End to End MLOps Basics // Raviraja Ganta // MLOps Meetup #82

MLOps Community Meetup #82! Last Wednesday we talked to Raviraja Ganta, Founding Engineer - NLP of Enterpret.
//Abstract
MLOps, or DevOps for machine learning, enables data science and IT teams to collaborate and increase the pace of model development and deployment by monitoring, validation, and governance of machine learning models.
To understand MLOps, we must first understand the ML systems lifecycle from developing ML models to deploying and monitoring them.
// Bio
Raviraja is currently working at Enterpret as a Founding Engineer - NLP.
His interests are in Unsupervised Algorithms, Semantic Similarity, and Productionising the NLP models. Raviraja likes to follow the latest research works happening in the NLP domain.
Besides work, Raviraja likes cooking 🥘 , cycling 🚴‍♀️ , and kdramas 🎥.
// Related links
github.com/graviraja/MLOps-Ba...
gravirajag.dev/
---------- ✌️Connect With Us ✌️------------
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Connect with Demetrios on LinkedIn: / dpbrinkm
Connect with Raviraja on LinkedIn: / ravirajag
Timestamps:
[00:00] Introduction to Raviraja Ganta
[03:05] Basics of End to End MLOps
[03:20] Raviraja's background
[03:46] Agenda
[04:04] Why MLOps?
[04:33] Survey by ML practitioners at Full Stack Deep Learning
[05:16] What is MLOps?
[05:49] MLOps Lifecycle
[06:17] ML Development
[06:41] Training Operationalization
[07:03] Continuous Training
[07:34] Model Deployment
[07:53] Prediction Serving
[08:28] Continuous Monitoring
[08:48] Data and Model Management
[09:37] Disclaimer!!
[10:12] ML Development
[10:33] ML Development - Pytorch Lightning
[12:00] Model Monitoring
[12:46] Model Monitoring - Weights and Biases
[13:32] Configuration Management
[14:42] Configuration Management - Hydra
[15:28] Data and Model Management
[15:56] Data and Model Management - DVC
[16:56] Model Packaging
[18:07] Model Packaging - ONNX
[18:46] Code Packaging
[19:10] Code Packaging - Docker
[19:40] Continuous Integration and Continuous Delivery (Deployment) - CI/CD
[20:17] CI/CD - Github Actions
[20:49] Container Registry
[21:30] Container Registry - ECR
[24:53] Model Deployment
[24:07] Model Deployment -Serverless
[26:45] Choosing the best tool from those with the same functionality
[29:15] DVC sister tool (CML) playing with Github Action
[32:07] Updating test data
[34:11] AWS Lambda vs Containers for serverless inference service
[38:00] AWS Lambda vs ECT EC2 AKS on saving money
[39:26] Model Monitoring
[40:19] Model Monitoring - Problems
[42:16] Model Monitoring - Kibana
[43:43] Summary
[45:50] Next Steps
[49:11] Complete Code
[49:42] Q&A
[50:16] Amazon Sagemaker as an option
[50:57] Managing Dependencies
[51:45] Tool that solves the same problem in a different way vs Optimize Efficiency which takes a different approach
[54:27] Single provider that provides an end-to-end solution

[@amusunny]

Excellent session... Thank you so much!

[@inzamam483]

Really good session. I now understand the high-level view of mlops.

[@thewhitewolf8502]

This was very insightful! Thanks a ton. Watched it throughout

[@afjalahamad2465]

great session keep it up.

[@adarshvyas71]

Awesome

[@kodjigarpp]

Very clear thanks! In your git repo course it would be very helpful to have the web app integration as well!

[@steffitan332]

The ukulele is so epic

[@091carl]

Great, thanks! Just FYI - there's a typo in the URL for Raviraja's website.

[@MLOps]

Thank you! have now updated!

[@user-hl5sk1oj1m]

thank you for sharing this awesome tutorial! can somebody let me know where I can get the slides used in the video please?

[@MLOps]

here you go! docs.google.com/presentation/d/1lBcQHZMQ72rAiW7DNFXzRLx6nyaQeWa0bj5plddwozg/edit?usp=sharing