This article was published as a part of the Data Science Blogathon. A Machine Learning solution to an unambiguously defined business problem is developed by a Data Scientist ot ML Engineer. The Model development process undergoes multiple iterations and finally, a model which has acceptable performance metrics on test data is taken to the production environment. Taking the final chosen model reaching it out to the users is called deployment and there are a few options available to deploy a model. Kubernetes(also called k8s) is one of the open-source tools used for deploying our applications.

Amazon SageMaker Serverless Inference (Preview) was recently announced at re:Invent 2021 as a new model hosting feature that lets customers serve model predictions without having to explicitly provision compute instances or configure scaling policies to handle traffic variations. Serverless Inference is a new deployment capability that complements SageMaker's existing options for deployment that include: SageMaker Real-Time Inference for workloads with low latency requirements in the order of milliseconds, SageMaker Batch Transform to run predictions on batches of data, and SageMaker Asynchronous Inference for inferences with large payload sizes or requiring long processing times. Serverless Inference means that you don't need to configure and manage the underlying infrastructure hosting your models. When you host your model on a Serverless Inference endpoint, simply select the memory and max concurrent invocations. Then, SageMaker will automatically provision, scale, and terminate compute capacity based on the inference request volume.

Prisma Cloud from Palo Alto Networks is sponsoring our coverage of AWS re:Invent 2021. Launched at the company's re:Invent 2021 user conference earlier this month, Amazon Web Services' Amazon SageMaker Serverless Inference is a new inference option to deploy machine learning models without configuring and managing the compute infrastructure. It brings some of the attributes of serverless computing, such as scale-to-zero and consumption-based pricing. With serverless inference, SageMaker decides to launch additional instances based on the concurrency and the utilization of existing compute resources. The fundamental difference between the other mechanisms and serverless inference is how the compute infrastructure is provisioned, scaled, and managed. You don't even need to choose an instance type or define the minimum and maximum capacity.

MLOps has become a trendy topic lately as managing ML in production has become challenging for teams and organizations. One of the components of MLOps is model deployment. Once a model is trained, it doesn't really have any value until is is deployed in production. One way that people deploy ML models is by containerizing them using docker and exposing the model via a REST endpoint. This works ok, but containerizing a model every time can be a bit cumbersome.

In this tutorial, we will package and deploy a simple model that exposes an HTTP API and serves predictions to a device managed by Synpse. Flash is a high-level deep learning framework for fast prototyping, baselining, finetuning and solving deep learning problems. It features a set of tasks for you to use for inference and finetuning out of the box, and an easy to implement API to customize every step of the process for full flexibility. Flash is built for beginners with a simple API that requires very little deep learning background, and for data scientists, Kagglers, applied ML practitioners and deep learning researchers that want a quick way to get a deep learning baseline with advanced features PyTorch Lightning offers. I decided to go with the image_classification as it was important to me to have some kind of service that could differentiate between ants and bees.

Machine learning is one of the most trending topics of our time. Almost every company and professionals/students related to the IT sector are working in this field and increasing their knowledge level day by day. As the projects about machine learning start to become widespread, there are more and more innovations about the practices related to how these projects are transferred to production environments. In this article, I will make an example of how to transfer a machine learning model to production in the fastest and most effective way. I hope it will be a useful study in terms of awareness. Before starting our example, I want to give some information about this transfer infrastructure verbally.