This presentation was recorded at GOTO Amsterdam 2022. #GOTOcon #GOTOams
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Joop Snijder - CTO at Aigency @JoopSnijder
Willem Meints - Microsoft MVP & Chief AI Architect at Aigency @willemmeints3123

ABSTRACT
Nowadays, developers can easily create machine learning models with AutoML. But when you make models, you also get associated responsibilities. For example, a machine learning model is only complete if you explain the outcome. But how do you do that? And to whom do you explain? And why should you be able to do this as a Developer? In this session, you will learn why Explainable AI is an essential skill for a Developer to master when training machine learning models.

In the first part of the session, you will learn why Explainable AI is important from a business perspective and why you need to know this as a Developer.

In the second part of this session, you’ll learn how to explain machine learning models in practice. We’ll dive in-depth into tooling to explain machine learning models and show you how to interpret these explanations. [...]

TIMECODES
00:00 Intro
02:34 Explain ML models
02:45 AutoML
06:10 Demo
10:37 AutoML (continued)
14:22 Why explainable AI?
19:58 Demo
20:54 Why explainable AI? (continued)
25:10 Segmentation
30:42 Takeaways
31:13 Outro

Read the full abstract here:
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RECOMMENDED BOOKS
Phil Winder • Reinforcement Learning • [ Ссылка ]
Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • [ Ссылка ]
Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • [ Ссылка ]
Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • [ Ссылка ]
Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • [ Ссылка ]
Aurélien Géron • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow • [ Ссылка ]

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#ExplainableAI #AI #ML #ArtificialIntelligence #MachineLearning #AutoML #DataScience

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