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When teams move from prototype AI projects to production-ready systems, they often discover the hard truth: shipping is only the beginning. Generative AI applications — whether voice agents, retrieval-augmented systems, or multi-step tool-calling agents — require robust evaluation, observability, and iteration to succeed at scale. Ian Cairns, co-founder and CEO of Freeplay, has seen this challenge firsthand.
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 Stress Testing FastAPI Application Build an optimized asynchronous machine learning application, then use Locust to stress test your app and determine if it is production-ready.
Evaluating Deep Learning models is an essential part of model lifecycle management. Whereas traditional models have excelled at providing quick benchmarks for model performance, they often fail to capture the nuanced goals of real-world applications. For instance, a fraud detection system might prioritize minimizing false negatives over false positives, while a medical diagnosis model might […] The post Evaluating Deep Learning Models with Custom Loss Functions and Calibration Metrics appe
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 How I Use AI Agents as a Data Scientist in 2025 And why data scientists must master AI agents before manual analysis becomes obsolete.
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.
Large language models (LLMs) have achieved impressive performance, leading to their widespread adoption as decision-support tools in resource-constrained contexts like hiring and admissions. There is, however, scientific consensus that AI systems can reflect and exacerbate societal biases, raising concerns about identity-based harm when used in critical social contexts.
SAS Viya now includes built-in bias mitigation in its machine learning procedures to help users develop ethical and trustworthy AI models by automatically detecting and reducing bias during training. The post Build without bias: Bias mitigation built-in with ML procedures in SAS Viya appeared first on SAS Blogs.
Apple researchers developed a method to train an open-source large language model, StarChat-Beta, to generate SwiftUI user interface code by creating a large synthetic dataset and iteratively refining it through automated feedback. The research, detailed in the paper “ UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback ,” addresses challenges faced by large language models (LLMs) in generating syntactically correct and well-designed u
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Apple researchers developed a method to train an open-source large language model, StarChat-Beta, to generate SwiftUI user interface code by creating a large synthetic dataset and iteratively refining it through automated feedback. The research, detailed in the paper “ UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback ,” addresses challenges faced by large language models (LLMs) in generating syntactically correct and well-designed u
This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. The Salesforce AI Platform Model Serving team is dedicated to developing and managing services that power large language models (LLMs) and other AI workloads within Salesforce. Their main focus is on model onboarding, providing customers with a robust infrastructure to host a variety of ML models.
Cohere, headquartered in Toronto, announced an oversubscribed $500 million funding round on Thursday, increasing its valuation to $6.8 billion from $5.5 billion a little over a year ago. The company, founded in 2019 by Aidan Gomez, a co-author of the foundational “Attention Is All You Need” paper, specializes in providing secure large language models (LLMs) for enterprise applications rather than consumer use.
Crazy Small Language Models 😉 CSLMs (Crazy Small Language Models) have arrived: you read it here first. ” D3: A Small Language Model for Drug-Drug Interaction prediction and comparison with Large Language Models ” introduces a 70 MILLION (yes, six zeros, not nine) Llama-like model that is on par with Llama-3.1 70B for drug interaction prediction. It was trained and fine-tuned from scratch in 2.5 hours on a single A100.
BALTIMORE — Building on its ongoing efforts to enrich the campus learning environment through the integration of artificial intelligence (AI), Morgan …
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!
Uploading is a fundamental process in our digital world, enabling users to transfer files and data from their devices to remote servers. This interaction shapes how we share information, collaborate on projects, and use online services. Understanding uploading is essential for anyone working with technology, whether for personal use or in a business setting.
Skip to main content CONTINUE TO SITE ➞ Dont miss tomorrows tech industry news Let CIO Dives free newsletter keep you informed, straight from your inbox. By signing up to receive our newsletter, you agree to our Terms of Use and Privacy Policy. You can unsubscribe at anytime. Deep Dive Opinion Library Events Press Releases Topics Sign up Search Sign up Search IT Strategy Cloud Security Big Data AI Software Leadership An article from Should CIOs care about new AI models?
