Ryan welcomes Sebastian Gierlinger, VP of Engineering at Storyblok, to talk about how headless content management systems (CMS) fit into an increasingly componentized software landscape.

In this article, we look at a SQL Server stored procedure that can be used for the DBSCAN algorithm for data analysis.

The post DBSCAN Algorithm in SQL Server appeared first on MSSQLTips.com.

Posted by Sandhya Mohan, Product Manager

Gratitude is a mental wellness Android app that encourages self-care and positivity with techniques like in-app journaling, affirmations, and vision boards. These mindfulness exercises need to be free from performance bottlenecks, bugs, and errors for the app to be truly immersive and helpful—but researching solutions and debugging code took away valuable time from the team experimenting on new features. To find a better balance, Gratitude used Gemini in Android Studio to help improve the app’s code and streamline the development process, enabling the team to implement those exciting new features faster.

This episode we are joined by Andrew Moreland, co-founder of Chalk. Andrew explains how their company’s core business model is to deploy their software directly into their customers’ cloud environments. This decision was driven by the need to handle highly sensitive data, like PII and financial records, that customers don't want to hand over to a third-party startup.

The conversation delves into the surprising and complex challenges of this approach, which include managing granular IAM permissions and dealing with hidden global policies that can block their application. Andrew and Warren also discuss the real-world network congestion issues that affect cross-cloud traffic, a problem they've encountered multiple times. Andrew shares Chalk's mature philosophy on software releases, where they prioritize backwards compatibility to prevent customer churn, which is a key learning from a competitor.

Finally, the episode explores the advanced technical solutions Chalk has built, such as their unique approach to "bitemporal modeling" to prevent training bias in machine learning datasets. As well as, the decision to move from Python to C++ and Rust for performance, using a symbolic interpreter to execute customer code written in Python without a Python runtime. The episode concludes with picks, including a surprisingly popular hobby and a unique take on high-quality chocolate.

Notable Facts

• Fact - The $1M hidden Kubernetes spend
• Giraffe and Medical Ruler training data bias
• SOLID principles don't produce better code?
• Veritasium - The Hole at the Bottom of Math
• Episode: Auth Showdown on backwards compatible changes

Picks:

• Warren - Switzerland Grocery Store Chocolate
• Andrew - Trek E-Bikes

Download audio: https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/67812183/download.mp3

Today on Talk Python: What really happens when your data work outgrows your laptop. Matthew Rocklin, creator of Dask and cofounder of Coiled, and Nat Tabris a staff software engineer at Coiled join me to unpack the messy truth of cloud-scale Python. During the episode we actually spin up a 1,000 core cluster from a notebook, twice! We also discuss picking between pandas and Polars, when GPUs help, and how to avoid surprise bills. Real lessons, real tradeoffs, shared by people who have built this stuff. Stick around.

Episode sponsors

Seer: AI Debugging, Code TALKPYTHON
Talk Python Courses

Links from the show

Download audio: https://talkpython.fm/episodes/download/519/data-science-cloud-lessons-at-scale.mp3

In March, Amazon Web Services (AWS) became the first cloud service provider to deliver DeepSeek-R1 in a serverless way by launching it as a fully managed, generally available model in Amazon Bedrock. Since then, customers have used DeepSeek-R1’s capabilities through Amazon Bedrock to build generative AI applications, benefiting from the Bedrock’s robust guardrails and comprehensive tooling for safe AI deployment.

Today, I am excited to announce DeepSeek-V3.1 is now available as a fully managed foundation model in Amazon Bedrock. DeepSeek-V3.1 is a hybrid open weight model that switches between thinking mode (chain-of-thought reasoning) for detailed step-by-step analysis and non-thinking mode (direct answers) for faster responses.

According to DeepSeek, the thinking mode of DeepSeek-V3.1 achieves comparable answer quality with better results, stronger multi-step reasoning for complex search tasks, and big gains in thinking efficiency compared with DeepSeek-R1-0528.

(c) https://api-docs.deepseek.com/news/news250821

Benchmarks	DeepSeek-V3.1	DeepSeek-R1-0528
Browsecomp	30.0	8.9
Browsecomp_zh	49.2	35.7
HLE	29.8	24.8
xbench-DeepSearch	71.2	55.0
Frames	83.7	82.0
SimpleQA	93.4	92.3
Seal0	42.6	29.7
SWE-bench Verified	66.0	44.6
SWE-bench Multilingual	54.5	30.5
Terminal-Bench	31.3	5.7

DeepSeek-V3.1 model performance in tool usage and agent tasks has significantly improved through post-training optimization compared to previous DeepSeek models. DeepSeek-V3.1 also supports over 100 languages with near-native proficiency, including significantly improved capability in low-resource languages lacking large monolingual or parallel corpora. You can build global applications to deliver enhanced accuracy and reduced hallucinations compared to previous DeepSeek models, while maintaining visibility into its decision-making process.

Here are your key use cases using this model:

As I mentioned in my previous post, when implementing publicly available models, give careful consideration to data privacy requirements when implementing in your production environments, check for bias in output, and monitor your results in terms of data security, responsible AI, and model evaluation.

You can access the enterprise-grade security features of Amazon Bedrock and implement safeguards customized to your application requirements and responsible AI policies with Amazon Bedrock Guardrails. You can also evaluate and compare models to identify the optimal model for your use cases by using Amazon Bedrock model evaluation tools.

Get started with the DeepSeek-V3.1 model in Amazon Bedrock
If you’re new to using the DeepSeek-V3.1 model, go to the Amazon Bedrock console, choose Model access under Bedrock configurations in the left navigation pane. To access the fully managed DeepSeek-V3.1 model, request access for DeepSeek-V3.1 in the DeepSeek section. You’ll then be granted access to the model in Amazon Bedrock.

Next, to test the DeepSeek-V3.1 model in Amazon Bedrock, choose Chat/Text under Playgrounds in the left menu pane. Then choose Select model in the upper left, and select DeepSeek as the category and DeepSeek-V3.1 as the model. Then choose Apply.

Using the selected DeepSeek-V3.1 model, I run the following prompt example about technical architecture decision.

Outline the high-level architecture for a scalable URL shortener service like bit.ly. Discuss key components like API design, database choice (SQL vs. NoSQL), how the redirect mechanism works, and how you would generate unique short codes.

You can turn the thinking on and off by toggling Model reasoning mode to generate a response’s chain of thought prior to the final conclusion.

You can also access the model using the AWS Command Line Interface (AWS CLI) and AWS SDK. This model supports both the InvokeModel and Converse API. You can check out a broad range of code examples for multiple use cases and a variety of programming languages.

To learn more, visit DeepSeek model inference parameters and responses in the AWS documentation.

Now available
DeepSeek-V3.1 is now available in the US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Mumbai), Europe (London), and Europe (Stockholm) AWS Regions. Check the full Region list for future updates. To learn more, check out the DeepSeek in Amazon Bedrock product page and the Amazon Bedrock pricing page.

Give the DeepSeek-V3.1 model a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy