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The Python feature nobody talks about (but every dev should use).
The 7 emerging application patterns you should know
The post Boosting Your Anomaly Detection With LLMs appeared first on Towards Data Science.
Popular small language models are reshaping the AI landscape by combining efficiency, strong reasoning, factual accuracy, tool use, and broad accessibility.
This article provides a summary of and commentary on the recent paper
How to stand out in a crowded field
The post The Programming Skills You Need for Today’s Data Roles appeared first on Towards Data Science.
Earlier this summer, I walked through the glassy lobby of a fancy office in London, into an elevator, and then along a corridor into a clean, carpeted room. Natural light flooded in through its windows, and a large pair of umbrella-like lighting rigs made the room even brighter. I tried not to squint as I…
The security landscape is undergoing yet another major shift, and nowhere was this more evident than at Black Hat USA 2025. As artificial intelligence (especially the agentic variety) becomes deeply embedded in enterprise systems, it’s creating both security challenges and opportunities. Here’s what security professionals need to know about this rapidly evolving landscape. AI systems—and […]
Bring time to a standstill in your Python tests
The post Useful Python Libraries You Might Not Have Heard Of: Freezegun appeared first on Towards Data Science.
What’s the state of AI in business these days, and how much does it cost us?
The post AI FOMO, Shadow AI, and Other Business Problems appeared first on Towards Data Science.
This is how to model rare events occurrences in a time series in a few lines of code
The post Hands On Time Series Modeling of Rare Events, with Python appeared first on Towards Data Science.
Atlas, Boston Dynamics’ dancing humanoid, can now use a single model for walking and grasping—a significant step toward general-purpose robot algorithms.
I’m really looking forward to our second O’Reilly AI Codecon, Coding for the Agentic World, which is happening on September 9, online from 8am to noon Pacific time, with a follow-on day of additional demos on September 16. But I’m also looking forward to how the AI market itself unfolds: the surprising twists and turns […]
The Ornstein-Uhlenbeck process in Python
The post Stochastic Differential Equations and Temperature — NASA Climate Data pt. 2 appeared first on Towards Data Science.
In this post, we showed how to implement TTI authentication for Amazon Q data accessors. We covered the setup process for both ISVs and enterprises and demonstrated how TTI authentication simplifies the user experience while maintaining security standards.
Proofpoint has redefined its professional services by integrating Amazon Q Business, a fully managed, generative AI powered assistant that you can configure to answer questions, provide summaries, generate content, and complete tasks based on your enterprise data. In this post, we explore how Amazon Q Business transformed Proofpoint’s professional services, detailing its deployment, functionality, and future roadmap.
In this post, we show how to deploy Coveo’s Passage Retrieval API as an Amazon Bedrock Agents action group to enhance response accuracy, so Coveo users can use their current index to rapidly deploy new generative experiences across their organization.
This article will explore nano-banana's ability to generate and edit images.
Learn more about how to use Google’s new Pixel Camera feature, Camera Coach.
System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.
Discover the steps you need to kickstart your journey as a machine learning engineer in today’s AI-driven world.
What I learned about growth, visibility, and chaos over the past five years
The post What Being a Data Scientist at a Startup Really Looks Like appeared first on Towards Data Science.
Your first RAG project doesn’t have to be basic. Here’s how to make it awesome from day one.
Developing machine learning systems entails a well-established lifecycle, consisting of a series of stages from data preparation and preprocessing to modeling, validation, deployment to production, and continuous maintenance.
Reasoning models step in, the AI brain drain worsens, shopper shortcut, and more...
Key lessons I’ve learned running RabbitMQ + Celery in production
The post A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your Queues appeared first on Towards Data Science.
In this post, we demonstrate how to use the new Amazon SageMaker HyperPod CLI and SDK to streamline the process of training and deploying large AI models through practical examples of distributed training using Fully Sharded Data Parallel (FSDP) and model deployment for inference. The tools provide simplified workflows through straightforward commands for common tasks, while offering flexible development options through the SDK for more complex requirements, along with comprehensive observability features and production-ready deployment capabilities.
Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.
Julius Caesar was a Roman ruler known for his military strategies and excellent leadership. Named after him, the Caesar Cipher is a fascinating cryptographic technique that Julius Caesar employed to send secret signals and messages to his military personnel. The Caesar Cipher is quite basic in its working. It works by shifting all the letters […]
The post Implementing the Caesar Cipher in Python appeared first on Towards Data Science.
Optimize your AI search with RAG, contextual retrieval and evaluations
The post How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques appeared first on Towards Data Science.
How independently trained transformers form same the neurons
The post What is Universality in LLMs? How to Find Universal Neurons appeared first on Towards Data Science.