I have fond memories of the time I built an application live at the AWS GenAI Loft London last September. AWS GenAI Lofts are back in locations such as San Francisco, Berlin, and more, to continue providing collaborative spaces and immersive experiences for startups and developers. Find a loft near you for hands-on access to AI products and services, events, workshops, and networking opportunities, that you can’t miss!

Last week’s launches
Here are some launches that got my attention during the previous week.

Four ways to grant cross-account access in AWS — For some situations, you might want to enable centralized operations across multiple AWS accounts or share resources across teams, or projects within your teams. In these cases, you may be concerned about security, availability, or the manageability of granting this cross-account access. We’ve announced four ways to grant cross-account access in AWS and detail each of the methods and its unique trade-offs.

Amazon ECS adds support for additional IAM condition keys — We’ve launched eight new service-specific condition keys for Identity and Access Management (IAM). These new condition keys let you create IAM policies as well as srvice control policies (SCPs) to better enforce your organizational policies in containerized environments. You can use IAM condition keys to author policies that enforce access control based on API request context.

AWS Chatbot is now named Amazon Q Developer — AWS Chatbot has been renamed to Amazon Q Developer, representing an enhancement to developer productivity through generative AI-powered capabilities. Furthermore, this update is an enhancement of our chat-based DevOps capabilities. By combining the proven functionality of AWS Chatbot with the generative AI capabilities of Amazon Q, we’re providing developers with more intuitive, efficient tools for cloud resource management.

Anthropic’s Claude 3.7 Sonnet hybrid reasoning model is now available in Amazon Bedrock — We’re expanding the foundation models (FM) offerings of Amazon Bedrock and we’ve announced the availability of Anthropic’s Claude 3.7 Sonnet FM in Amazon Bedrock. Claude 3.7 Sonnet is Anthropic’s most intelligent model to date. It stands out as their first hybrid reasoning model capable of producing quick responses or extended thinking, meaning it can work through difficult problems using careful, step-by-step reasoning.

Other AWS news
JAWS-UG (Japan AWS User Group) is the largest AWS user group in the world, and holds JAWS Days every year with over a thousand participants from Japan, Korea, Taiwan, and Hong Kong. The March 1st event started with a keynote speech on next-generation development by Jeff Barr (VP of AWS Evangelism), and included over 100 technical and community experience sessions, lightning talks, and workshops such as Game Days, Builders Card Challenges, and networking parties. If you want to experience the most active AWS community event in the world, I recommend attending next year.

Amazon Q Developer now generally available in Amazon SageMaker Canvas — Announced as available in preview at AWS reinvent 2024, Amazon Q Developer is now generally available in Amazon SageMaker Canvas to help you build machine learning (ML) models using natural language.

Applications for the 2025 AWS Cloud Club Captains Program are still open through March 6th. AWS Cloud Clubs are student-led groups for post-secondary and independent students, 18 years old and over. Find a club near you on the Meetup page.

From community.aws
Here are some of my favorite posts from community.aws:

DevSecOps on AWS: Secure, Automate, and Have a Laugh Along the Way – Discover how DevSecOps on AWS transforms your development pipeline by integrating security from the very first commit to production deployment, by Ahmed Mohamed.

Find out how to earn 100 percent free AWS certification vouchers in Opportunity to earn free AWS Certification Vouchers, published by Anand Joshi.

In the post, Boost SaaS Onboarding & Retention with AWS AI & Automation, Kaumudi Tiwari details how to navigate endless forms, generic guides, and a cluttered interface when signing up for a new software as a service (SaaS) platform.

My colleague Dennis Traub has published helpful step-by-step guides on how to use reasoning capabilities with Anthropic’s Claude 3.7 Sonnet in your C#/.NET, Java, JavaScript, or Python applications. Find these posts and much more generative AI-related content in the Gen AI Space on community.aws.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Milan, Italy (April 2), Bay Area – Security Edition (April 4), Timișoara, Romania (April 10), and Prague, Czech Republic (April 29).

