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Machine Learning Engineering

MLOps Engineering

US Online

Welcome to our MLOps course, where we guide you through mastering the essential skills and tools needed for deploying machine learning models in a production environment. Gain proficiency in cloud computing, containerization, web development, model packaging, experimentation, version control, monitoring, and orchestration. Enroll now to revolutionize your approach to machine learning deployments and become proficient in MLOps. Join us on the journey to making your machine learning projects production-ready!

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Part-time
Online Live
8 weeks
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About the Course

This course encompasses the fundamental skills and tools required for proficiency in deploying machine learning models within a production environment. These skills encompass cloud computing, containerization, web development, model packaging, model experimentation, version control, model monitoring, and model orchestration.

WHAT YOU WILL LEARN

  • How to deploy model in the cloud, Containerization.
  • how to manage multi-container applications for simplifying development environments.
  • Apply techniques for packaging machine learning models for deployment.
  • Utilize deferent tools to build scalable and distributed Machine Learning models.
  • Implementing Continuous Integration/Continuous Deployment (CI/CD) and Infrastructure as Code (IaC) principles.
  • Apply Techniques for monitoring the performance of machine learning models in production.
  • Techniques and tools used for deploying of large language models in production environments

Case-based learning with real-life examples

  • Project Kickoff and Version Control
  • Setting Up CI/CD Pipelines
  • Infrastructure as Code Implementation
  • Artifact and Model Registry

WeCloudData is the perfect place to grow your career

5/5

Alex V, Alumni Review
I can already tell Indrani is an incredible teacher.  She’s very knowledgeable. Her teaching style makes the material more memorable.  She asks questions which forces me to reread over my previous notes. Instructor Yi instills a lot of confidence in his knowledgeability. You can tell from his cadence and confidence that he knows what he’s teaching very well.

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CURRICULUM

Be ready for the new economy

WeCloudData programs are designed to be project-based. We not only cover essential theories, but also teach how to apply tools and platforms that are in high demand today. Our program curriculum is also highly adaptive to the latest market trends. 

MODULE 1
Introduction Cloud Computing
Quick introduction in the cloud computing world. understanding the basics of Cloud Computing. How to deploy Machine Learning models using different AWS solutions. Like AWS EC2 and SageMaker
LEARNING OUTCOMES
  • Deploy Machine Learning Models on AWS
  • Hyperparameter tuning for Machine Learning model development and model deployment.

KEY SKILLS
  • Familiarity with key terms such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)
  • Acquire skills in deploying and managing virtual servers using AWS EC2
  • Learn how to deploy machine learning models on AWS infrastructure
Tools:
AWS EC2
AWS SageMaker
Tools:
Docker
Docker Compose
MODULE 2
Containerization
Introduction to containerization using Docker for efficient software deployment, learning how to manage multi-container applications using Docker Compose for simplifying development environments.
LEARNING OUTCOMES
  • Explore the Docker ecosystem and its role in facilitating the creation and deployment of containers.
  • Managing Dockerfile and its role in creating container images, including the layering mechanism for efficient image construction.
  • Practices deploying applications as Docker containers to ensure reliability and ease of maintenance.
  • Configure multi-container applications using Docker Compose YAML files.

KEY SKILLS
  • Grasp fundamental concepts of containerization and its advantages in software deployment like isolation, portability, and efficiency benefits
  • Acquire proficiency in using Docker, the leading containerization platform. and Familiarity with Docker commands, images, containers, and Dockerfiles
  • Develop skills in deploying software efficiently using Docker containers
  • Learn techniques for debugging and resolving container-related problems
Module 3
Model Experimentation
Experimentation with models is a crucial stage in overseeing machine learning workflows. It involves four key elements: monitoring training sessions, establishing a standardized unit for packaging and reusing machine learning, overseeing the model registry component to ensure efficient reuse, and facilitating the reproduction of machine learning experiments. In this module, MLFLow and torchserve will be utilized for the effective management of machine learning experimentation.
LEARNING OUTCOMES
  • Explore techniques and tools for monitoring metrics, logs, and visualizing training progress using MLflow Tracking.
  • Implement best practices for packaging and organizing machine learning code and dependencies using MLflow Projects.
  • Leverage MLflow Tracking to record and reproduce experiments, capturing parameters, metrics, and dependencies.
  • Deploy and manage machine learning models in production using TorchServe, ensuring scalability and reliability.

KEY SKILLS
  • Understand the importance of experimentation in the machine learning development lifecycle
  • Develop skills in monitoring and tracking training sessions for machine learning models. Learn how to use tools and techniques to analyze and optimize model training processes
  • Develop the ability to oversee and ensure the efficient reuse of machine learning models
  • Gain practical experience in utilizing TorchServe for deploying and serving PyTorch models
Tools:
MLFlow
TorchServe
Tools:
GitLab CI
GitHub Actions
Git
MLflow
DVC (Data Version Control)
Module 4
Data Version Control and Continuous Integration
This module delves into the critical aspects of implementing Continuous Integration/Continuous Deployment (CI/CD) and Infrastructure as Code (IaC) principles in the context of machine learning. Participants will gain practical insights into the significance of version control for managing machine learning data and models efficiently. The module will explore tools and best practices for seamless integration, deployment, and versioning of ML workflows, ensuring a robust and reproducible development pipeline.
LEARNING OUTCOMES
  • Automate the testing, building, and deployment of machine learning models to enhance development efficiency.
  • Explore tools and practices for defining and managing infrastructure as code for ML projects.
  • Implement version control strategies for both data and code to ensure reproducibility and traceability.
  • Configure and set up CI/CD pipelines for ML projects to automate testing, building, and deployment processes.

