The capabilities of Machine Learning and Data Science algorithms are improving very quickly and promise to positively impact many sectors of our lives. However, a lot of this potential is hard to realize in practice. Large engineering organizations can afford to build their own Machine Learning systems that allow them to develop and exploit these algorithms for real-world problems. However, small and medium-sized companies struggle to move past the proof-of-concept stage and find the right tools and processes to develop, deploy and monitor ML-centered products reliably. Challenges revolve around product definition, data collection, training in the low-data regime, tracking and operations, reliable deployment, and ethical implications. 

    Giacomo will review these challenges and a few strategies for small or medium-sized organizations to fully realize the potential of ML in real-world applications.

Instructor's Bio

Giacomo Vianello

Principal Data Scientist at Cape Analytics

Giacomo is a data scientist with a passion for state-of-the-art but practical technical solutions. He is Principal Data Scientist at Cape Analytics, a Silicon Valley startup bringing cutting-edge data solutions to the insurance and real estate industries, where he specializes in developing AI systems to extract intelligence from geospatial imagery. In addition, he teaches Deep Learning and Machine Learning Operations for Udacity and is a technical advisor for two stealth-phase startups. Before Cape Analytics, he spent almost 10 years at Stanford University as a research scientist applying machine learning to big astrophysical data from NASA. He is an accomplished speaker with more than 20 invited public talks and has authored over 100 journal publications.


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    ON-DEMAND WEBINAR: "Machine Learning for the 99%"

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