Registration for EMLO 3.0
This is a registration form for our third Extensive ML Operations Course.

๐Ÿค– Introducing EMLO 3.0 (Extensive MLOps), a cutting-edge course for mastering the art of managing and deploying machine learning models at scale. This course is the result of extensive research and development, and its curriculum covers everything from model development to deployment, with a particular emphasis on the deployment aspect of MLOps.

๐Ÿ”ฌWith EMLO, you'll dive deep into the cutting-edge world of MLOps, exploring advanced techniques and tools for developing and deploying models in production environments. From Docker and PyTorch Lightning to AWS and Kubernetes, this course covers everything you need to know to excel in the fast-paced world of machine learning.

๐Ÿ—“๏ธ Registration for EMLO3.0 is now open and will close on May 15th. The course will commence on May 20th, with classes held every Saturday at 9:00 AM.

๐Ÿš€ EMLO is a comprehensive course that covers all aspects of MLOps, from model deployment to CI/CD pipeline, with a focus on the most advanced techniques and technologies. You'll learn how to create Docker containers, use PyTorch Lightning for high-performance deep learning, manage data and models with Data Version Control (DVC), and much more.. Each session includes hands-on exercises designed to give students practical experience with the tools and technologies covered in the course. These exercises are accessible through our new Learning Management System (LMS), which provides students with a seamless learning experience.

๐Ÿ’ป But that's not all! EMLO 3.0 takes things to the next level with a special focus on the deployment of Stable Diffusion and LLMs (Language Model Models), two of the most advanced machine learning models currently in use. You'll learn how to fine-tune and deploy these models!. And if that's not enough, you'll also learn how to optimize the performance of your models using techniques like INT8 Quantization, Triton Server, and TensorRT.

๐ŸŽ Join the EMLO 3.0 community today and embark on a journey that will take you to the forefront of machine learning deployment. Get ready to master the art of managing and deploying machine learning models at scale, and become a true MLOps expert!
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Course Structure
The course is divided into 15 Sessions, and would run for 15 weeks including the Capstone Project Preparation/Discussions. Here is the final course structure:

Session 1: Introduction to MLOps; Best practices and tools for managing, deploying, and maintaining ML models.

Session 2: Docker - I; Creating Docker containers from scratch and understanding core concepts.

Session 3: Docker - II; Introduction to Docker Compose for deploying ML applications.

Session 4: PyTorch Lightning and Project Setup; High-performance training and deployment using PyTorch Lightning.

Session 5: Data Version Control (DVC); Managing ML data and models using DVC.

Session 6: Experiment Tracking; Using Tensorboard, MLFlow, and Hydra for experiment tracking and configuration.

Session 7: HyperParameter Optimization; Techniques using Optuna and Bayesian Optimization.

Session 8: Deployment for Demos - Gradio; Creating and sharing ML model demos using Gradio.

Session 9: AWS CrashCourse; Covering EC2, S3, ECS, ECR, and Fargate for ML model deployment.

Session 10: Model Deployment with FastAPI; Deploying ML models using FastAPI.

Session 11: Deploying CLIP; CLIP-as-a-Service with caching on Redis and gRPC endpoints.

Session 12: Model Deployment on Serverless; Introduction to AWS Lambda and Google Cloud Functions.

Session 13: Model Deployment with TorchServe; Deploying ML models using TorchServe.

Session 14: Model Runtime in Browser; Running ML models in the browser.

Session 15: Model Explainability; XAI techniques such as SHAP and LIME.

Session 16: Kubernetes - I; Introduction to Kubernetes and its key concepts.

Session 17: Kubernetes - II; Monitoring and configuring Kubernetes clusters for ML workloads.

Session 18: Deploying Models on a Cluster; Using KServe for deploying ML models on Kubernetes.

Session 19: CI/CD Pipeline - I; Preprocessing, training, and model packaging in CI/CD pipelines.

Session 20: CI/CD Pipeline - II; Model registration and deployment for staging and production.

Session 21: CI/CD Pipeline - III; Connecting the pipeline to a code repository for auto-training and deployment.

Session 22: Auto Scaling Deployment and Stress Testing; Techniques for auto-scaling and stress testing.

