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Walk away with practical approaches to designing robust evaluation frameworks that ensure AI systems are measurable, reliable, and deployment-ready. ExplainableAI for Decision-Making Applications Patrick Hall, Assistant Professor at GWSB and Principal Scientist at HallResearch.ai
True to its name, ExplainableAI refers to the tools and methods that explainAI systems and how they arrive at a certain output. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.
Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and MLEngineers seeking to build cutting-edge autonomous systems.
Machine Learning and NeuralNetworks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neuralnetworks gained popularity.
AI comprises Natural Language Processing, computer vision, and robotics. ML focuses on algorithms like decision trees, neuralnetworks, and support vector machines for pattern recognition. AIEngineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI.
Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among MLengineers, data scientists, and other stakeholders.
At that point, the Data Scientists or MLEngineers become curious and start looking for such implementations. Model Training : Embeddings enable neuralnetworks to consume training data in formats that extract features from the data. This is where embeddings come into play.
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