AWS Innovate: Generative AI + Data – Join a free online conference focusing on generative AI and data innovations. Available in multiple geographic regions: APJC and EMEA (March 6), North America (March 13), Greater China Region (March 14), and Latin America (April 8).

AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Paris (April 9), Amsterdam (April 16), London (April 30), and Poland (May 5).

AWS re:Inforce – AWS re:Inforce (June 16–18) in Philadelphia, PA, is our annual learning event devoted to all things AWS Cloud security. Registration opens in March, so be ready to join more than 5,000 security builders and leaders.

AWS DevDays are free, technical events where developers can learn about some of the hottest topics in cloud computing. DevDays offer hands-on workshops, technical sessions, live demos, and networking with AWS technical experts and your peers. Register to access AWS DevDays sessions on demand.

Create your AWS Builder ID and reserve your alias. Builder ID is a universal login credential that gives you access—beyond the AWS Management Console—to AWS tools and resources, including over 600 free training courses, community features, and developer tools such as Amazon Q Developer.

AWS Training and Certification hosts free training events, both online and in-person, that help you get the most out of the AWS Cloud. Register to gain foundational cloud knowledge or dive deep in a technical area. Join AWS experts for training events that meet your goals, such as AWS Discovery Days, in-person. and virtual events at AWS Skills Centers including the one in Cape Town.

You can browse all upcoming in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Veliswa

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

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The post AWS Weekly Roundup: Anthropic Claude 3.7, JAWS Days, cross-account access, and more (March 3, 2025) appeared first on The NOKIA Blog.

As a data scientist, I’ve experienced firsthand the challenges of making machine learning (ML) accessible to business analysts, marketing analysts, data analysts, and data engineers who are experts in their domains without ML experience. That’s why I’m particularly excited about today’s Amazon Web Services (AWS) announcement that Amazon Q Developer is now available in Amazon SageMaker Canvas. What catches my attention is how Amazon Q Developer helps connect ML expertise with business needs, making ML more accessible across organizations.

Amazon Q Developer helps domain experts build accurate, production-quality ML models through natural language interactions, even if they don’t have ML expertise. Amazon Q Developer guides these users by breaking down their business problems and analyzing their data to recommend step-by-step guidance for building custom ML models. It transforms users’ data to remove anomalies, and builds and evaluates custom ML models to recommend the best one, while providing users control and visibility into every step of the guided ML workflow. This empowers organizations to innovate faster with reduced time to market. It also reduces their reliance on ML experts so their specialists can focus on more complex technical challenges.

For example, a marketing analyst can state, “I want to predict home sales prices using home characteristics and past sales data”, and Amazon Q Developer will translate this into a set of ML steps, analyzing relevant customer data, building multiple models, and recommending the best approach.

Let’s see it in action
To start using Amazon Q Developer, I follow the Getting started with using Amazon SageMaker Canvas guide to launch the Canvas application. In this demo, I use natural language instructions to create a model to predict house prices for marketing and finance teams. From the SageMaker Canvas page, I select Amazon Q and then choose Start a new conversation.

In the new conversation I write:

I am an analyst and need to predict house prices for my marketing and finance teams.

Next, Amazon Q Developer explains the problem and recommends the appropriate ML model type. It also outlines the solution requirements, including the necessary dataset characteristics. Amazon Q Developer then asks if I want to upload my dataset or I want to choose a target column. I select it to upload my dataset.

In the next step, Amazon Q Developer lists the dataset requirements, which include relevant information about houses, current house prices, and the target variable for the regression model. It then recommended next steps, including: I want to upload my dataset, Select an existing dataset, Create a new dataset or I want to choose a target column. For this demo, I’ll use the canvas-sample-housing.csv sample dataset as my existing dataset.

