Scientific Autonomy

One of the key attributes of successful teams is that they have deep expertise and practice in planning, modifying, training, tuning, and deploying machine learning models. This way they are in charge of their own destiny and can decide which tools, architectures, data sets, applications and deployment scenarios serve them best. It limits the dependency on third party experts and allows teams to assess the best solutions independently. Such AI competency can be acquired by extensive practice, by strategic hires, or through education. Among these choices, education is often the most effective strategy, as it is much more economical (and predictable) than expensive hires or time-consuming (and often equally costly) self-study and experimentation. This creates the need for team training.

Watch

Our cofounders, Mu Li and Alex Smola have an extensive track record of teaching AI to generations of students.

They have taught classes as faculty at Carnegie Mellon University, Stanford, UC Berkeley, and the Australian National University.
Watch some of the courses as they are available on Bilibili and YouTube
In the past, our cofounders organized a number of Machine Learning Summer Schools, involving 2 week training events for hundreds of students.
While at Amazon, Alex Smola was in charge of Amazon's Machine Learning University. Using teaching materials from his team, the Machine Learning University trained over 20,000 Amazon software engineeers per year in practical machine learning and AI.

Read

Our founders didn't just research in machine learning, the wrote the book on it. As a matter of fact, they wrote a total of 6 books on machine learning. Here's a list of them.

A. Zhang, Z. Lipton, M. Li, A.J. Smola, Dive into Deep Learning, 2019.
G. Bakir, T. Hofmann, B. Schölkopf, A.J. Smola, B. Taskar, and S.V.N. Vishwanathan, editors. Predicting Structured Data. MIT Press, Cambridge, MA, 2006.
S. Mendelson and A. J. Smola, editors. Machine Learning, Proceedings of the Summer School 2002, Australian National University, volume 2600 of Lecture Notes in Computer Science. Springer, 2003.
B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2002.
B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors. Advances in Kernel Method – Support Vector Learning. MIT Press, Cambridge, MA, 1999.
A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors. Advances in Large Margin Classifiers. MIT Press, Cambridge, MA, 2000.

Code

Our team has an extensive track record of building open source tools for machine learning. This includes building AutoGluon, the leading AutoML Tool, Apache MXNet, a deep learning framework, the D2L Toolkit, and DGL, a graph neural networks library.

Boson AI can help you train your team to train, tune and deploy models at scale and to help your team succeed in using AI.

Ready to Upskill Your Team?

Our expert instructors can provide customized training programs for your technical teams. Contact us today to discuss how we can help develop your team's AI and machine learning capabilities.

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Team Training

Key Features