
• MLOps Training Guide (PDF e-book)
• Downloadable files, codes, and resources
• Laboratories applied to use cases
• Practical exercises and quizzes
• Resources such as Cheatsheets
• 1 to 1 expert support
• Course question and answer forum
• 30 days money back guarantee
Whether you're a data scientist, machine learning engineer, or DevOps professional, this course will equip you with the skills and knowledge you need to implement MLOps in your organization and take your ML projects to the next level. Sign up now and start your journey to becoming an MLOps expert!
Who this course is for:
- Machine Learning engineers and Data Scientists interested in MLOps
- Machine Learning professionals who want to deploy models to production
- Anyone interested in developing APIs in FastAPI or Flask
- Anyone who wants to learn Docker, Azure, DVC o MLFlow
User Reviews
Rating
Data Bootcamp
Instructor's Courses
Udemy
View courses Udemy- language english
- Training sessions 119
- duration 10:03:53
- Release Date 2023/06/16