KEY SKILLS
  • Develop proficiency in implementing Continuous Deployment practices for machine learning workflows
  • Learn how to implement Infrastructure as Code (IaC) principles for managing and provisioning machine learning infrastructure
  • Acquire skills in using version control systems for managing machine learning data and models effectively. Understand the importance of versioning in tracking changes and ensuring reproducibility
  • Acquire skills in automating tasks related to version control, integration, deployment, and infrastructure provisioning
Module 5
Container Orchestration
This module provides a comprehensive overview of container orchestration using Kubernetes, specifically tailored for Machine Learning Operations (MLOps). Participants will gain hands-on experience with deploying, scaling, and managing machine learning models in a Kubernetes-based environment. The module explores the integration of popular ML tools such as Kubeflow and Seldon to streamline the deployment and management of machine learning models.
LEARNING OUTCOMES
  • Deploying ML Models with Kubernetes: Learn how to containerize machine learning models and deploy them on Kubernetes clusters. Participants will explore best practices for packaging and distributing ML applications as containers.
  • Scalability and Reliability: Gain insights into scaling ML workloads efficiently using Kubernetes. Understand how to handle load balancing, auto-scaling, and ensure the reliability of machine learning applications in a distributed environment.
  • Seldon for Model Deployment: Dive into Seldon, a platform for deploying, scaling, and managing machine learning models on Kubernetes. Participants will learn how to leverage Seldon for deploying and serving models with ease, ensuring consistency and reliability.

KEY SKILLS
  • Develop a strong understanding of Kubernetes fundamentals, specifically tailored for Machine Learning Operations (MLOps)
  • Acquire skills in orchestrating containers for machine learning workloads using Kubernetes
  • Gain hands-on experience in deploying machine learning models within a Kubernetes-based environment
  • Acquire problem-solving skills for identifying and resolving issues in Kubernetes-based MLOps environments
Tools:
Kubernetes
Kubeflow
Seldon
Kubernetes Security Tools
FACULTY TEAM

Learn from the best

We’ve brought together a team of highly skilled and experienced instructors to help you learn effectively. Our instructors have a passion for teaching and a wealth of real-world experiences in their respective fields, so you can be confident that you’re learning from the best.

Allan Qian
MLOps Developer | Deloitte
Indrani Gorti
Director of Platform and ML engineering, Loblaw Digital
Indrani Gorti is a polyglot with experience in data engineering, data science, and solving business problems using ML and AI. She has worked in major industries like Finance, Telecommunications, Technology, Retail, and Startups. Her work has involved end-to-end projects from data ingestion, data analysis to the deployment of models in various setups using user transaction and interaction logs. Indrani is a Director of Platform and ML engineering at Loblaw Digital. She holds a BS and MS in Computer Science.
SCHEDULE

Upcoming Start Dates

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MLOps Engineering
$3,500 USD
  • Case-based learning
  • Portfolio project mentoring
  • Flexible payment plan
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Recommended Short Courses
$3,500 USD
  • Enrich your AI experience with LLM and Computer Vision courses
  • Get alumni discount for other DE, AI, and MLE courses
  • Short courses to consider after completing this course ⇩
Upgrade to Bootcamp
$10,000 USD
  • Upgrade to the AI/MLE bootcamp and get $5,000 discount
  • Get extensive 1-1 career mentoring and job support
  • Get the flexibility to create your own bootcamp
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What our graduates are saying

5/5

Jason Lee, Alumni

Thank you so much for coordinating an awesome course. The assistant instructor was really great in exposing to us how course material is applied in production level environment. I also like the instructor’s approach of pushing us to build from fundamental to real project using PyTorch first. And then progressing to Tensorflow. Personally, I think it’s a super awesome course, but I believe you really have to dig yourself into the contents and dedicate many head banging hours. But course material is very practical both in theory and application. Thanks much!

5/5

Waqas Khan, Senior Data Scientist

All the Instructors and TA’s are very knowledgeable and are always available for any clarification or support. There are dedicated TA office hours daily to assist students if there are any roadblocks in their assignments. Students are generally from very different backgrounds and experience levels but the Instructors and TA’s do a great job to make sure that everyone is following along and is on the same page.

