Bhanu Sharma
Machine Learning Engineer
SpotDraft
location_on India
Member since 4 years
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Bhanu Sharma
Specialises In (based on submitted proposals)
My name is Bhanu Sharma, currently employed as a Machine Learning engineer at SpotDraft where i am involved in developing deep learning models for contract analysis. Before that i was employed as a Machine Learning intern in Bangalore where i worked with sensor data to build a system to detect and differentiate true occurrences of an event from the false one's
Here at SpotDraft i have been working with various NLP methods using tools like StanfordCoreNLP and Spacy to provide meaningful insights from contracts.
I have been involved in building an entity extraction model (Bi-LSTM + CRF), multi-class text classier (CNN), object detection and segmentation (modified Darknet).
I have had the privilege to be involved in the entire life cycle of model creation, from annotating data using Prodigy, to building the model and improving it, to serving the model predictions by deploying the docker container to Kubernetes clusters.
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From "Hello World" to production, running Machine Learning models into production using Tensorflow serving and Kubernetes.
45 Mins
Talk
Intermediate
Do you have issues running your Machine Learning models in to production or want to learn what are some of the best industry practices in regards to deployment and serving predictions for Machine Learning models.
In a local environment the task is simple but things become complex when a model is run in production. Scaling, Robustness and reliability are few metrics that one has to take care of while doing so.
This talk will look into answering that question by taking the audience through an example using best made industry practices.
Tensorflow serving "is a flexible, high-performance serving system for machine learning models, designed for production environments" . It allows one to deploy newer versions of a model to production without changing either the architecture or the API.
Configure automatic deployment of a newer model as soon as an update model is available and run it alongside the older one for a no frills transition or scrap the older model completely and run the improved one in the production. With Tensorflow Serving one can easily deploy, manage their models in production to scale and even automate their deployment pipeline.
image credits: Tensorflow Serving Architecture
Kubernetes is used to deploy the created Serving, which will enable better scaling and robustness. How properties of a Kubernetes cluster like elasticity and resource managment can be used to serve models to a large number of users and run computationally and data intensive models in production.
Audience will be taken through each step of the process with in depth insights on the tools used while answering the questions of What, Why and How along the process. -
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