MLOps Cert- Basics, Deployment, Vertex AI & Feature Store
What you'll learn
- MLOps- What are MLOps (Machine Learning Opeartions)?
- MLOps: Components including Continuous X & Versioning
- MLOps: Life Cycle Process ( End to End Learning Flow)
- MLOps: Model Testing & Model Packaging in PMML and ONNX
- MLOps: Workflow Decomposition & Production Environment
- MLOps: Pre- Computing Serving Patterns
- MLOps: Data, Machine Learning and Code Pipelines
- MLOps: Offline & Live Evaluation & Monitoring
- MLOPs: LinkedIn as a case example of large scale ML Deployment
Requirements
- No prior experience is needed. You will learn everything you need to know.
Description
This course introduces participants to MLOps concepts and best practices for deploying, evaluating, monitoring and operating production ML systems on both cloud and Edge. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
This course encompasses the following topics;
1. Introduction of Data, Machine Learning Model and Code with reference to MLOps.
2. MLOps vs DevOps.
3. Where and How to Deploy MLOps.
4. Components of MLOps.
5. Continuous X & Versioning in MLOps.
6. Experiment Tracking in MLOps.
7. Three Levels of MLOps.
8. How to Implement MLOps?
9. CRISP (Q)- ML Life Cycle Process.
10. Complete MLOps Toolbox.
11. ML Flow library for MLOps.
12. Tensor Flow Extended (TFX) for the deployment of MLOps.
13. PyCaret for the evaluation and deployment of MLOps.
14. Kubernetes as package manager for MLOps.
11. Google Cloud architectures for reliable and effective MLOps environments.
12. Working with AWS MLOps Services.
LAB Exercises with Solutions:
1. How to Deploy MLOps using Helm.
2. Make Changes with Helm.
3. Keep Track of Deployed Applications.
4. Share Helm Charts.
By the end of this course, you will be ready to:
Design an ML production system end-to-end: data needs, modeling strategies, and deployment requirements.
How to develop a prototype, deploy, and continuously improve a production-sized ML application.
Understand data pipelines by gathering, cleaning, and validating datasets.
Establish data lifecycle by leveraging data lineage.
Use analytics to address model fairness and mitigate bottlenecks.
Deliver deployment pipelines for model serving that require different infrastructures.
Apply best practices and progressive delivery techniques to maintain a continuously operating production system.
Who this course is for:
- Beginner students and researchers curious to know about MLOps
- Individuals looking to enter the data and AI industry.
Instructor
![Junaid Zafar](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 3.9 Instructor Rating
- 520 Reviews
- 60,815 Students
- 9 Courses
Prof. Dr. Engr. Junaid Zafar is currently working as Chairperson in Department of Electrical and Computer Engineering, Government College University, Lahore. He is also Director, Office of Research, innovation and Commercialization. He has completed his PhD in Electrical and Electronics Engineering, The University of Manchester University, UK, and BSc in Electrical Engineering from U.E.T Lahore. He is Academic visitor to the University of Cambridge, UK, MMU, UK and National University of Ireland. He remained Dual Degree programme coordinator at the Lancaster University, UK. Dr. Engr. Junaid Zafar received Roll of Honors for National Education Commission and Outstanding Teacher/ Researcher Awards from the Higher Education Commission, Pakistan. He is leading the macine learning and Artificial Intelligence centre with GC University, Lahore. He is member of Universal Association of Electronics & Computer Engineers, International Association of Computer Science & Information, and member of International Association of Engineers, IAENG Society of Artificial Intelligence, IAENG Society of Electrical Engineering, Science & Engineering Institute, IAENG Society of Imaging Engineering, Institute of Research Engineers & Doctors, and IAENG Society of Wireless Networks. He is member of editorial board in Journal of Future Technologies & Communications, Technical Programme committee, Frontiers of Information & Technologies, and Technical Programme Committee, Multi- Conference on Sciences & Technology. He is also serving as reviewer for IEEE Transactions on Microwave Theory & Techniques, IEEE Transactions on Antennas, IEEE Antenna & Wireless Propagation Letters, IEEE Transactions on Plasma Science, IEEE Transactions on Magnetics, International Journal of Electronics, and IET Antennas & Radio- wave Propagation. He has so far taught over twenty diffrent online courses based on outcome based student oriented models. He has also supervised more than 100 Masters/ MPhil thesis. He has published over 50 high impact factor publications and presented his work at several national and international renowned platforms.