l5kit VS serverless-graphql

Level 5 | Self-driving Research | Palo Alto, US & London, UK & Tokyo, Japan | Full-Time + Interns | Onsite (Hybrid - 2 days in p/w - currently closed)

Level 5 at Woven Planet develops real-time car automation solutions via applied Machine Learning and Computer Vision (SFM, SLAM, 3D Perception etc). The system is adding self-driving capabilities to vehicles with the end goal of providing autonomy features to all cars from the largest car company in the world, Toyota. For more information please read through https://www.self-driving-cars.org

The platform at Level 5 is written in Python & C++. We are an AWS environment with additional use of NumPy, PyTorch, GRPC, Kafka, Kubernetes, Terraform, SageMaker, Spark, Postgres & others. We work in a OneBox cloud environment with continuous deployment and are firm believers in the benefits of open source (https://github.com/woven-planet/l5kit). We apply multiple flavours of ML to petabytes of data; such as Deep Learning, Transformers, Neural Networks & Reinforcement Learning. If you are interested in applying Machine Learning (ML) to real world data - look no further. https://www.self-driving-cars.org/datasets

If you like the idea of working on some of the most challenging problems in applied computer science. We are looking for talent across Data, Computer Vision, Machine Learning, Infrastructure, Research - and of course Software Engineering. Please find our jobs at https://boards.greenhouse.io/l5

Since then, the ecosystem has changed. Using the Serverless Framework makes deployment simpler. We released the managed Tracetest App making any serverless-based systems simpler to instrument and test. You can now test public-facing apps with no infra overhead!

We see some great results from using these in conjunction with frameworks such as SST or Serverless, and also some real spaghetti from people who organically proliferate 100’s of functions over time and lose track of how they relate to each other or how to update them safely across time and service. Buyer beware!

When we think about AWS serverless service, the first thing that comes to our mind is Lambda function. Yes, the quickest way to deploy this backend Express JS app to AWS is to deploy it as a Lambda function. The easiest way is using Serverless Framework.

Serverless Framework: https://www.serverless.com/

Github | Website

In this example, we're using the serverless framework to quickly set up the Lambda function along with an API gateway for the entry point. The lambda function is a simple Koa REST API with a few functional endpoints.

Naturally, there are several options available to declare your cloud resources. The options with the most popularity are the CDK, AWS CloudFormation, SST, Serverless framework, Terraform, and AWS SAM. There are others, but when talking about Infrastructure as Code (IaC), these are the ones you hear about most often.

1 - Serverless + AWS CDK + Lift: An integration that amps up the traditional Serverless Framework with Lift's static frontend construct and CDK's robust infra definition.

It's a bit easier in Python if you use tools like https://www.serverless.com/. I'm not sure if Rust has something similar yet.

Serverless