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Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
Small language models are rapidly transforming the landscape of artificial intelligence, offering a powerful alternative to their larger, resource-intensive counterparts. As organizations seek scalable, cost-effective, and privacy-conscious AI solutions, small language models are emerging as the go-to choice for a wide range of applications. In this blog, we’ll explore what small language models are, how they work, their advantages and limitations, and why they’re poised to shape the next wave
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Why Python Pros Avoid Loops: A Gentle Guide to Vectorized Thinking Loops are easy to write, but vectorized operations are the secret to writing efficient and elegant Python code.
The second week of the Agentic AI Summit built upon week 1 by diving deeper into the engineering realities of agentic AI — from protocol-level orchestration to agent deployment inside enterprise environments and even developer IDEs. Leaders from Monte Carlo, TrueFoundry, LlamaIndex, TripAdvisor, and more shared how they’re moving from prototypes to production, surfacing the tools, patterns, and challenges they’ve encountered along the way.
Speaker: Jason Chester, Director, Product Management
In today’s manufacturing landscape, staying competitive means moving beyond reactive quality checks and toward real-time, data-driven process control. But what does true manufacturing process optimization look like—and why is it more urgent now than ever? Join Jason Chester in this new, thought-provoking session on how modern manufacturers are rethinking quality operations from the ground up.
TL;DR Multimodal AI at scale demands more than fast hardware—it requires a fundamentally different architecture. Vespa AI brings compute to the data, enabling real-time performance across text, images, and video. Companies like Spotify, Perplexity, and Vinted rely on Vespa to power search, recommendations, and RAG at global scale. Tensor-based retrieval and hybrid ranking strategies make Vespa uniquely capable of supporting complex multimodal use cases.
AI chips are revolutionizing the way we approach complex computations across various domains. They are not just the products of technological advancement; they also open doors to unprecedented opportunities in fields such as machine learning and natural language processing. By providing specialized hardware designed for AI workloads, these chips improve efficiency and performance, allowing for rapid advancements in artificial intelligence applications.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Top Skills Data Scientists Should Learn in 2025 Forget what you knew — these underrated data science skills will define who wins for the rest of 2025.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Top Skills Data Scientists Should Learn in 2025 Forget what you knew — these underrated data science skills will define who wins for the rest of 2025.
The quality of data used is the cornerstone of any data science project. Bad quality of data leads to erroneous models, misleading insights, and costly business decisions. In this comprehensive guide, we’ll explore the construction of a powerful and concise data cleaning and validation pipeline using Python. What is a Data Cleaning and Validation Pipeline?
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 7 Must-Know Machine Learning Algorithms Explained in 10 Minutes Get up to speed with the 7 most essential machine learning algorithms.
TL;DR – What you’ll learn Why lakehouses combine the flexibility of data lakes with the governance and performance of warehouses to cut friction in AI adoption. How modern file formats (Iceberg, Delta Lake) and open object storage enable real-time analytics, schema management, and engine interoperability. Practical strategies for governance, cost control, migration, and early wins in your lakehouse journey.
Large reasoning models (LRMs) represent an exciting evolution in artificial intelligence, combining the prowess of natural language processing with advanced reasoning techniques. Their ability to analyze and interpret complex prompts effectively allows them to excel in solving intricate problems across various domains, making them essential for tasks that require more than simple text generation.
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!
Skip to main content Rust GPU Docs Blog Ecosystem Changelog GitHub Recent posts 2025 Rust running on every GPU Porting GPU shaders to Rust 30x faster with AI Rust CUDA May 2025 project update Shadertoys ported to Rust GPU Rust CUDA project update Rust running on every GPU July 25, 2025 · 19 min read Christian Legnitto Rust GPU and Rust CUDA maintainer Ive built a demo of a single shared Rust codebase that runs on every major GPU platform: CUDA for NVIDIA GPUs SPIR-V for Vulkan-compatible GPUs fr
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
How do LLMs work? It’s a question that sits at the heart of modern AI innovation. From writing assistants and chatbots to code generators and search engines, large language models (LLMs) are transforming the way machines interact with human language. Every time you type a prompt into ChatGPT or any other LLM-based tool, you’re initiating a complex pipeline of mathematical and neural processes that unfold within milliseconds.
This post is divided into four parts; they are: • Why Attnetion Matters: Limitations of Basic Seq2Seq Models • Implementing Seq2Seq Model with Attention • Training and Evaluating the Model • Using the Model Traditional seq2seq models use an encoder-decoder architecture where the encoder compresses the input sequence into a single context vector, which the decoder then uses to generate the output sequence.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Businesses constantly generate unstructured data like emails, reports, customer chats, and social media posts. Because it doesn’t follow a fixed format, this data type is often challenging to organize, analyze, or use effectively with traditional tools. Large language models , a form of AI trained on vast collections of text, are changing that. With their ability to understand and generate human language, LLMs give organizations new ways to unlock insights, automate processes, and helping with m
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Setting Up a Machine Learning Pipeline on Google Cloud Platform Learn the steps for setting up the machine learning pipeline in the top cloud provider.
TL;DR – What you’ll learn Highlights of AI‑powered agent and model innovations from Snowflake, Google Cloud, Precisely, Zenlytic, Fivetran, Zerve, and Dataiku. How structured & unstructured data meet generative AI in scalable platforms—with demos across Cortex Agents, Gemini models, AI storytelling, data orchestration, and flexible agent workflows.
Qwen3 Coder is quickly emerging as one of the most powerful open-source AI models dedicated to code generation and software engineering. Developed by Alibaba ’s Qwen team, this model represents a significant leap forward in the field of large language models (LLMs). It integrates an advanced Mixture-of-Experts (MoE) architecture , extensive reinforcement learning post-training, and a massive context window to enable highly intelligent, scalable, and context-aware code generation.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 10 Free Online Courses to Master Python in 2025 How can you master Python for free? Here are ten online courses we recommend.
AI browsers are setting a new standard in how we explore the web, bringing the power of artificial intelligence directly to our browsing experience. With capabilities that go far beyond traditional web browsers, these innovative tools are reshaping the way users interact with online content. AI browsers leverage advanced technologies like natural language processing and web automation to deliver tailored search results and assist users in navigating vast amounts of information efficiently.
Alzheimer’s disease is a progressive neurological disorder that profoundly affects cognitive functions and daily activities. Rapid and precise identification is essential for effective intervention and improved patient outcomes. This research introduces an innovative hybrid filtering approach with a deep transfer learning model for detecting Alzheimer’s disease utilizing brain imaging data.
Computer vision is a dynamic branch of AI that enables machines to interpret and extract insights from visual inputs like images and video. It underpins technologies such as autonomous vehicles, facial recognition systems, medical image diagnostics, and automated retail checkout. Common tasks in computer vision include image classification, object detection, semantic segmentation, and facial recognition.
Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.
As datasets grow and the need for machine learning (ML) solutions expands, scaling ML pipelines presents increasing complexities. Feature engineering can become time-consuming, model training can take longer, and the demands of managing computational infrastructure can all be blockers for business requirements. Snowflake AI Data Cloud addresses these challenges by providing ML Objects on its unified platform, allowing ML workflows to scale efficiently.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Benefits of Using LiteLLM for Your LLM Apps In this article, we will explore why LiteLLM is beneficial for building LLM applications.
While advanced AI systems known as large reasoning models (LRMs) have demonstrated impressive performance on complex problem-solving benchmarks, their true reasoning capabilities may be overestimated by current evaluation methods. According to a recent article by Sajjad Ansari, a novel multi-problem stress-testing framework reveals that even state-of-the-art models struggle under more realistic conditions.
← Back to main Do Variable Names Matter for AI Code Completion? When GitHub Copilot suggests your next line of code, does it matter whether your variables are named current_temperature or just x ? I ran an experiment to find out, testing 8 different AI models on 500 Python code samples across 7 naming styles. The results suggest that descriptive variable names do help AI code completion.
Speaker: Chris Townsend, VP of Product Marketing, Wellspring
Over the past decade, companies have embraced innovation with enthusiasm—Chief Innovation Officers have been hired, and in-house incubators, accelerators, and co-creation labs have been launched. CEOs have spoken with passion about “making everyone an innovator” and the need “to disrupt our own business.” But after years of experimentation, senior leaders are asking: Is this still just an experiment, or are we in it for the long haul?
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, while useful, often focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes. This lack of granularity makes it difficult to deeply discern where failures stem from.
TL;DR Many enterprises stall on data democratization because of fragmented systems, lack of governance, and cultural inertia—not because of technology. A hybrid organizational model (hub and spoke) combined with role-based access and semantic layers can help scale responsible access. Data quality, usability, and literacy programs are just as critical as tools for enabling non-technical users to make informed decisions.
Random numbers are a fascinating aspect of mathematics and computer science, often playing a crucial role in applications like cryptography, statistical analysis, and computer simulations. This article explores the intricacies of random numbers, their characteristics, and methods of generation, as well as their diverse applications and the challenges associated with achieving true randomness.
Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
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