Microsoft:deployment, indico: model adapting , Oriel Research Therap: AML&ML

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Madison May - ML Architect - indico data
Madison is the Machine Learning Architect at Indico where he has played a key role in developing the company's enterprise AI platform for unstructured content. Prior to joining indico in its early days, Madison designed and built an NLP system at Fetchnotes and was an active open source contributor to projects like Python3 and Pylearn2."

The past year and a half has seen the rise of language model finetuning as the de-facto standard for natural language processing applications in low data environments. However, the boost to model quality comes at a cost -- modern language models have large disk-space footprints and are compute + memory intensive. Indico data solutions has deployed several unique strategies to make these model architectures more suitable for industry use. One such strategy, dubbed the "adapter" method, achieves comparable accuracy to full model finetuning while training only 5% of the weights. We'll discuss how we employ the adapter strategy in production and how custom model finetuning with the indico platform allows subject matter experts to automate the boring aspects of their jobs.

Eila Arich-Landkof (meetup organizer)- Founder and CEO of Oriel Research Therapeutics - a startup company that uses big data and machine learning to diagnose disease and match therapy to patients.
In the last five years, Eila navigated her career from the high tech industry to the biomedical research field.
She worked as a wet-lab technician at The Whitehead institute for biomedical research at MIT and as a product manager at Mass General Hospital and The Broad Institute of Harvard and MIT where she used her biological and computer science experience to build an application to present therapies and genomic information. Her work won the first place for clinical poster at the 2016 Broad Institute scientific contest.
Machine learning @ healthcare panelist:
I will introduce our work on screening for AML (acute myeloid leukemia) using RNA and clinical data and machine learning. AI in medical field challenges and ways to move forward.

Francesca Lazzeri, Ph.D. - @frlazzeri - is Senior Machine Learning Scientist at Microsoft on the Cloud Advocacy team and expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, statistical modeling, time series econometrics and forecasting, and a range of industries – energy, oil and gas, retail, aerospace, healthcare, and professional services.

Francesca periodically teaches applied analytics and machine learning classes at universities and research institutions around the world. She is Data Science mentor for Ph.D. and Postdoc students at the Massachusetts Institute of Technology, and speaker at academic and industry conferences - where she shares her knowledge and passion for AI, machine learning, and coding.

How to tackle the challenges in deploying machine learning models Francesca Lazzeri, PhD

Model deployment is the method by which you integrate a machine learning model into an existing production environment in order to start using it to make practical business decisions based on data. It is only once models are deployed to production that they start adding value, making deployment a crucial step. However, there is complexity in the deployment of machine learning models.

In this talk you will learn how to deploy your machine learning models with Azure Machine Learning. The service fully supports open-source technologies such as PyTorch, TensorFlow, and scikit-learn and can be used for any kind of machine learning, from classical ml to deep learning, supervised and unsupervised learning.

Useful Resources for the Session:

Azure Notebooks:
Python Microsoft: