ranklens VS Medusa

Right now it runs in a dev-mode on a single EC2 t3.large instance with loadavg ~0.30, but the inference load is quite tiny right now: around 3-4 reranking requests per second. And yes, as a typical open-source project it still crashes from time to time :)

The training dataset is not that huge (see https://github.com/metarank/ranklens/ for details, it's open-source), so we do a full retraining directly on the node right after the deployment, and it takes around 1 minute to finish. We also run the same process inside the CI: https://github.com/metarank/metarank/blob/master/run_e2e.sh

There is an option to run this thing in a distributed mode:

* training is done using a separate batch job running on Apache Flink (and on k8s using flink's integration)

* feature updates are done in a separate streaming Flink job, writing everything in Redis

* The API fetches latest feature values from Redis and runs the ML model.

The dev-mode I've mentioned earlier is when all these three things are bundled together in a single process to make it easier to play with the tool. But we didn't spent much time testing distributed setup, as this thing is still a hobby side-project and we're limited in time spent developing it.

You can learn more about Medusa by checking their GitHub repository.

Hello, I'm Nick, co-founder of Medusa. In keeping with our tradition, we are excited to share our progress on our open-source commerce SDK with this wonderful community. We eagerly want your feedback!

If you have everything installed, follow these steps to set up your Medusa project.

As the world moves towards a more digital economy, e-commerce is becoming an increasingly important part of businesses. To keep up with the changing times, it’s essential to adopt a cloud-native approach to your e-commerce platform. In this blog, we will introduce you to two powerful tools that can help you achieve rapid deployment of your e-commerce website: Microtica and Medusa.js. We will take a deep dive into what cloud-native e-commerce is, how Microtica and Medusa.js work, and how they complement each other. We will also discuss case studies of successful deployment using these tools and what skills are required for implementing them. Lastly, we’ll talk about future trends in cloud-native e-commerce and how Microtica and Medusa.js shape the future of online shopping.

The Medusa Next.js Starter Template supports popular instant-search providers Melliseach and Algolia out of the box. With these integrations, you can provide fast and accurate search results.

Co-founder of Medusa, here.

Co-founder of Medusa here; building blocks for digital commerce.

Hi - I'm Nick, co-founder of MedusaJS.

Nick, co-founder of Medusa, here. As per tradition now, we love to share the progress we make on our open-source commerce SDK with this amazing community. Always keen to hear your feedback!