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FAQ

Frequently asked questions about the bootcamp
To become successful in this course the learner will need to have solid python programming skills and experience with machine learning. Understanding of neural network and deep learning basics is helpful but not a must-have. This course covers the engineering and operations sides of machine learning so knowledge and experience of cloud, Linux, containerization, and software development basics will be helpful too. If you don’t meet the pre-requisites, we recommend you go through the MLOps prep material after enrolling in the course. You will still be able to do the course without knowing cloud, Linux, and software development since we also cover them in the course, but the learning curve will be steep due to the amount of tools and concepts you need to learn.
Software engineering knowledge is not necessary but definitely helpful. Many students have successfully completed the course without a CS background. We highly recommend learners pick up some basic CS knowledge before attending this course.
No. The MLOps skills covered in this course can be used for any ML systems. If the learner wants to deploy LLM and deep learning models, then knowledge of deep learning and LLM will be required.
This course is very practical and focuses on implementation. Therefore we don’t spend too much time on explaining the theory of computer system, cloud, and software engineering. MLOps is relatively new and it evolves fast. Most companies want to hire MLOps engineers who know how to apply industry tools to solve real challenges. Since building ML systems is a very practical topic, we will focus on the HOWs and teach students to build things that work as close to best practices as possible.
We don’t offer extensive job support in our short courses. If you need career mentorship and help, you have two options. You can either enrol in the career mentorship program with alumni discount or consider joining our machine learning bootcamp. You will be able to get a $5000 scholarship for the Bootcamp and fully take advantage of the career support.
Depending on your existing skill sets and experience with machine learning, learners usually spend 15 to 20 hours each week (including the lectures and labs)
Yes, during the regular weeks we have office hours and labs sheets students get to follow labs and ask questions. During the project weeks, students will join the project mentoring sessions to interact the project mentors.
The project mentors will teach students the blueprint of building end to end MLOps project. Students are encouraged to have their own ideas and project use cases. Project can be fairly advanced depending on the time commitment.
Labs are designed to help learners practice what’s taught during the lectures. Instructors will be hands-on demos and cover new topics. Any questions regarding the lectures, demos will be addressed in the lab sessions. Students will be given additional lab exercises and self-paced exercises to work on. Lab instructors will provide live solution walkthroughs and students are encouraged to follow along and ask lots of questions. If the students have additional questions outside of the class, we encourage you to reach out to teaching assistants on Slack or attend the office hours.
This course is designed to be very hands-on. It’s impossible to become good at MLOps without actually trying to deploy ML solutions. If you prefer a more academic environment, we recommend you consider a Master’s program. If you want to gain practical experience and build portfolio projects, this is the perfect course.
We cover popular tools in each categories. For cloud we choose to go with AWS. Docker and kubernetes are covered in the containerization modules. For workflow orchestration we cover airflow. For managed ML services we cover AWS SageMaker. For version control we teach students how to work with git and DVC. For monitoring we cover Grafana and other open source tools. As you can tell, there’re a lot of tools we teach in this course. However, our philosophy is that tools can change but being able to complete an end to end project by putting some of these tools together and also knowing the pros and cons of different solutions are the most important things to teach our learners.
Yes. We’ve adapted our curriculum to introduce MLOps for LLMs. In the LLM course we cover quite a bit LLMOps and in this course we will go deeper and cover how to scaling things and teaches the hardware and accelerators.
There are two types of projects: personal projects (also called capstone or portfolio projects) and real client projects. All students in the course will need to complete the capstone project. The real client project is a different training and career service offered at WeCloudData via our partner Beamdata. Learners will become a trainee and receive project-based training. Learners will be assigned to a real project team to work with clients and get mentored and trained by our project managers and project leads. It’s a great learning opportunity and also allow the students to gain real experience to stand out in the job market. You can talk to our learning advisors to find more details.
Yes. Lots of labs and exercises. Students will have access to quizzes so they can test their knowledge on certain topics.
Yes, payment plan is available for this course. You can fill out the form on this course page to access the course package page. It has funding related information. Our learning advisor can also help you with your questions.
Yes, absolutely. All short courses are eligible for bundling and scholarship. When you purchase multiple courses you can get good discounts. Please reach out to our learning advisors by filling out the inquiry form.
No. This AI course is developed for anyone with solid python programming experience. You don’t need to be a SDE or developer. Many students in this program are from data science and analytics background.
Yes. Scholarship is available for students who meet the requirements. A scholarship test needs to be completed and the learner needs have a 20-minute live assessment with the program manager. Alumni who have completed courses that meet the pre-requisites will also be eligible for scholarships.
This is a short course that’s part of the diploma program which is only available to learners in Canada, and you will need to complete 3 courses. Please contact our learning advisor for more details. For US and international learners, you will get a certificate by completing the course.
– Courses in the AI/ML program usually have a good mix of learners from various background. Most students already have python programming and data science experiences. It doesn’t have to be related work experience though. We have learners who has completed self-paced ML courses or other data science bootcamps as well. Typically learners come from 1. Data scientist who want to harness the MLE and MLOps skills. 2. Software engineers who have machine learning knowledge 3. DevOps engineers who are interested in operating ML systems 4. New graduates from computer science or computer engineering who have taken ML courses in school or who can complete the pre-course materials on their own 5. Career switchers who take this course as part of the ML Engineering Bootcamp. 6. Non-tech background learners who have completed WeCloudData’s AI short courses as a pre-requisites. 7. PhD graduates who want to fast track their job search and meet the technical pre-requisites
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View our MLOps Engineering course package
View our MLOps Engineering course package