Seemless Deployment of the models to Production

Rohit Adhikari (~rohit40)




Seemless Delpoyment of the ML models to Production:

As the major challenge in Machine Learning is to deploy your model to production. Most of the models was developed after the research done by Data Scientist and ML Engineers in their local system and when they plan to deploy it in production they face lot of challenges in integrating the models with application, retraining of models, monitoring of models, versioning of models etc. This is the reason 80-85% of the model doesn't goes into production.

MLOps is the solution to overcome this issue as it empowers the data scientist and software engineer to bring the ML models to Production. It also enables you to track, version, audit, re-use every stage in your Machine Learning lifecycle and provide orchestration services to streamline this workflow.

How MLOps is different from DevOps?

Data/model versioning != code versioning, how to version the data sets as the schema and origin data change

Model reuse is different than software reuse, as models must be retuned based on input data / scenario.

To reuse a model you may need to fine-tune / transfer learn on it (meaning you need the training pipeline)

Models tend to decay over time & you need the ability to retrain them on demand to ensure they remain useful in a production context.

DevOps = Development + Operations

MLOps = Machine Learning(Data and Model) + Development + Operations

Key Challenges to solve with MLOps?

Model reproducibility & versioning:

Track, snapshot & manage assets used to create the model

Enable collaboration and sharing of ML pipelines

Model audibility & explainability:

Maintain asset integrity & persist access control logs

Certify model response meets regulatory & adversarial standards

Model packaging & validation:

Support model portability across a variety of platforms

Certify model performance meets functional and latency requirements

Model deployment & monitoring:

Release models with confidence

Monitor & know when to retrain by analysing signals such as data drift

How to build your MLOps platform?

Collaboration Tool + Development & Deployment Tools + Monitoring and Operations Tools


Software Development Python Data science engineering

Speaker Info:

I'm a Data Scientist, worked on different projects Machine learning, Deep Learning and Computer Vision. Passionate about building intelligent machines using AI ML. Holding Masters Degree in Computer Application and PGP in Big Data and Machine Learning.

Speaker Links:

Section: Data Science, Machine Learning and AI
Type: Talks
Target Audience: Intermediate
Last Updated: