This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
As AIengineers, crafting clean, efficient, and maintainable code is critical, especially when building complex systems. For AI and large language model (LLM) engineers , design patterns help build robust, scalable, and maintainable systems that handle complex workflows efficiently. forms, REST API responses).
The Rise of AIEngineering andMLOps 20182019: Early discussions around MLOps and AIengineering were sparse, primarily focused on general machine learning best practices. 20232024: AIengineering became a hot topic, expanding beyond MLOps to include AI agents, autonomous systems, and scalable model deployment techniques.
With the rapid advance of AI across industries, responsibleAI has become a hot topic for decision-makers and data scientists alike. But with the advent of easy-to-access generative AI, it’s now more important than ever. There are several reasons why responsibleAI is critical as the technology continues to advance.
The team paused using the AI tool to build better editorial processes and has committed to refining it to suit their editorial standards and needs. Example of a CNET AI disclaimer The tech publication now updates an AI Policy page detailing how they’re using AI.
Though there are great potential benefits, it is important to ensure this technology is developed and used responsibly. In her keynote speech at ODSC West, Sarah Bird, Global Lead for ResponsibleAIEngineering at Microsoft, discussed Microsoft’s journey in building and using generative AIresponsibly.
Sarah Bird, PhD | Global Lead for ResponsibleAIEngineering | Microsoft — Read the recap here! Jepson Taylor | Chief AI Strategist | Dataiku Thomas Scialom, PhD | Research Scientist (LLMs) | Meta AI Nick Bostrom, PhD | Professor, Founding Director | Oxford University, Future of Humanity Institute — Read the recap here!
Webinars Under our Ai+ Training platform, we host playlists of past webinars that we’ve held with Microsoft. Here, we have several different playlists, including machine & deep learning , NLP , responsibleAI, model explainability, and other miscellaneous data science topics.
Artificial intelligence (AI) continues to transform industries, offering groundbreaking capabilities while presenting unique challenges. In a recent episode of ODSCs AiX Podcast , Cal Al-Dhubaib, Head of AI and Data Science at Further and a prominent advocate for responsibleAI, shared invaluable insights from his journey.
Topics Include: Advanced ML Algorithms & EnsembleMethods Hyperparameter Tuning & Model Optimization AutoML & Real-Time MLSystems ExplainableAI & EthicalAI Time Series Forecasting & NLP Techniques Who Should Attend: ML Engineers, Data Scientists, and Technical Practitioners working on production-level ML solutions.
Robotics also witnessed advancements, with AI-powered robots becoming more capable in navigation, manipulation, and interaction with the physical world. ExplainableAI and Ethical Considerations (2010s-present): As AI systems became more complex and influential, concerns about transparency, fairness, and accountability arose.
Grok-1 Grok-1 is a tool designed to make AI more explainable, addressing the need for transparency in AI-driven decision-making. It uses interpretability techniques to provide insights into the factors influencing AI predictions, helping identify biases and ensure fair and accountable AI operation.
AI & NLP Day 2021 Date: September 23-24h Place: Online Ticket: 399-1,599 PLN The next AI event is a little more focused, placing the lion’s share of its attention on natural language programming. If you walk in political circles or need to tackle complex economic questions, this is the conference for you.
No-code/low-code integration is essential for AI adoption at scale because it democratizes access to AI, enabling domain experts and business leaders to operationalize AI without requiring dedicated AIengineers. How does AI Squared ensure responsibleAI deployment? Whats next for AI Squared?
Additionally, we discuss the design from security and responsibleAI perspectives, demonstrating how you can apply this solution to a wider range of industry scenarios. To better understand the solution, we use the seven steps shown in the following figure to explain the overall function flow. The cache is also updated.
We organize all of the trending information in your field so you don't have to. Join 15,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content