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Explore Algorithmia's ML lifecycle management platform and learn to efficiently deploy models in production environments using GitHub Actions.
Explore neural-symbolic AI's potential for enhanced transfer learning, generalization, and creativity in AI applications like personalized medicine and robotics.
Explore challenges and emerging practices in responsible AI, including unintended consequences, model interpretability, fairness, and engineering objectives.
Explore how AI is shaping media experiences and learn strategies for implementing data-driven solutions in organizations to stay competitive in the streaming industry.
Explore ML model failure modes, analyze production performance, and set up pragmatic monitoring strategies to prevent degradation and breakage in production.
Explore data-driven approaches for financial time series modelling, performance metrics, and generative modelling techniques for market simulation.
Explore practical approaches to liability, fairness, and explainability in algorithmic decision-making with industry experts.
Explore cardiometabolic disease trends in Canada through Loblaw's advanced analytics, examining pharmaceutical data and health-geospatial insights for diabetes and cardiovascular diseases.
Explore how AI in banking adapted to the radical changes brought by the COVID-19 pandemic, including economic shifts and new work environments.
Explore the intersection of innovation, transportation, public engagement, and equity in future mobility systems.
Explore race as a concept, tool, and structure, examining its historical context and impact on data practices to build equity and dismantle systemic racism in technology.
Explore practical and legal aspects of applying machine learning in healthcare, including data challenges, algorithm development, and ethical considerations.
Predict and proactively resolve customer technical issues using machine learning and robotic process automation, improving service efficiency and customer satisfaction in telecom.
Explore strategies to overcome the cold start problem in AI, focusing on data aggregation and leveraging pre-trained models for efficient scaling of machine learning applications.
Explore creative strategies to gain stakeholder buy-in for ML projects in insurance, focusing on metrics that matter beyond traditional ROC scores.
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