After selecting and loading the dataset, Amazon Q Developer analyzes it and suggests median_house_value as the target column for the regression model. I accept by selecting I would like to predict the “median_house_value” column. Moving on to the next step, Amazon Q Developer details which dataset features (such as “location”, “housing_median_age”, and “total_rooms”) it will use to predict the median_house_value.

Before moving forward with model training, I ask about the data quality, because without good data we can’t build a reliable model. Amazon Q Developer responds with quality insights for my entire dataset.

I can ask specific questions about individual features and their distributions to better understand the data quality.

To my surprise, through the previous question, I discovered that the “households” column has a wide variation between extreme values, which could affect the model’s prediction accuracy. Therefore, I ask Amazon Q Developer to fix this outlier problem.

After the transformation is done, I can ask what steps Amazon Q Developer followed to make this change. Behind the scenes, Amazon Q Developer applies advanced data preparation steps using SageMaker Canvas data preparation capabilities, which I can review and see the steps so that I can visualize and replicate the process to get the final, prepared dataset for training the model.

After reviewing the data preparation steps, I select Launch my training job.

After the training job is launched, I can see its progress in the conversation, and the datasets created.

As a data scientist, I particularly appreciate that, with Amazon Q Developer, Ican see detailed metrics such as the confusion matrix and precision-recall scores for classification models and root mean square error (RMSE) for regression models. These are crucial elements I always look for when evaluating model performance and making data-driven decisions, and it’s refreshing to see them presented in a way that’s accessible to nontechnical users to build trust and enable proper governance while maintaining the depth that technical teams need.

You can access these metrics by selecting the new model from My Models or from the Amazon Q conversation menu:

• Overview – This tab shows the Column impact analysis. In this case, median_income emerges as the primary factor influencing my model.
• Scoring – This tab provides model accuracy insights, including RMSE metrics.
• Advanced metrics – This tab displays the detailed Metrics table, Residuals and Error density for in-depth model evaluation.

After reviewing these metrics and validating the model’s performance, I can move to the final stages of the ML workflow:

• Predictions – I can test my model using the Predictions tab to validate its real-world performance.
• Deployment – I can create an endpoint deployment to make my model available for production use.

This simplifies the deployment process, a step that traditionally requires significant DevOps knowledge, into a straightforward operation that business analysts can handle confidently.

Things to know
Amazon Q Developer democratizes ML across organizations:

Empowering all skill levels with ML – Amazon Q Developer is now available in SageMaker Canvas, helping business analysts, marketing analysts, and data professionals who don’t have ML experience create solutions for business problems through a guided ML workflow. From data analysis and model selection to deployment, users can solve business problems using natural language, reducing dependence on ML experts such as data scientists and enabling organizations to innovate faster with reduced time to market.

Streamlining the ML workflow – With Amazon Q Developer available in SageMaker Canvas, users can prepare data, and build, analyze, and deploy ML models through a guided, transparent workflow. Amazon Q Developer provides advanced data preparation and AutoML capabilities that democratize ML, and allows non-ML experts to produce highly-accurate ML models.

Providing full visibility into the ML workflow – Amazon Q Developer provides full transparency by generating the underlying code and technical artifacts such as data transformation steps, model explainability, and accuracy measures. This allows cross-functional teams, including ML experts, to review, validate, and update the models as needed, facilitating collaboration in a secure environment.

Availability – Amazon Q Developer is now in preview release in Amazon SageMaker Canvas.

Pricing – Amazon Q Developer is now available in SageMaker Canvas at no additional cost to both Amazon Q Developer Pro Tier and Amazon Q Developer Free tier users. However, standard charges apply for resources such as SageMaker Canvas workspace instances and any resources used for building or deploying models. For detailed pricing information, visit the Amazon SageMaker Canvas Pricing.

To learn more about getting started visit the Amazon Q Developer product web page.

— Eli

The post Use Amazon Q Developer to build ML models in Amazon SageMaker Canvas appeared first on The NOKIA Blog.