ODSC West 2018 (Open Data Science Conference)

ODSC West 2018 (Open Data Science Conference)

by ODSC Open Data Science Conference
Released July 2019
Publisher(s): Pearson
ISBN: 0136526470

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Video description

Royalties for this video set help fund ODSC community initiatives such as grants to open source projects, our diversity program, student travel grants, and other initiatives.

The Open Data Science Conference has established itself as the leading conference in the field of applied data science. Each ODSC event offers a unique opportunity to learn directly from the core contributors, experts, academics and renowned instructors helping shape the field of data science and artificial intelligence Presentations cover not only data science modeling but also the languages and tools needed to deploy these models in the real world such as TensorFlow, MXNet, scikit-learn, Kubernetes, and many more. Our conferences are organized around focus areas to ensure our attendees are at the forefront of this fast emerging field and current with the latest data science languages, tools, and models. You’ll find in our East 2018 video catalog some of our most popular focus areas including:

Deep Learning and Machine Learning

Over the last 5 years, we have seen incredible advances in the field of data scientist thanks to breakthroughs in neural networks, transfer learning, reinforcement learning, and generative adversarial networks (GANs) to name a few. With the advent of Google Voice, Alexa, and other voice assistants, presentations on enabling technologies like NLP, RNNs, and LSTM are popular. Some session to note:

  • OS for AI: How Serverless Computing Enables the Next Gen of ML—Jon Peck
  • pomegranate: Fast and Flexible Probabilistic Modeling in Python—Jacob Schreiber
  • Data Wrangling to Provide Solar Energy Access Across Africa—Brianna Schuyler, PhD
  • The past, present, and future of Automated Machine Learning—Randy Olson, PhD
  • Minimizing and Preventing Bias in AI—Frances Haugen
  • Deep Learning on Mobile—Anirudh Koul
  • State of the Art Natural Language Understanding at Scale—David Talby, PhD
  • Latest Developments in GANS—Seth Weidman
  • How to Reason About Stateful Streaming Machine Learning Serving—Lessons from Production—Patrick Boueri
  • An Introduction to Active Learning—Jennifer Prendki, PhD
  • How to use Satellite Imagery to be a Machine Learning Mantis Shrimp—Sean Patrick Gorman, PhD

Core Data Science and Data Visualization

As data science advances at a rapid pace, core skills are more important than ever. Our sessions range from beginner to advanced level for core topics. Additionally, data and models need to be actionable and data visualization remains a key skill in any data scientist’s toolkit. Some session of note include:

  • Panel: Visual Search: The Next Frontier of Search—Clayton Mellina
  • Visualizing Vectors: Basics Every Data Scientist Should Know—Jed Crosby
  • Revolutionizing Visual Commerce—Robinson Piramuthu
  • Scaling Interactive Data Science and AI with Ray—Richard Liaw
  • The Platform and Process of Agile Data Science—Sarah Aerni, PhD
  • The AI Engineer: A Foot in Two Worlds—Guy Royse

Data Science, Management, And Business

Data science is permeating every industry as adoption gathers pace. The management and practice of data science will become increasingly strategically important to all industries including finance and healthcare. Hear from leading experts on important topics including:

  • Managing Effective Data Science Teams—Conor Jensen
  • A Manager’s Guide to Starting a Computer Vision Program—Ali Vanderveld, PhD
  • Word Play: Understanding the Mechanics and Business Value of Speech Technologies—Omar Tawakol
  • Just How Much Data Is Required to Make Autonomous Vehicles Truly Road-Ready?—Alexandr Wang
  • How to Democratize Artificial Intelligence in Your Business—Olivier Blais
  • Reality Check: Beyond the Hype. Real Companies Doing Real Business Getting Real Value with AI—Alyssa Rochwerger
  • An Ethical Foundation for the AI-driven Future—Harry Glasser
  • Best Practices for Deploying Machine Learning in the Enterprise—Robbie Allen
  • Greatest hurdles in AI proliferation in Education—Varun Arora
  • 10 Things I Learned Deploying AI into Human Environments—Cameron Turner

Thought Leadership | Keynotes

Data science is permeating every industry as adoption gathers pace. The management and practice of data science will become increasingly strategically important to all industries including finance and healthcare. Hear from leading experts on important topics including:

  • Data Science and Open-Source Education for the Enterprise—Zachary Sean Brown
  • Turning Machine Learning Research into Products for Industry—Reza Bosagh Zadeh
  • AI—Disruption for the Marketing World—Luc Dumont

Please see our table of contents for a full list of videos.

Table of contents

  1. Deep Learning and Machine Learning
    1. An Introduction to Active Learning—by Jennifer Prendki, PhD
    2. Applying Deep Learning to Article Embedding for Fake News Evaluation—by Amit Gupta
    3. Collaborative Data science and How to Build a Data science Toolchain Around Notebook Technologies—by Moon soo Lei
    4. Continuous Experiment Framework at Uber—by Jeremy Gu
    5. CuPy: A NumPy-compatible Library for GPU—by Crissman Loomis
    6. Data Wrangling to Provide Solar Energy Access Across Africa—by Brianna Schuyler, PhD
    7. Deep Learning for Speech Recognition—by Pranjal Daga
    8. Deep learning is not always the best solution: Illustrative examples from educational products—by Josine Verhagen, PhD
    9. Deep Learning on Mobile—by Anirudh Koul
    10. Dynamic Pricing for Parking—by Maokai Lin
    11. Exploring the Deep Learning Framework: PyTorch—by Stephanie Kim
    12. Guided Analytics for Machine Learning Automation with KNIME—by Iris Adä
    13. How to Reason About Stateful Streaming Machine Learning Serving—Lessons from Production—by Patrick Boueri
    14. How to use Satellite Imagery to be a Machine Learning Mantis Shrimp—by Sean Patrick Gorman, PhD
    15. Image Recognition Primer: ImageNet AlexNet to Mask R-CNN, R-CNN and Fast R-CNN—by Bhairav Mehta
    16. Improving Customer Support through Deep Learning and NLU—by Sami Ghoche
    17. Introduction to Technical Financial Evaluation with R—by Ted Kwartler
    18. Latest Developments in GANS—by Seth Weidman
    19. Law Disorder: Mathematical Models in a Messy World—by Benjamin Pedrick
    20. Machine Learning Algorithms for the Early Detection of Behavioral Health Disorders in Children—by Stuart Liu-Mayo
    21. MacroBase: Prioritizing Human Attention in Big Data—by Firas Abuzaid
    22. Mastering A/B Testing: From Design to Analysis—by Guillaume Saint-Jacques
    23. Mathematical Approaches to Clustering—by Joseph Ross, PhD
    24. Minimizing and Preventing Bias in AI—by Frances Haugen
    25. ML Operationalization: From What? Why? to How? Who?—by Sivan Metzger
    26. Model Evaluation in the Land of Deep Learning—by Pramit Choudhary
    27. pomegranate: Fast and Flexible Probabilistic Modeling in Python—by Jacob Schreiber
    28. Predicting Alzheimer’s: Generating Neural Networks to Detect the Neurodegenerative Disease—by Ayin Vala
    29. Raise your own Pandas Cub—by Ted Petrou
    30. State of the Art Natural Language Understanding at Scale—by David Talby, PhD
    31. The History and Future of Machine Learning at Reddit—by Anand Mariappan
    32. The past, present, and future of Automated Machine Learning—by Randy Olson, PhD
    33. Tuning the Un-tunable: Lessons for tuning expensive deep learning functions—by Patrick Hayes
    34. Unpredictable Predictions of Self-Driving Cars AI—Handling Inference in Anomalous Environment.—by Stepan Pushkarev
  2. Core Data Science and Data Visualization
    1. How Data Fueled the Birth of Computer Vision—by Michael Gormish
    2. Revolutionizing Visual Commerce—by Robinson Piramuthu
    3. The AI Engineer: A Foot in Two Worlds—by Guy Royse
    4. The Platform and Process of Agile Data Science—by Sarah Aerni, PhD
    5. Using Data Science for Good—by David Smith
    6. Visualizing Vectors: Basics Every Data Scientist Should Know—by Jed Crosby
  3. Data Science, Management, And Business
    1. 10 Things I Learned Deploying AI into Human Environments—by Cameron Turner
    2. A Manager’s Guide to Starting a Computer Vision Program—by Ali Vanderveld, PhD
    3. A Practical Example of Taking Data Science, Machine Learning function from 0 to 10 in your Enterprise.—by Madhura Dudhgaonkar
    4. Accelerate AI—AI Gold Rush: Conundrum for Startups—by Divya Jain
    5. Agile Experimentation—from ideas to deployment—by John Haller
    6. An Ethical Foundation for the AI-driven Future—by Harry Glasser
    7. Best Practices for Deploying Machine Learning in the Enterprise—by Robbie Allen
    8. Greatest hurdles in AI proliferation in Education—by Varun Arora
    9. How to Democratize Artificial Intelligence in Your Business—by Olivier Blais
    10. Just How Much Data Is Required to Make Autonomous Vehicles Truly Road-Ready?—by Alexandr Wang
    11. Leveraging AI for product and company growth—by Jeremy Karnowski
    12. Making Data Great Again—by Julia Lane, PhD
    13. Managing Effective Data Science Teams—by Conor Jensen
    14. Most Data-Driven Cultures… Aren’t—by Cassie Kozyrkov, PhD
    15. Practical Data Science—by Michael Galvin
    16. Reality Check: Beyond the Hype. Real Companies Doing Real Business Getting Real Value with AI—by Alyssa Rochwerger
    17. Role and placement of data science in the organization—by Eric Colson
    18. Why effective and Ethical AI needs human-centered design—by James Guszcza, PhD
  4. Thought Leadership | Keynotes
    1. AI—Disruption for the Marketing World—by Luc Dumont
    2. Data Science and Open-Source Education for the Enterprise—by Zachary Sean Brown
    3. Turning Machine Learning Research into Products for Industry—by Reza Bosagh Zadeh

Product information

  • Title: ODSC West 2018 (Open Data Science Conference)
  • Author(s): ODSC Open Data Science Conference
  • Release date: July 2019
  • Publisher(s): Pearson
  • ISBN: 0136526470