In this episode from a stellar TWIMLcon panel, the state and future of larger, more established brands are analyzed and discussed.
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Machine learning and AI is finding a place in the traditional enterprise – although the path to get there is a bit different. Hear from Amr Awadallah, Founder and Global CTO of Cloudera, Pallav Agrawal, Director of Data Science at Levi Strauss & Co., and Jürgen Weichenberger, Data Science Senior Principal & Global AI Lead at Accenture, moderated by Josh Bloom Professor at UC Berkeley. For an ML/AI initiative to be successful, you have to start with the right people, and that means the hiring process and culture need to be relevant and appealing to those you are trying to attract. In traditional enterprises, this often means a conscious and noticeable shift in how things used to be managed while educating cross-functional teams in data science terms and methodologies. Once you are set up to work on projects, new challenges arise. With the new age of data science, it can be tempting and exciting to constantly be trying out the latest technologies. Hear from these experts on why brand consistency and sustainability is imperative to success. Build v buy has become an age old question, but the real business value – the money – can be found by putting your big ML/AI goals and projects in the core competencies of the company. The panel also delves into the larger question of traditional enterprises fundamentally changing their business through ML/AI, and if so, why? With environmental sustainability pressure from customers and the broader society, and the applications of ML/AI more accessible than ever, even the most traditional companies are making changes in how they run their business. Hearing from this range of viewpoints and industries, this episode is full of real world examples and thought-provoking ideas for scaling ML/AI in the traditional enterprise.
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“More On That Later” by Lee Rosevere licensed under CC By 4.0