New features have been introduced to the platform that are designed to improve model handling, including the introduction of new classes in the SageMaker Python SDK: ModelBuilder and SchemaBuilder.

ModelBuilder, selects a compatible SageMaker container to deploy to, and captures the necessary dependencies. SchemaBuilder manages the tasks of serializing and deserializing model inputs and outputs.

“You can use the tools to deploy the model in your local development environment to experiment with it, fix any runtime errors, and when ready, transition from local testing to deploy the model on SageMaker with a single line of code”, Antje Barth, principal developer advocate at AWS, wrote in a blog post.

SageMaker Studio has been enhanced with updated deployment workflows, offering guidance to assist in selecting the most optimal endpoint configuration.

Furthermore, SageMaker has received improvements in its inference capabilities, contributing to reduced deployment costs and latency. These enhancements enable the deployment of one or more foundation models on a single endpoint, with control over memory allocation and the number of accelerators assigned to each model.

The system also features automatic monitoring of inference requests, intelligently routing them based on the availability of instances. According to Amazon, this advanced capability has the potential to slash deployment costs by up to 50% and decrease latency by up to 20%.

Additionally, Amazon SageMaker Canvas, a no-code interface designed for constructing machine learning models, has some updates. Users can now leverage natural language prompts during the data preparation process.

Within the chat interface, the application furnishes a variety of guided prompts tailored to the specific database being utilized. Alternatively, users have the flexibility to create their own prompts. For instance, they can instruct the system to generate a data quality report, filter out rows based on specific criteria, and perform various other tasks.

Moreover, users now have the ability to incorporate foundation models from Amazon Bedrock and Amazon SageMaker Jumpstart. This added capability empowers companies to deploy models specifically tailored to their unique business requirements.

SageMaker Canvas takes charge of the entire training process, facilitating fine-tuning of the model post-creation. Additionally, it offers an in-depth analysis of the generated model, presenting metrics such as perplexity and loss curves, training loss, and validation loss.

vFunction CMM enables software architects to detect and troubleshoot application architecture anomalies, define architectural baselines, and set thresholds. The vFunction CMM joins the vFunction Assessment Hub and vFunction Modernization Hub as the company’s newest application modernization platform products.

“Application architects today lack the architectural observability, visibility, and tooling to understand, track, and manage architectural technical debt as it develops and grows over time,”

said Moti Rafalin, Founder and CEO, vFunction.

“vFunction Continuous Modernization Manager allows architects to shift left into the ongoing software development lifecycle from an architectural perspective to manage, monitor, and fix application architecture anomalies on an iterative, continuous basis before they erupt into bigger problems”

he continued.

The latest version of the vFunction Modernization Hub also adds collaboration capabilities to make it easier for architects and modernization teams to work together. New analytics also identify the highest technical debt classes to focus refactoring priorities. The vFunction Assessment Hub has added a new multi-application assessment dashboard to analyze technical debt across a broad portfolio of applications.

“We are excited to be working with vFunction to monitor our applications to detect and fix issues before they result in more serious consequences. A key part of a strategic modernization strategy is to not only transform current monoliths but prevent future monoliths from forming”,

said Martin Lavigne, R&D Lead, Trend Micro.

vFunction Continuous Modernization Manager observes Java and .NET applications and services to first baseline the architecture, set baselines, and monitor for architectural drift and erosion to detect critical architectural anomalies including:

–New Dead Code Found: vFunction will detect new dead code in applications indicating that new, unnecessary code has surfaced in the application or the baseline architecture drifted and existing class or resource dependencies were changed.

–New Service Introduced: Based on the observed baseline service topology, when a new service has been detected vFunction will identify and alert that a new domain or major architectural event has occurred.

–New Common Classes Found: Building a stable, shared common library is a critical modernization best practice to reduce duplicate code and dependencies. Newly identified common classes can be added to a common library to prevent further technical debt from building up.

–Service Exclusivity Dropped: vFunction measures and baselines service exclusivity to determine the percentage of independent classes and resources of a service, alerting when new dependencies are introduced that expand architectural technical debt.

–New High-Debt Classes Identified: vFunction identifies the highest technical debt classes that are the highest contributors to application complexity. A “high-debt” class score is determined by its dependents, dependencies, and size and pinpoints a critical software component that should be refactored or re-architected.

Users will be notified of changes in the architecture through Slack, email, and vFunction Notifications Center. Through vFunction Continuous Modernization Manager, architects will be able to configure schedules for learning, analysis and the option to configure baseline measurements.

This release includes a quick linter and formatter; they require minimal configuration, come with beautiful and descriptive diagnostics, and have built-in support for JavaScript and TypeScript.

Rome’s formatter was inspired by Prettier, allowing most users to migrate with minimal or no changes. Our linter is based on a recommended set of rules, following community standards, and strives to be actionable and informative when it detects a problem.

Rome is a monolithic runtime-agnostic, multilingual toolchain. It is a formatter, linter, and in the future a compiler, packager, tester, and many more dependency-free features. Its monolithic architecture reveals a new experience for developers that is hard to achieve with many development stacks.

Avoid unnecessary work: Formatting your files with Prettier and linting them with ESLint means that both tools spend significant time traversing directories and parsing. Rome is fast because it never does the same work twice.

Reduce repetition: Rome uses a single configuration for all tools to reduce the number of configuration files in your project and avoid repetition.

Learn once: Many-tool development stacks suffer that engineers must interact with each tool, having to learn and remember the command names, configuration schemas, CLI arguments, and diagnostics formats. This is a lot to remember.

Remove entry barriers: Advanced analysis and checks, like analyzing the size of a bundle or bundle size budgeting, often require additional tools and lack editor or linter integration. This added complexity is an entry barrier for many.