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ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Are humans really at risk from artificial intelligence? Will there be a rise of the machines, and if so, is that a good thing? UNSW's Professor Toby Walsh, an expert in AI and one of Australia's rock stars of the digital revolution, dispels some myths about AI and tells us what we should really be worried about. From UNSW's UNSOMNIA event.
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A VR game for experiencing the life of a transgender boy
Recorded: Sept 15th , 2017
Life 3.0 Professor Max Tegmark talked about his book Life 3.0: Being Human in the Age of Artificial Intelligence, in which he examines the concept of intelligence and what questions should be asked in light of the advancement of artificial intelligence.
We are in the third-generation business, an era that requires to reimagine the business and work.
Paul Daugherty, Chief Technology & Innovation Officer di Accenture - author of the book “Human+Machine - Reimagining Work in the Age of AI” - explains how we are moving towards a new "collaborative intelligence" Era, where human skills like perception, creativity, communication embrace machine skills like prediction, repetition, memorization