Complete MLOps Bootcamp

If you're looking for a comprehensive, hands-on, and project-based guide to learning MLOps (Machine Learning Operations), you've come to the right place.

According to an Algorithmia survey, 85% of Machine Learning projects do not reach production. In addition, the MLOps have exponentially grown in the last years. MLOPS was estimated at $23.2 billion for 2019 and is projected to reach $126 billion by 2025. Therefore, MLOps knowledge will give you numerous professional opportunities.

This course is designed to teach everything related to MLOps, from model development, model registration, and model versioning; model performance monitoring, CI/CD, cloud deployment, model serving and APIs, and web applications development to punt into production the model.

We will guide you through the MLOps skills, sharing clear explanations and valuable professional advice.

With visual training, downloadable study guides, hands-on exercises, and real-world labs, this is the only course you'll need to learn how to implement an end-to-end MLOps project. By the end of this course, not only will you have developed an entire MLOps project from the ground up, but you will also gain the knowledge and confidence to apply these same concepts to your projects.

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.
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.
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.

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

If you are ready to improve your MLOps skills, increase your job opportunities and become a data science professional, we are waiting for you.

What's inside

Learning objectives

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

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

Syllabus

Introduction to this course

How to get the most out of the course

Course material

Challenges and evolution of Machine Learning

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers a wide range of MLOps tools, including MLflow, DVC, and Pycaret, which are essential for managing the ML lifecycle from experimentation to deployment

Includes hands-on labs and exercises, providing practical experience in building and deploying machine learning models, which is crucial for career advancement

Explores deploying ML models through APIs using FastAPI and Flask, as well as web applications using Gradio, which are valuable skills for productionizing ML models

Examines model interpretability using SHAP and Evidently, which are important for understanding and auditing machine learning models in production environments

Focuses on deploying ML models in Azure Cloud, which is beneficial for those looking to work with cloud-based MLOps solutions and gain experience with a popular cloud platform

Requires installing Docker and Ubuntu, which may require learners to have access to a computer with sufficient resources and the ability to install and manage these tools

Reviews summary

Practical mlops with broad tool coverage

According to learners, this course provides a solid foundation in MLOps, particularly praising its highly practical, hands-on approach with numerous useful projects and labs. Students appreciate the broad coverage of essential MLOps tools like MLFlow, DVC, and Docker. The instructor is often described as clear and knowledgeable. However, some reviewers feel the course doesn't fully live up to its 'Zero to Hero' title, suggesting it lacks depth for intermediate or advanced users and covers topics superficially. A few note that certain sections and code examples can be outdated.

Instructor provides clear explanations.

"The instructor is very clear and easy to understand."

"The instructor is knowledgeable and passionate about the subject."

"The instructor is great at explaining complex concepts simply."

"The instructor is very good at explaining the concepts and demonstrating them."

"Really enjoyed the hands-on approach. The instructor explains complex topics well."

"I found the instructor's explanations to be consistently clear."

"The teaching style of the instructor was effective for me."

Covers a wide array of MLOps tools.

"Good overview of various MLOps tools and concepts."

"Covered a wide range of topics essential for an MLOps workflow."

"A true bootcamp. Packed with information on many different tools."

"The course covers many important topics in the MLOps space."

"This was a decent introduction to a wide range of MLOps tools."

"Covers a wide range of essential MLOps tools like Docker and MLFlow."

"I appreciated the comprehensive coverage of MLOps workflow steps."

"It touches on a lot of relevant technologies."

"Good overview of the MLOps landscape and the tools involved."

Highly practical with useful coding labs.

"The hands-on projects really helped me understand MLOps concepts."

"The projects are very practical and helped solidify my understanding."

"Highly recommend! The project-based approach is perfect for learning by doing."

"Best MLOps course I've taken. ...Loved the DVC and MLFlow labs."

"Excellent value. The project structure makes it easy to follow along and apply concepts."

"Fantastic practical examples and projects. Made understanding MLOps much easier."

"I found the hands-on labs highly practical and applicable."

"Comprehensive coverage of MLOps workflow. The projects are useful."

"I really enjoyed the hands-on approach throughout the course."

Explanations can be confusing in parts.

"Some parts were confusing. Needed to do external research to understand fully."

"I needed to do external research to understand some sections better."

"Certain sections felt confusing and required clarification from other sources."

"At times, the pacing was uneven, leading to some confusion."

"I found myself troubleshooting code that didn't match the video or was unclear."

Some sections require updates for current tools.

"Could benefit from updates to reflect current best practices and tool versions."

"I noticed that many sections feel outdated."

"Code doesn't work sometimes due to outdated libraries or tool versions."

"I was disappointed by some outdated content and broken examples."

"Some parts of the course definitely need updating."

"I found some tools covered were outdated or not the latest versions."