Session 23: Model Monitoring and Alerting; Using Grafana and Prometheus for monitoring and alerting.

Session 24: Fine Tuning and Deploying Stable Diffusion; Using DreamBooth for fine-tuning and deployment.

Session 25: Model Performance Optimization; Techniques such as FP16 CUDA_HALF, INT8 Quantization, CUDA AMP, Triton Server, and TensorRT.

Session 26: Fine Tuning and Deploying LLMs; Using FSDP, PEFT, and LoRA for fine-tuning and deployment.

Session 27: Deploying Chat with LLM; Deploying a chat instruction-tuned LLM for conversation.

Session 28: Connecting LLMs with External Data; Augmenting LLMs with external data using in-context learning and data indexing.

Session 29: Capstone - I; Develop and deploy an end-to-end MLOps pipeline as a final project.

Session 30: Capstone - II; Conclusion of the final project and course.

The full course structure can be found here.ย 
Important Dates
Registrations Start: 15th April, 2023
Enrolments Start: 22nd April 2023
Registrations Close : 15th May 2023
Enrolments Close: 18th May 2023
Course Start Date: 20th May 2023
Class Timings: 9:00 AM Saturdays
Course Schedule
Classes will be held every Saturday at 9:00AM
Course tentative start date is 20th May, 2023
Course is 30 Sessions long, split into 1-15 Sessions, followed by 2 weeks of off, followed by 16-30 Sessions, followed by 4-6 weeks of Capstone. Total Course duration would be 36-38 Weeks.ย 

What is your Full Name (spellings here would be used for certification) *
What is your email address (MUST be a GMAIL id) that you want to use for the course *
Please type the same GMAIL id you mentioned above! *
If you get your email spelling wrong, we can't communicate with you.
Have you attended any of these TSAI's programs earlier? *
Why are you taking this course? *
Since this is our first cohort, we have limited slots. We'd like to know why are you taking this course and how would this be helpful to you.
This course is 30 sessions or 9 months long. I know I need to invest 4-6 hours per weeks for next 9 month. I have the bandwidth and I will remain motivated to finish the course till the end. *
Please note, that it really doesn't make sense for TSAI to offer you enrolment, and then later you realise that you don't have time. This course will be repeated every 5-6 months, so if you don't have time, you can join EMLO4 or later course. Please think about your commitments.
Few other things we'd like you to know:
1. This is an intermediate level course, and it is expected that you already know how to train deep neural network models, such as an Object Detector.
2. It is assumed that you are comfortable in PyTorch or would learn PyTorch on your own before joining the course
3. It is assumed that you are comfortable in using Git, GitHub and Linux.
4. It is assumed that you have some background in handling HTML frontend templates (will be covered as well in the class, but basics of HTML will not be)
5. You must ensure that you have at least 4 hours per week to be able to work on the assignments
6. Every session WILL end with an assignment and a quiz.
7. You need to maintain 60% aggregate score to be eligible for Course Completion Certificate.
8. You need to finish the CAPSTONE project to be eligible for the course certification
9. You know that the classes will be held every Saturday at 9:00 AM
10. Classes are held live on Google Meet, and class video will be uploaded on the LMS. You can watch the video if you miss the class.
11. You will be asked to create your own AWS accounts for your model training and deployment. There are free tiers available with both, but you understand that if you do not follow the instructions, you may end up paying money. You agree to be cautious about your usage.
12. We offer "no questions asked full refund" if requested within 15 days (first 2 classes).
13. This course is 30 sessions long and would take 9 months to cover (including time for CAPSTONE project).
I understand the course requirements and would like to register. *
Would you like to pay via Bitcoin? *
If you'd like to pay via Bitcoin, you'd be sent a different enrollment payment link
This is a registration form.
If you have selected YES above, you'll receive the Enrolment form. This is just a registration form to share details with you, implement "first-in-time" limited enrolments, and to schedule interview (if applicable). Enrolment start on 22nd April. If you are filling this form after 22 April, then you'll get your enrolment details within 48 hours of filling in this form.ย 

Please make sure that you have joined the telegram group:ย to follow up on your enrolment request.
A copy of your responses will be emailed to the address you provided.
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