Cookies help us display personalized product recommendations and ensure you have great shopping experience. Accept X By using this site, you agree to the Privacy Policy and Terms of Use. Accept Analytics Analytics Show More Turning Data Into Decisions: How Analytics Improves Transportation Strategy 3 Min Read How Data Analytics Improves Lead Management and Sales Results 9 Min Read How Data Analytics Reduces Truck Accidents and Speeds Up Claims 7 Min Read Interior Designers Boost Profits with Pre
AI hiring is going full steam ahead. In fact, AI-related job postings grew an impressive 38% between 2020 and 2024, according to LinkedIn's 2025 Future of Work Report. But did you know you don’t have to be a tech expert to break into this field?
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.
The landscape of artificial intelligence is rapidly evolving, and OpenAI’s Deep Research feature for ChatGPT marks a pivotal leap toward truly autonomous AI research agents. Unlike traditional chatbots or simple web-browsing tools, Deep Research empowers ChatGPT to independently plan , execute , and synthesize complex research tasks, delivering structured, cited reports that rival human analysts.
The MERN (MongoDB, Express, React, Node.js) stack is a popular JavaScript web development framework. The combination of technologies is well-suited for building scalable, modern web applications, especially those requiring real-time updates and dynamic user interfaces. Amazon Q Developer is a generative AI-powered assistant that improves developer efficiency across the different phases of the software development lifecycle (SDLC).
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.
In times when threats are lurking on the corners of the internet, cybersecurity should be at the top of business priorities. It is important to protect sensitive data and preserve trust with clients and customers. With increased enterprise size, new challenges arise in terms of security, and you need scalable solutions to protect from possible dangers Why an approach that scales with security matters Cyberattacks can affect businesses of all sizes; therefore, it is important to discuss the best
Want to start a startup? Get funded by Y Combinator. July 2013 One of the most common types of advice we give at Y Combinator is to do things that dont scale. A lot of would-be founders believe that startups either take off or dont. You build something, make it available, and if youve made a better mousetrap, people beat a path to your door as promised.
Operational technology (OT) is increasingly becoming vital in our interconnected world, playing a pivotal role in enhancing efficiency and safety across various sectors. Its integration with modern technology introduces new capabilities, allowing industries to monitor and control physical processes in real-time. As these systems evolve, they face unique challenges, particularly in cybersecurity, which is critical for maintaining trust and reliability in essential services.
In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
I/O, or Input/Output, is an essential aspect of computing that affects how data is shared and exchanged between computers and their external environments. It forms the backbone of communication in computer systems, enabling interactions that range from simple keystrokes to complex data transfers across networks. A solid grasp of I/O can significantly enhance understanding of computer architecture and performance.
Anthropic has extended its learning mode , initially for education users, to all Claude.ai users and developers, altering the chatbot’s interaction to guide users toward self-discovery rather than providing direct answers. First introduced in the spring, the learning mode modified Claude’s interaction style. When activated, the chatbot responded to questions by attempting to guide the user to formulate their own solution, rather than delivering an immediate, complete answer.
Skip to content Scott Jenson Exploring the world beyond mobile Menu Articles Most Popular Talks About August 10, 2025 Article The Timmy Trap This is Part 2 of my LLM series. In Part 1 , I discussed how in just a few short years, we went from the childlike joy of creating “Pirate Poetry” to the despair that our jobs would disappear. My main message was to relax a bit, as companies abuse the hype cycle to distort what is actually happening.
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.
Plus: the Trump administration reportedly wants a stake in Intel This is today's edition of The Download, our weekday newsletter that provides a daily …
Arcanix , a platform designed to streamline and enhance the live operations (LiveOps) of video games, has announced a significant milestone in its growth. The company has secured investment and accelerator support from ForsVC and imec.istart, a prominent European tech accelerator. This partnership is set to accelerate Arcanix’s product development, team expansion, and the onboarding of early partners.
Cookies help us display personalized product recommendations and ensure you have great shopping experience. Accept X By using this site, you agree to the Privacy Policy and Terms of Use. Accept Analytics Analytics Show More Turning Data Into Decisions: How Analytics Improves Transportation Strategy 3 Min Read How Data Analytics Improves Lead Management and Sales Results 9 Min Read How Data Analytics Reduces Truck Accidents and Speeds Up Claims 7 Min Read Interior Designers Boost Profits with Pre
Converting video content into written text is a valuable process that allows you to repurpose your media and expand its reach. Whether you’re looking to improve your blog’s content or make your videos more accessible, learning how to transcribe video to text so you can enhance your online presence and SEO. In this guide, we will explore how to convert video content into written text, highlighting the tools and methods you can use to streamline the process. 1.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
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