This is one of the most dynamic and fascinating topics: API technologies for machine learning.
It's always fun to build ML models. But how about serving them in the real world? In this episode I speak about three must-know technologies to place your model behind an API.
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This episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.
To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascience
If building software is your passion, you’ll love ThoughtWorks Technology Podcast. It’s a podcast for techies by techies. Their team of experienced technologists take a deep dive into a tech topic that’s piqued their interest — it could be how machine learning is being used in astrophysics or maybe how to succeed at continuous delivery.
Episode 21: Additional optimisation strategies for deep learning
Episode 20: How to master optimisation in deep learning
Episode 19: How to completely change your data analytics strategy with deep learning
Episode 18: Machines that learn like humans
Episode 17: Protecting privacy and confidentiality in data and communications
Episode 16: 2017 Predictions in Data Science
Episode 15: Statistical analysis of phenomena that smell like chaos
Episode 14: The minimum required by a data scientist
Episode 13: Data Science and Fraud Detection at iZettle
Episode 12: EU Regulations and the rise of Data Hijackers
Episode 11: Representative Subsets For Big Data Learning
Episode 10: History and applications of Deep Learning
Episode 9: Markov Chain Montecarlo with full conditionals
Episode 8: Frequentists and Bayesians
Episode 7: 30 min with data scientist Sebastian Raschka
Episode 6: How to be data scientist
Episode 5: Development and Testing Practices in Data Science
Episode 1: Predictions in Data Science for 2016
Episode 4: BigData on your desk
Episode 2: Networks and Graph Databases
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