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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 AI-Powered Feature Engineering with n8n: Scaling Data Science Intelligence Generate strategic feature engineering recommendations using AI-powered workflows in n8n.
Generative AI is profoundly revolutionizing the creation of training data through synthetic datasets, addressing long-standing challenges in AI development and redefining what is possible in artificial intelligence. This innovation provides a transformative alternative to traditional data collection methods, which are often costly, time-consuming, and fraught with privacy and ethical concerns.
About Song Han News Publications Blog Course Awards Talks Media Team Gallery Efficient AI Computing, Transforming the Future. How Attention Sinks Keep Language Models Stable Guangxuan Xiao August 7, 2025 TL;DR We discovered why language models catastrophically fail on long conversations: when old tokens are removed to save memory, models produce complete gibberish.
One of the fastest-growing areas of technology is machine learning, but even seasoned professionals occasionally stumble over new terms and jargon. It is simple to get overwhelmed by the plethora of technical terms as research speeds up and new architectures, loss functions, and optimisation techniques appear. This blog article is your carefully chosen reference to […] The post 50+ Must-Know Machine Learning Terms You (Probably) Haven’t Heard Of appeared first on Analytics Vidhya.
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.
Microsoft is integrating OpenAI’s open-weight language models, gpt-oss, into Azure AI Foundry and Windows AI Foundry, broadening its AI toolset. This expansion introduces gpt-oss-120b and gpt-oss-20b models to personal computers. The gpt-oss-120b model is designed for high-performance reasoning applications. Conversely, the gpt-oss-20b model operates on personal computers equipped with graphics processing units possessing a minimum of 16 gigabytes of memory.
Skip to Content MIT Technology Review Featured Topics Newsletters Events Audio Sign in Subscribe MIT Technology Review Featured Topics Newsletters Events Audio Sign in Subscribe Artificial intelligence Five ways that AI is learning to improve itself From coding to hardware, LLMs are speeding up research progress in artificial intelligence. It could be the most important trend in AI today.
Discover a comprehensive collection of cheat sheets covering Docker commands, mathematics, Python, machine learning, data science, data visualization, CLI commands, and more.
Discover a comprehensive collection of cheat sheets covering Docker commands, mathematics, Python, machine learning, data science, data visualization, CLI commands, and more.
Jump to Content Research Research Who we are Back to Who we are menu Defining the technology of today and tomorrow. Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more Philosophy People Our researchers drive advancements in computer science through both fundamental and applied research.
Leveraging machine learning (ML) for recycling analytics is no longer a hypothetical or risky investment. Several recent breakthroughs have proven it is effective, suggesting it could become an industry staple. It may lead to innovative developments that reshape how people approach recycling. What can you learn from these early adopters? The Need for More Efficient Recycling Analytics Recycling analytics is complex.
In Part 1 of our series, we established the architectural foundation for an enterprise artificial intelligence and machine learning (AI/ML) configuration with Amazon SageMaker Unified Studio projects. We explored the multi-account structure, project organization, multi-tenancy approaches, and repository strategies needed to create a governed AI development environment.
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
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!
This article introduces and discusses four key reasons why data visualization is essential in data storytelling: simplifying complex information, discovering hidden patterns, fostering engagement and impact, and supporting informed decisions.
Graph rag is rapidly emerging as the gold standard for context-aware AI, transforming how large language models (LLMs) interact with knowledge. In this comprehensive guide, we’ll explore the technical foundations, architectures, use cases, and best practices of graph rag versus traditional RAG, helping you understand which approach is best for your enterprise AI, research, or product development needs.
In time series analysis and forecasting , transforming data is often necessary to uncover underlying patterns, stabilize properties like variance, and improve the performance of predictive models.
Amazon SageMaker Unified Studio represents the evolution towards unifying the entire data, analytics, and artificial intelligence and machine learning (AI/ML) lifecycle within a single, governed environment. As organizations adopt SageMaker Unified Studio to unify their data, analytics, and AI workflows, they encounter new challenges around scaling, automation, isolation, multi-tenancy, and continuous integration and delivery (CI/CD).
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.
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
Explore 10 agentic AI terms and concepts that are key to understanding the latest AI paradigm everyone wants to talk about — but not everyone clearly understands.
Traditionally, much of artificial intelligence (AI) and machine learning (ML) has been focused on the models themselves. How big the model is, how fast they are and how accurate they can be made. In the ever-evolving landscape of AI, this mindset has begun to shift to the possibility that it is the data – and not the model – that is being used as the foundation for success.
OpenAI has released GPT-OSS 12B and 20B, open-source AI models that enable local operation on personal computers. This development offers enhanced privacy and control. OpenAI’s GPT-OSS 12B and GPT-OSS 20B are reasoning models. They facilitate advanced AI capabilities on local systems, enhancing privacy, speed, and user control. GPT-OSS 20B is optimized for high-end consumer hardware, while GPT-OSS 12B is for professional-grade systems with more powerful GPUs.
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.
Device-directed speech detection (DDSD) is a binary classification task that separates the user’s queries to a voice assistant (VA) from background speech or side conversations. This is important for achieving naturalistic user experience. To this end, we propose knowledge distillation (KD) to enhance DDSD accuracy while ensuring efficient deployment.
Tired of spending hours on repetitive data tasks? These Python scripts can come in handy for the overworked data scientist looking to simplify daily workflows.
The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and cloud computing offer low-cost, scalable, and on-demand computational resources, enabling service providers to deliver intelligent data analytics and real-time insights to end-users.
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.
OpenAI models have transformed the landscape of artificial intelligence, redefining what’s possible in natural language processing, machine learning, and generative AI. From the early days of GPT-1 to the groundbreaking capabilities of GPT-5 , each iteration has brought significant advancements in architecture, training data, and real-world applications.
Executive Summary Effective AI governance frameworks are essential for managing the lifecycle of AI models, addressing transparency gaps, monitoring bias and drift, and adapting to evolving regulatory demands. Key practices include centralized model registries, automated compliance workflows, continuous monitoring, standardized templates, and cross-functional collaboration.
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 Agentic AI Hands-On in Python: A Video Tutorial Introducing a four-hour video workshop on agentic AI engineering from Jon Krohn and Edward Donner.
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
Happy to share my new GitHub project: “ An Amazon SageMaker Container for Hugging Face Inference on AWS Graviton ”. ✅ Based on a clean source build of llama.cpp ✅ Native integration with the SageMaker SDK and with Graviton3/Graviton4 instances ✅ Model deployment from the Hugging Face hub or an Amazon S3 bucket ✅ Deployment of existing GGUF models ✅ Deployment of safetensors models, with automatic GGUF conversion and quantization ✅ Support for OpenAI API ✅ Support for streaming and non-streaming
In the world of data engineering, the most impactful work is often the least glamorous. At ODSC East, Veronika Durgin, VP of Data at Saks, struck a chord with her talk on the “10 Most Neglected Data Engineering Tasks.” Drawing from decades of experience in data architecture, engineering, and analytics, she emphasized the foundational practices that keep pipelines stable, teams agile, and businesses prepared for rapid technological change.
Microsoft has developed Project Ire, an AI prototype that can autonomously reverse engineer software to identify malware, a task typically performed by human security researchers. The prototype can fully reverse engineer software without prior clues about its origin or purpose. In a Microsoft test, Project Ire accurately identified 90% of malicious Windows driver files , flagging only 2% of benign files as dangerous.
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.
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