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Mojo🔥 24.3 is now available for download and this is a very special release. This is the first major release since Mojo🔥 standard library was open sourced and it is packed with the wholesome goodness of community contributions! The enthusiasm from the Mojo community to enhance the standard library has been truly remarkable. And on behalf of the entire Mojo team, we’d like to thank you for all your feedback, discussion and, contributions to Mojo, helping shape it into a stronger and more inclusive platform for all. 

Today, we're exploring how a multimodal approach can further enhance semantic search by combining textual and visual data and we will discuss how MAX Engine can optimize multiple models for inference.

Modern software and delivery mechanisms are more vulnerable to security attacks than ever before. It is estimated that in 2024, 96% of total codebases contained open-source code with an average of 500+ components per app.

A matrix is a rectangular collection of row vectors and column vectors that defines linear transformation. A matrix however, is not implemented as a rectangular grid of numbers in computer memory, we store them as a large array of elements in contiguous memory.

Very recently, we announced the open sourcing of the Mojo standard library. This has marked a significant milestone for our community, not only providing the best way to understand the implementation details of various functionalities within the standard library but also creating an excellent opportunity to contribute to Mojo.

This will be your example-driven guide to Mojo SDK 24.2, as part of the latest MAX release. If I had to pick a name for this release, I’d call it MAXimum⚡ Mojo🔥 Momentum 🚀 because there is so much much good stuff in this release, particularly for Python developers, adopting Mojo. 

At Modular, open source is ingrained in our DNA. We firmly believe for Mojo to reach its full potential, it must be open source. We have been progressively open-sourcing more of Mojo and parts of the MAX platform, and today we’re thrilled to announce the release of the core modules from the Mojo standard library under the Apache 2 license!

In this blog post, we will dive deep into the dynamic shapes support in MAX Engine’s 24.2 release. We will do so by first defining what dynamic shapes mean in machine learning, discussing their various types and use cases.

Today, we’re excited to announce general availability of MAX 24.2, with several big features.

Model deployment is often the domain of IT professionals and cloud infrastructure experts who understand how to securely and reliably host model endpoints that scale with usage demand. Thankfully, Amazon SageMaker is fully managed and handles all the underlying infrastructure, allowing developers and data scientists like you and me, who are not IT experts, to use simple APIs to host secure, low-latency, and highly scalable model endpoints.