"Sometimes the code examples weren't perfectly aligned with the video, requiring troubleshooting."

Some feel it lacks depth for advanced users.

"Expected more depth. It's called 'Zero to Hero' but felt more like 'Zero to Intermediate'."

"Doesn't go deep enough for 'Hero' status. Covers too many tools superficially."

"It's a decent starting point, but definitely not 'Hero'. More like 'Zero to Novice'."

"Some parts felt a bit rushed, especially the cloud deployment section."

"I felt some explanations weren't deep enough for real production use."

"The course rushed through several key tools without sufficient detail."

"The 'Zero to Hero' claim seems a bit ambitious given the depth of coverage."

"It provides a good starting point but isn't enough to be truly 'Hero'."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete MLOps Bootcamp | From Zero to Hero in Python 2022 with these activities:

Review Python Fundamentals

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Strengthen your Python foundation to better understand the code examples and implement MLOps pipelines effectively.

Browse courses on Python Programming

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Review basic Python syntax and data structures.
Practice writing simple Python functions.
Work through online Python tutorials.

Brush up on Docker Basics

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Familiarize yourself with Docker concepts to easily containerize and deploy machine learning models.

Browse courses on Docker Containers

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Learn about Docker images and containers.
Practice building Dockerfiles.
Run and manage Docker containers.

Read 'Designing Machine Learning Systems'

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Gain a deeper understanding of the architectural considerations for building and deploying machine learning systems.

View Designing Machine Learning Systems: An... on Amazon

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Read the book chapter by chapter.
Take notes on key concepts and best practices.
Relate the concepts to the course materials.

Four other activities

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Show all seven activities

Implement a Simple CI/CD Pipeline

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Solidify your understanding of CI/CD by building a basic pipeline for a machine learning model.

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Set up a Git repository for your project.
Create a simple machine learning model.
Configure a CI/CD tool (e.g., Jenkins, GitLab CI) to automatically build, test, and deploy your model.

Write a Blog Post on Model Monitoring

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Deepen your knowledge of model monitoring by researching and writing a blog post on the topic.

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Research different model monitoring techniques.
Choose a specific aspect of model monitoring to focus on.
Write a clear and concise blog post explaining the concepts.

Contribute to an MLOps Open Source Project

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Gain practical experience and contribute to the MLOps community by contributing to an open-source project.

Show steps

Identify an MLOps open-source project that interests you.
Explore the project's codebase and documentation.
Contribute by fixing bugs, adding new features, or improving documentation.

Read 'MLOps Engineering at Scale'

Show steps

Further your understanding of the challenges and solutions for scaling MLOps infrastructure.

View MLOps Engineering at Scale on Amazon

Show steps

Read the book chapter by chapter.
Take notes on key concepts and best practices.
Relate the concepts to the course materials.

Career center

Learners who complete Complete MLOps Bootcamp | From Zero to Hero in Python 2022 will develop knowledge and skills that may be useful to these careers:

MLOps Engineer

An MLOps Engineer specializes in bridging the gap between model development and production deployment. This course directly aligns with the MLOps Engineer role by offering in-depth coverage of MLOps fundamentals, tools, and best practices. Learning about model versioning with MLFlow, containerization with Docker, and deployment strategies through APIs and web applications will be essential. The course's hands-on exercises and real-world labs provide practical experience needed to excel as an MLOps Engineer. The extensive treatment of the subject matter makes this course especially useful.

See salaries and explore the career path for MLOps Engineer

Machine Learning Engineer

A Machine Learning Engineer focuses on deploying and maintaining machine learning models in production. This course helps build a foundation for this role by covering MLOps fundamentals, model versioning with MLFlow, and containerization with Docker. The course emphasizes deploying models to production through APIs and web applications, skills crucial for a Machine Learning Engineer. Learning about MLOps in Azure Cloud will also be beneficial for those who want to deploy models in a cloud environment. This course may be especially useful given its focus on MLOps tools to implement an end to end project.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist analyzes data and develops machine learning models. This course helps build a foundation for this role by teaching MLOps fundamentals, model versioning, and deployment strategies. The course's exploration of AutoML and low-code MLOps with Pycaret may be especially useful as it allows the data scientist to automate model development. The course can help a Data Scientist deploy models developed with Python using APIs, web applications, and cloud platforms, thus improving their ability to put models into use.

See salaries and explore the career path for Data Scientist

AI Engineer

AI Engineers focus on developing and deploying AI-powered applications. This course provides a comprehensive understanding of MLOps, including model development, versioning, and deployment, which are vital for an AI Engineer. The course's focus on deploying ML models through APIs and web applications, as well as the use of Azure Cloud, helps build a foundation for creating scalable and production-ready AI solutions. The explanation of model interpretability, explainability, auditability, and data drift with SHAP and Evidently may also be useful.

