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Explore ethical AI design principles and learn to create purposeful, responsible machine learning systems for real-world applications.
Explore ML model governance in finance through insights from 66 global institutions, focusing on key practices and regulatory considerations.
Explore AI-driven economic policy design using deep reinforcement learning and simulations. Learn how AI can improve equality, productivity, and public health outcomes in complex socio-economic challenges.
Explore adversarial methods, multi-task learning, and the effectiveness of fine-tuning large language models on small datasets in NLP. Gain insights into improving pre-trained representations.
Explore retail AI demand prediction models, KPIs, and implementation strategies for effective business use cases in the retail industry.
Explore advanced techniques for detecting AI-generated misinformation using multimedia knowledge extraction and graph neural networks. Gain insights into fine-grained falsified information analysis.
Overcome MLOps challenges in production inference workloads. Learn strategies for improving throughput, reducing latency, and maximizing GPU utilization in AI deployments.
Explore key ML, DL, RL, and Graph Analytics use cases in Retail, with a demo of Microsoft Azure AI platform's MLOps capabilities in a Retail environment.
Explore distance metric learning for mutual fund categorization, resolving paradoxes in previous research and improving accuracy in identifying similar funds for various applications.
Explore ML-based time series regression for demand forecasting in retail. Learn to optimize replenishment and pricing using AI for accurate predictions and automated decision-making at scale.
Explore AI-driven retail promotion planning, focusing on monotonicity and smoothness in demand estimation. Learn about Kinaxis' Optimize product and model-agnostic methodologies for consistent promotions.
Explore efficient XVA calculation using deep learning, GPU computation, and AAD for computationally intensive finance models.
Supercharge ML experimentation with PyTorch Lightning and DVC, and streamline production using Kubeflow. Learn to overcome challenges in complex ML model development and deployment.
Discover strategies for integrating domain experts, data officers, and annotators to create robust Health AI solutions and improve healthcare outcomes.
Explore cloud-based solutions to overcome AI implementation challenges in healthcare, addressing infrastructure gaps and enabling real-world impact on patient care.
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