Deploying Deep Learning Models as Microservices
Deploying Deep Learning Models as Microservices


Powering your application with deep learning is no walk in the park, but is certainly attainable with some tricks and good practice. Serving a deep learning model on a production system demands the model to be stable, reproducible, capable of isolation and behave as a stand-alone package. One possible solution to this is a containerized microservice.

Ideally, serving deep learning microservices should be quick and efficient, without having to dive deep into the underlying algorithms and their implementation. Too good to be true? Not anymore! Together, we will demystify the process of developing, training, and deploying deep learning models as a web microservice.

We will kick off with an overview of how deep learning models are best published as Docker images on DockerHub, and are best prepared for deployment in local or cloud environments using Kubernetes or Docker.

We highlight the following benefits of such an approach:
- Standardized REST API implementation and application-friendly output format (JSON)
- Abstracting out the complex pre and post processing portions of the model inputs and outputs.

We then demonstrate these concepts with Model Asset Exchange, an open source framework. All these applications and the framework itself are open source and we conclude by inviting contributions and opening the gates for you to be a part of this amazing initiative!


Saishruthi Swaminathan is a Data science enthusiast who is passionate about transforming data into useful products. A person with 'Agile Mindset'

She is an electrical engineer who identified her true passion for data science. The amazing growth of intelligence in things around intrigued her to think 'how they can work so ?'. This curiosity led me exploring areas of data mining, machine learning, and deep learning. She started teaching myself how these algorithms work !!!

Saishruthi loves creative as well as complex problem solving and programming. Continuous innovation and learning are what attracted her to data science. She is a person who has always been the first one to roll up sleeves if I encounter any data problem.

2 years work experience as a software developer at Tata Consultancy Services has greatly strengthened my programming and problem solving ability. Worked as Software Engineering Intern at MOBODEXTER who is offering cloud, analytics and IoT engineering services. I have been working in designing products using machine learning, deep learning and image processing techniques for surveillance, safety, and healthcare. Working on solving problems whose solutions are not obvious, where the success rate is critical and highly depending on data preprocessing and algorithm efficiency.

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