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12 Can’t-Miss Hands-on Training & Workshops Coming to ODSC East 2025

ODSC - Open Data Science

Walk away with practical approaches to designing robust evaluation frameworks that ensure AI systems are measurable, reliable, and deployment-ready. Explainable AI for Decision-Making Applications Patrick Hall, Assistant Professor at GWSB and Principal Scientist at HallResearch.ai

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Explainable AI (XAI): The Complete Guide (2024)

Viso.ai

True to its name, Explainable AI refers to the tools and methods that explain AI 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.

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Introducing the Topic Tracks for ODSC East 2025: Spotlight on Gen AI, AI Agents, LLMs, & More

ODSC - Open Data Science

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 ML Engineers seeking to build cutting-edge autonomous systems.

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Where AI is headed in the next 5 years?

Pickl AI

Machine Learning and Neural Networks (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 neural networks gained popularity.

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2024 Tech breakdown: Understanding Data Science vs ML vs AI

Pickl AI

AI comprises Natural Language Processing, computer vision, and robotics. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. AI Engineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

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 ML engineers, data scientists, and other stakeholders.

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ML Pipeline Architecture Design Patterns (With 10 Real-World Examples)

The MLOps Blog

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Model Training : Embeddings enable neural networks to consume training data in formats that extract features from the data. This is where embeddings come into play.

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