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Cloud Engineer

Cloud Engineers manage and maintain cloud infrastructure. This course helps build a foundation for understanding how machine learning models can be deployed and managed in the cloud. The course's coverage of MLOps in Azure Cloud, including training and deploying models, may be especially useful. Learning about containerization with Docker and deploying ML models through APIs will allow a Cloud Engineer to integrate machine learning capabilities into cloud-based applications. This is a crucial skill as cloud services become increasingly integrated with AI.

See salaries and explore the career path for Cloud Engineer

Site Reliability Engineer

Site Reliability Engineers ensure the reliability and availability of systems. This course helps build a foundation for understanding how to deploy and monitor machine learning models in production, a key aspect of ensuring their reliability. The course's coverage of containerization with Docker, API deployment, and cloud deployment options, as well as model performance monitoring may be useful. The course's hands-on exercises provide practical insight into the challenges of deploying and maintaining machine learning models in a production environment.

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Platform Engineer

Platform Engineers build and maintain the infrastructure that supports software development and deployment. This course can help a Platform Engineer by providing an understanding of the MLOps lifecycle and the tools used to manage it. Learning about model versioning with MLFlow, containerization with Docker, and deployment strategies through APIs helps build a foundation for creating an efficient and scalable machine learning platform. The course’s comprehensive approach makes it a very helpful introduction.

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Solutions Architect

Solutions Architects design and implement complex IT solutions. This course may be useful for a Solutions Architect by providing a comprehensive understanding of the MLOps lifecycle and the technologies involved. The course's coverage of MLOps in Azure Cloud, API development, and web application deployment helps build a foundation for designing end-to-end machine learning solutions. The course's hands-on exercises and real-world labs provide practical insights into the challenges and requirements of MLOps.

See salaries and explore the career path for Solutions Architect

Data Architect

A Data Architect designs and manages data infrastructure. This course may be useful for the Data Architect as it provides insights into the MLOps lifecycle, including data versioning with DVC and model deployment strategies. Understanding how data is used in machine learning pipelines, as well as how models are deployed through APIs and web applications, helps a Data Architect design efficient and scalable data solutions. The hands-on exercises and real-world labs provide practical insight into the challenges and requirements of MLOps.

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Software Engineer

Software Engineers design, develop, and test software applications. This course may be useful for a Software Engineer by providing an understanding of how to integrate machine learning models into software applications. The course's coverage of API development with FastAPI and Flask, as well as web application development with Gradio and Flask, may be especially useful. The course also helps build a foundation regarding how to containerize applications with Docker and deploy them to the cloud, which are essential skills for modern software development.

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Technical Lead

A Technical Lead guides and mentors a team of engineers. This course may be useful for a Technical Lead by providing a comprehensive understanding of MLOps and the technologies involved. The course's coverage of model versioning, containerization, and deployment strategies helps build a foundation for leading machine learning projects effectively. The course's hands-on exercises and real-world labs also provide practical insights into the challenges and requirements of MLOps, making it a great resource.

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Data Analyst

A Data Analyst analyzes data to identify trends and insights. This course may be useful for a Data Analyst as it introduces the concepts of MLOps, which are increasingly relevant to data analysis workflows. The course's explanation of model interpretability, explainability, auditability, and data drift may be especially useful. While a Data Analyst may not directly deploy models, understanding the deployment process and the challenges involved helps improve their ability to generate actionable insights.

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Product Manager

Product Managers oversee the development and launch of new products. This course may be useful for a Product Manager working on machine learning-powered products. The course's coverage of MLOps fundamentals, model deployment strategies, and cloud deployment options helps build a foundation for making informed product decisions. The course can help you understand the technological challenges involved in developing and deploying machine learning models, enabling you to create more realistic and achievable product roadmaps.

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Business Intelligence Analyst

A Business Intelligence Analyst uses data to inform business decisions. This course may be useful for a Business Intelligence Analyst by providing an understanding of how machine learning models are developed and deployed. The course's coverage of MLOps fundamentals and model deployment strategies can help you better understand the capabilities and limitations of machine learning in a business context. This knowledge can then be used to improve the quality and relevance of business intelligence reports.

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Database Administrator

Database Administrators manage and maintain databases. This course can help a Database Administrator by providing an understanding of the data requirements of machine learning applications. The course's coverage of data versioning with DVC and MLOps fundamentals may be especially insightful. Understanding how data is used in machine learning pipelines helps a Database Administrator design and optimize databases for machine learning workloads leading to better support for data scientists and machine learning engineers.

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From Zero to Hero in Python 2022

What's inside

Learning objectives

Syllabus

Traffic lights

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Reviews summary

Practical mlops with broad tool coverage

Activities

Career center

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