The growing adoption of Industrial Internet of Things (IIoT) technologies enables automated, real-time collection of manufacturing process data, unlocking new opportunities for data-driven product development. Current data-driven methods are generally applied within specific domains, such as design or manufacturing, with limited exploration of integrating design features and manufacturing process data. Since design decisions significantly affect manufacturing outcomes, such as error rates, energy consumption, and processing times, the lack of such integration restricts the potential for data-driven product design improvements. This paper presents a data-driven approach to mapping and analyzing the relationship between design features and manufacturing process data. A comprehensive system architecture is developed to ensure continuous data collection and integration. The linkage between design features and manufacturing process data serves as the basis for developing a machine learning model that enables automated design improvement suggestions. By integrating manufacturing process data with sustainability metrics, this approach opens new possibilities for sustainable product development.

The widespread adoption of Large Language Models (LLMs) has revolutionized AI deployment, enabling autonomous and semi-autonomous applications across industries through intuitive language interfaces and continuous improvements in model development. However, the attendant increase in autonomy and expansion of access permissions among AI applications also make these systems compelling targets for malicious attacks. Their inherent susceptibility to security flaws necessitates robust defenses, yet no known approaches can prevent zero-day or novel attacks against LLMs. This places AI protection systems in a category similar to established malware protection systems: rather than providing guaranteed immunity, they minimize risk through enhanced observability, multi-layered defense, and rapid threat response, supported by a threat intelligence function designed specifically for AI-related threats. Prior work on LLM protection has largely evaluated individual detection models rather than end-to-end systems designed for continuous, rapid adaptation to a changing threat landscape. We present a production-grade defense system rooted in established malware detection and threat intelligence practices. Our platform integrates three components: a threat intelligence system that turns emerging threats into protections; a data platform that aggregates and enriches information while providing observability, monitoring, and ML operations; and a release platform enabling safe, rapid detection updates without disrupting customer workflows. Together, these components deliver layered protection against evolving LLM threats while generating training data for continuous model improvement and deploying updates without interrupting production.

Data contracts formalize agreements between data producers and consumers regarding schema, semantics, and quality expectations. As data pipelines grow in complexity, manual authoring and maintenance of contracts becomes error-prone and labor-intensive. We present an AI-driven framework for automatic data contract generation using large language models (LLMs). Our system leverages parameter-efficient fine-tuning methods, including LoRA and PEFT, to adapt LLMs to structured data domains. The models take sample data or schema descriptions and output validated contract definitions in formats such as JSON Schema and Avro. We integrate this framework into modern data platforms (e.g., Databricks, Snowflake) to automate contract enforcement at scale. Experimental results on synthetic and real-world datasets demonstrate that the fine-tuned LLMs achieve high accuracy in generating valid contracts and reduce manual workload by over 70%. We also discuss key challenges such as hallucination, version control, and the need for continuous learning. This work demonstrates that generative AI can enable scalable, agile data governance by bridging the gap between intent and implementation in enterprise data management.

I recently had a conversation with a senior executive who had just landed at a new organization. He had been trying to gather new data insights but was frustrated at how long it was taking. After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current data architecture and technology stack. It was obvious that things had to change for the organization to be able to execute at speed in real time. Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence.

The excitement over generative AI--and AI in general--has reached the multi-trillion-dollar healthcare industry, driven by the news of ChatGPT passing the United States Medical Licensing Exam (USMLE) and the rapid introduction of new healthcare-related AI applications. Bill Gates, for example, is recommending the use of generative AI tools for primary diagnoses of patients. While acknowledging that AI will inevitably misdiagnose patients, Gates argues that the upside is worth it. Preventing misdiagnoses that can impact, at the very least, a patient's quality of life, depends a lot on the quality and availability of the health data that is fed into the AI model. The current excitement notwithstanding, the development of AI healthcare solutions has been severely constrained by the dearth of comprehensive and representative real-world health data.

Editor's note: Michael Sanky is global industry lead of healthcare and life sciences at Databricks, an enterprise software company. As pervasive health disparities in the U.S. continue to widen, data and artificial intelligence offer the potential to help close that gap. New technologies can analyze large, diverse data sets, informing the work of researchers, decision makers and policymakers across healthcare. If done correctly, AI can ultimately improve care delivery, advance proactive healthcare planning and predictive treatments, reduce clinician burnout and drive better patient outcomes. To this end, we've already seen groundbreaking advancements that move the needle in healthcare.

Welcome to our fabulous world. Creator of a unique model that disrupted the restaurant industry 15 years ago, we are now the leading dining platform across Europe and Australia. We are experiencing an exciting period of growth, and we need the greatest folks onboard. Together, we will make our wildest dreams come true! We strongly believe that our mission can only be achieved if we also bring happiness to our working environment.

Biotechnology is rewriting life as we know it, from the medicines we take, to the crops we grow, the materials we wear, and the household goods that we rely on every day. But moving at the new speed of science requires better technology. Benchling's mission is to unlock the power of biotechnology. The world's most innovative biotech companies use Benchling's R&D Cloud to power the development of breakthrough products and accelerate time to milestone and market. Come help us bring modern software to modern science.

At Recursion, we combine experimental biology, chemistry, automation and artificial intelligence to quickly and efficiently identify treatments for diseases. We generate a wide variety of data across different biological and chemical domains. Reporting to the Data Platforms Engineering Manager, the Data Analyst will develop solutions to ingest, model, transform and visualize this data, working closely with research scientists, biologists, chemists and data engineers. You will champion best practices and serve as a subject matter expert in data modeling, analytics, and visualization. Success in this role means the right visualizations and data is at the fingertips of the right people so they can make the right decisions and discover medicines that will change lives.

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable and secure payments network, enabling individuals, businesses and economies to thrive. When you join Visa, you join a culture of purpose and belonging – where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world – helping unlock financial access to enable the future of money movement.