Seamless Deployment of the models to Production

Rohit Adhikari (~rohit40)




Seamless Deployment of the ML models to Production:

As the major challenge in Machine Learning is to deploy your model to production. Most of the models were 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 a lot of challenges in integrating the models with the application, retraining of models, monitoring of models, versioning of models, etc. This is the reason 80-85% of the model doesn't go 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 from software reuse,  as models must be re-tuned based on input data/scenario.

To reuse a model you may need to fine-tune 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 analyzing signals such as data drift

How to build your MLOps platform?

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


Knowledge of Machine Learning life cycle Understanding of dockers to containerize the image Basic knowledge of Kubernetes Understanding of Kubeflow - Machine Learning workflow to develop, deploy and serve the machine learning models

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Speaker Info:

I'm a Sr. Data Scientist, worked on different projects Machine learning, Deep Learning, Computer Vision, and MLOps. Passionate about building intelligent machines using AI ML and deploy them seamlessly to the productions so that it can be easily used for inference. Holding a Masters Degree in Computer Application and PGP in Big Data and Machine Learning.

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Section: Developer tools and automation
Type: Talks
Target Audience: Intermediate
Last Updated: