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Turn ODSC into a mini-bootcamp with our 3 day pass

Avail free access to on-demand pre-bootcamp training to win prizes.

Join Pre-Bootcamp Training

Start learning in the winter with the very fundamentals of data science on-demand training to build confidence and prepare for the bootcamp.

Learn Applied Skills at the Mini- Bootcamp

Join the 3 day mini-bootcamp and learn the languages, algorithms, and tools from some of the leading data scientists in the world

Career Support

 

Network with attendees from 1,600+ companies and meet over 40 companies hiring directly at ODSC, career days, webinars, resume reviews and more

Get Certified 

 

Get a certification of completion upon finishing 3 days of instruction

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 Kick-start your career in data science, from beginner to advanced in just 3 days. Get the skills you need, taught by world-renowned experts

ODSC Mini Virtual Bootcamp is the best way to gain in-demand data science skills in the shortest time with minimum investment. Starting in the winter, the on-demand pre-Bootcamp Training will provide the right preparation and confidence to put your practice to work in your learning path.

It’s truly an immersive experience. Over almost a week, you will access an expanse of industry-focused data science topics on a scale not offered elsewhere. Uniquely, you will also get invaluable insights by networking and connecting with hundreds of data science attendees, world renowned instructors, industry experts, and dozens of top companies seeking the next wave of talent.

Current Specialization Tracks

Attend one of our six specialization tracks or customize it to your own goals

  • Machine Learning with Python

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • Programming with Data: Python and Pandas
    • Hands-on Machine Learning with scikit-learn
    • Bayesian Data Science: Probabilistic Programming
    • Introduction To Face Processing With Computer Vision
    • Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples
    • Missing Data in Supervised Machine Learning
    • Causal Inference for Data Science
    • Scalable Active Learning and Semi-supervised Learning
    • ML Inference on Edge with ONNX Runtime
    • Atypical Applications of Typical Machine Learning Algorithms
    • Interpreting and Explaining XGBoost Models

  • Machine Learning with R

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • R for Python Programmers
    • Machine Learning in R Part I: Penalized Regression and Boosted Trees
    • Machine Learning in R Part II: Using workflows to build an ML optimization pipeline
    • Machine Learning in R Part III: Forecasting Time Series Data
    • Missing Data in Supervised Machine Learning
    • Machine Learning in R Part IV: Putting R-Based Machine Learning into Production with Plumber and Docker
    • Causal Inference for Data Science
    • Missing Data in Supervised Machine Learning
    • Atypical Applications of Typical Machine Learning Algorithms

  • Deep Learning

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • Programming with Data: Python and Pandas
    • Hands-on Machine Learning with scikit-learn
    • Introduction To Face Processing With Computer Vision
    • Deep Learning Building Blocks
    • Variational Auto-Encoders for Customer Insight
    • Transformers for Computer Vision
    • State of the art AI methods with TensorFlow: Transfer Learning, RL and GANs

     

     

    More sessions coming soon

  • Data Engineering / MLOps

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • SQL for Data Science
    • Data Science Best Practices: Continuous Delivery for Machine Learning
    • Deploying ML pipeines with open-data
    • Kubeflow Security and Pipeline deployment in MLOps
    • ML Inference on Edge with ONNX Runtime

     

     

     

    More sessions coming soon

  • NLP

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • Programming with Data: Python and Pandas
    • Advanced NLP: From Essentials to Deep Transfer Learning
    • Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems
    • Building an Industry Classifier With The Latest Scraping, NLP and Deployment Tools
    • State-of-the-art NLP Made Easy with AdaptNLP
    • State of the art AI methods with TensorFlow: Transfer Learning, RL and GANs

       

     

    More sessions coming soon

  • Data Visualization

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • Bringing Data to the Masses Through Visualization
    • Animating Data: From matplotlib plots to GIFs
    • From Numbers to Narrative: Turning Raw Data into Compelling Visual Stories with Impact

     

     

     

     

    More sessions coming soon

Our mini bootcamp is fully customizable, you can attend the sessions you want live and on-demand after our conference. Check all available training here – Training Page.

East Mini Virtual Bootcamp Overview

Begin, this winter, with a pre-bootcamp on-demand training to build confidence on the core fundamentals of data science, and steadily progress through the bootcamp week with our hands-on training and expert-led sessions and projects. Refer to the timeline below.

Pre-Bootcamp On-demand Training

Get started in your learning track with free access to introductory concepts with short, to-the-point and industry driven on-demand courses.

Schedule: Self-paced

Pre-bootcamp Live Training

Build a foundation of essential hands-on concepts as part of your Bootcamp pass and get complimentary access to one Live training of your choice on the Ai+ training platform. 

Upcoming live training include:

Mini-Bootcamp Day 1 - Beginner Level Sessions (March 30th)

Previous sessions include:

  • Mathematics for Data Science
  • Statistics for Data Science
  • Introduction to Python Programming with Pandas
  • Introduction to Natural Language Processing
  • SQL for Data Science
  • Introduction to Bayesian Modelling
  • Recommendation Systems Fundamentals
  • Introduction To Face Processing With Computer Vision

Mini-Bootcamp Day 2 - Intermediate Level sessions (March 31st)

Previous sessions include:

  • Introductory to Intermediate Machine Learning with scikit-learn
  • Advanced Machine Learning with scikit-learn: Pipelines, Evaluation Metrics, Imbalanced Classification
  • Machine Learning in R: Penalized Regression, Boosted Trees and build ML pipelines
  • Machine Learning in R: Forecasting Time Series Data and ML in production with Plumber, Docker
  • Machine Learning for Trading
  • Interpreting and Explaining XGBoost Models
  • Bayesian Data Science: Probabilistic Programming
  • Deep Learning with TensorFlow and PyTorch

Mini-Bootcamp Day 3 - Advanced Level Sessions (April 1st)

Previous sessions include:

  • Variational Auto-Encoders for Customer Insight
  • Learning and Mining Large-Scale Spatiotemporal Data
  • From Numbers to Narrative: Turning Raw Data into Compelling Stories with Impact
  • How To Build And Test A Trading Strategy Using ML
  • State-of-the-art NLP Made Easy with AdaptNLP
  • Building an Industry Classifier With The Latest Scraping, NLP and Deployment Tools
  • Sequence Modelling with Deep Learning
  • Image Detection as a Service: Use APIs and Deep Learning to Support Products

Certificate of Completion & More

  • Obtain an ODSC Certification of Completion
  • Get access to all ODSC East 2021 recorded sessions
  • Get access to over 100+ additional recorded sessions from prior ODSC events, including ODSC West and ODSC Europe

What You’ll Learn

 Learn Fundamentals

Prep for your immersive training by learning core fundamentals, including:

  • Fundamentals of Python Programming

  • Data Analysis in Python

  • Introduction to Statistics for Data Science

  • Fundamental Analytics for Data Engineering

Learn Frameworks & Tools

Get hands-on experience with in-demand data science tools and frameworks, including:

  • Python, Jupyter Notebooks

  • R programming, Julia, Scala, Stan

  • Apache Spark, MLlib, Streaming

  • Tensorflow, MXNet, Caffe, CNTK

  • Scikit-learn, Theano, Shogun, Pylearn2

  • See schedule for many more…

Learn Core Topics

Get hands-on experience in the most important data science topics with over 40 beginner to intermediate sessions to choose from, including

  • Deep Learning

  • Reinforcement Learning and Deep Reinforcement Learning

  • Machine Learning

  • Transfer Learning

  • Natural Language Processing

  • Text Analytics

  • Data Visualization

  • Data Modeling and Data Wrangling

  Build your Network

A career in data science opens up the possibility of rewarding and exciting work. Build your network and help grow your career

  • Learn from and connect with world-leading data science experts

  • Stop by our Career Expo and meet top hiring companies like Google, DataRobot, and dozens of others

  • Join our various networking events, such as Meet the Speakers, Meet the Experts, Dinner with Data Scientists; plus other networking opportunities

  • Network with hundreds of data science attendees to learn what it’s like to be a data scientist

Bootcamp Experience

Registration

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Choose your Pass

Pre-Bootcamp On-Demand Training

Tuesday - Thursday | Data Science Training  - 20 Sessions

Tuesday - Thursday | Workshops & Talks - 30 Sessions

On-demand Access to All Conference recordings 

Tuesday & Wednesday | Virtual AI Expo and Demo Talks

Thursday | Virtual Career Expo 

All Access

$559


Purchase Separately

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Virtual Mini-Bootcamp 

$699


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Current ODSC East Instructors

Kirk borne
Principal Data ScientistBooz Allen Hamilton

Dr. Kirk Borne

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples(Half-Day Training)

Atypical Applications of Typical Machine Learning Algorithms(Half-Day Training)

Open Source Relations ManagerVertica

Paige Roberts

In two decades in the data management industry, Paige Roberts has worked as an engineer, a trainer, a support technician, a technical writer, a marketer, a product manager, and a consultant.
She has built data engineering pipelines and architectures, documented and tested large scale open source analytics implementations, spun up Hadoop clusters from bare metal, picked the brains of some of the stars in the data analytics and engineering industry, championed data quality when that was supposedly passé, worked with a lot of companies in a lot of different industries, and questioned a lot of people’s assumptions.
Now, she promotes understanding of Vertica, MPP data processing, open source, high scale data engineering, and how the analytics revolution is changing the world.

Data Science ManagerEY

Lara Kattan

Lara is a Data Science Manager at EY and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science bootcamp and built risk models for large financial institutions at McKinsey & Co.

Probabilistic Programming and Bayesian Inference with Python(Half-Day Training)

Machine Intelligence ScientistDraper

Teal Guidici, PhD

Teal Guidici is a Senior Machine Intelligence Scientist at Draper where she uses statistical techniques and machine learning algorithms to develop creative solutions for interesting data-driven problems in areas including biomedicine, finance, and remote sensing. Prior to Draper, she did graduate work creating new methods to analyze patterns of co-variation in complex datasets and applied these methods applied to high throughput metabolomics data. She has additional experience in survey design and data analysis in consumer marketing research. Dr. Guidici has a B.S. in Theoretical Mathematics from MIT, a M.S. in Bioinformatics and a Ph.D. in Statistics from the University of Michigan.

Echo State Networks for Time-Series Data (Tutorial)

Chief Data Scientist, Author of "R for Everyone"Lander Analytics

Jared Lander

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fundraising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Machine Learning in R Part I(Full-Day Training)

Machine Learning in R Part II(Full-Day Training)

Head of Data Science and AnalyticsBadoo (now MagicLab, which owns several apps)

Leonardo De Marchi

Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving.

He now works as Head of Data Scientist and Analytics in Badoo, the largest dating site with over 420 million users. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning, and Machine Learning training.

Senior Inventive ScientistAT&T Chief Data Office

Noemi Derzsy, PhD

Noemi Derzsy is a Senior Inventive Scientist at AT&T Chief Data Office within the Data Science and AI Research organization. Her research is centered on understanding and modeling customer behavior and experience through large-scale consumer and network data, using machine learning, network analysis/modeling, spatio-temporal mining, text mining and natural language processing techniques. She is an organizer of Data Umbrella meetup group and NYC Women in Machine Learning and Data Science meetup group, and she is a NASA Datanaut.
Prior to joining AT&T, Noemi was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute. She holds a PhD in Physics, MS in Computational Physics, and has a research background in Network Science and Computer Science.

Exploring the Interconnected World: Network/Graph Analysis in Python(Tutorial)

Global Lead Data Science InstructorGeneral Assembly

Matt Brems

Matt currently leads instruction for GA’s Data Science Immersive in Washington, D.C. and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about making data science more accessible and putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.

Good, Fast, Cheap: How to do Data Science with Missing Data(Half-Day Training)

Technical Program ManagerGoogle

Irene Giannoumis

Irene is a Technical Program Manager on the Research & Machine Intelligence team at Google, where she works to bring machine learning into production pipelines through TensorFlow (TFX). Previously, Irene helped Google’s Counter-Abuse Technology team train machine learning models to fight spam and abuse. Prior to joining Google, she spent over a decade at Microsoft working in several engineering and program management roles across the Enterprise and Operating Systems divisions.

From Experimentation to Products: The Production ML Journey(Talk)

Staff Associate - Machine LearningColumbia University in the City of New York

Thomas Fan

Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open source machine learning library written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open source software is one of the best ways to maximize one’s impact.

Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Half-Day Training)

Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Half-Day Training)

deep learning with TensorFlow
Chief Data ScientistUntapt

Dr. Jon Krohn

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.

Machine Intelligence ScientistDraper

Laura A. Seaman, PhD

Laura Seaman is a Senior Machine Intelligence Scientist at Draper where she applies machine learning and bioinformatics algorithms to a variety of applications. Dr. Seaman’s graduate work focused on using genetic data to identify alterations to the genomic structure in cancer. She is currently using data science for many applications including analysis of financial networks and identification of genetically engineered organisms. Dr. Seaman has a Bachelor of Science in Biological Engineering from the Massachusetts Institute of Technology, a Masters of Arts in Statistics from the University of Michigan, and a Doctor of Philosophy in Bioinformatics from the University of Michigan.

Data ScientistIndellient

Jayeeta Putatunda

Jayeeta is a Senior Data Scientist with 5+ years of industry experience. She specializes in Natural Language Processing and its applications in various business use-cases. Currently, she is working on machine learning and big data projects at Indellient Inc., leading software development and IT professional services company working with Fortune 100 companies. Prior to this, she worked with Omnicom Group and Deloitte. Jayeeta has received her Master of Science in Quantitative Methods and Modeling from the City University of New York, NY, and Bachelor of Science in Economics and Statistics from West Bengal State University, India. Jayeeta is passionate about her work and is actively engaged with some amazing organizations to promote and inspire more women to take up STEM.

Developer Advocate, TensorFlowGoogle

Robert Crowe

A recovering data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive.

From Experimentation to Products: The Production ML Journey(Talk)

Data Visualization DeveloperObservable

Ian Johnson

Ian Johnson is a User Experience Engineer at Google. He also organizes of Bay Area d3, starts with SVG and then dives deep into d3 including DOM manipulation, categorical and quantitative scales, axis, brushes, color schemes, events and histograms. Ian likes to make sense of data by exploring it visually with D3.js!

Painting with Data: Introduction to d3.js(Half-Day Training)

Author of nxviz Package

Eric Ma, PhD

Eric is an Investigator at the Novartis Institutes for Biomedical Research, where he solves biological problems using machine learning. He obtained his Doctor of Science (ScD) from the Department of Biological Engineering, MIT, and was an Insight Health Data Fellow in the summer of 2017. He has taught Network Analysis at a variety of data science venues, including PyCon USA, SciPy, PyData, and ODSC, and has also co-developed the Python Network Analysis curriculum on DataCamp. As an open-source contributor, he has made contributions to PyMC3, matplotlib, and bokeh. He has also led the development of the graph visualization package nxviz, and a data cleaning package pyjanitor (a Python port of the R package).

Network Analysis Made Simple(Half-Day Training)

Advanced ResearcherInria

Julie Josse, PhD

Julie Josse is a senior researcher at Inria and a scientific collaborator and teacher at at Ecole Polytechnique (Institut Polytechnique de Paris) in France. Julie Josse’s is an expert in handling missing values (inference, multiple imputation, matrix completion, missing non at random data, supervised learning with missing values) and has created a platform to collect works and give resources to users https://rmisstastic.netlify.app/, has organized workshops on the topic. Her vocation is to push methodological innovation to bring useful application of her research to the user in particular in bio-sciences and health. Her curent research focuses on causal inferences techniques for personalized medicine. She leads an important project with the Traumabase group dedicated to the management of polytraumatized patients to help emergency doctors making decisions. Julie Josse is dedicated to reproducible research with the R statistical software: she has developed packages including missMDA and FactoMineR to transfer her work, she is a member of the R foundation and of Rforwards to increase the participation of minorities in the community.

PresidentEnplus Advisors, Inc.

Daniel Gerlanc

Daniel Gerlanc has worked as a data scientist for more than decade and been writing sofware for nearly 20 years. He frequently teaches live trainings on oreilly.com and is the author of the video course Programming with Data: Python and Pandas. He has coauthored several open source R packages, published in peer-reviewed journals, and is a graduate of Williams College.

Programming with Data: Python and Pandas(Full-Day Training)

Developer Evangelist, Machine LearningSixgill, LLC

Sage Elliott

Sage Elliott is a Machine Learning Engineer and Developer Advocate at Sixgill. He is passionate about making AI approachable for everyone. Sage has helped build some of the largest tech communities in Seattle, WA and has taught thousands of people how to program.

Chief AI Scientist at CortAIxThales

Freddy Lecue, PhD

Dr Freddy Lecue (PhD 2008, Habilitation 2015) is the Chief Artificial Intelligence (AI) Scientist at CortAIx (Centre of Research & Technology in Artificial Intelligence eXpertise) at Thales in Montreal, Canada. Former AI lead at Accenture Labs and IBM Research (Dublin, Ireland). 80+ publications, mostly in AI (7 at AAAI, 4 at IJCAI, 4 at ECAI). 28 h-index, 2,000+ citations, 40+ granted patents on AI methods and systems. Internal (Accenture, IBM) and external (scientific community) award for AI research assets and transfer in products.

Research EngineerFacebook AI Research Group.

Shagun Sodhani

Shagun Sodhani is a Research Engineer in the Facebook AI Research Group. He is primarily interested in lifelong reinforcement learning – training AI systems that can interact with and learn from the physical world and consistently improve as they do so without forgetting the previous knowledge. He did his MS from Mila, University of Montreal., where he was supervised by Dr. Yoshua Bengio and Dr. Jian Tang.

Professor, Lab Director, Sr. AI ScientistCornell Tech, Rochester Institute of Technology, Paige

Christopher Kanan, PhD

Christopher Kanan is an Assistant Professor at the Rochester Institute of Technology (RIT), a Visiting Assistant Professor at Cornell Tech, and a Senior AI Scientist at Paige. At RIT, his lab works on lifelong machine learning and language driven computer vision, which has been supported by awards from NSF, AFOSR, ONR, DARPA, Adobe Research, and other industrial partners. He is also Associate Director of RIT’s Center for Human-aware AI and he is a member of RIT’s McNair Scholars advisory board. At Paige, a startup that has raised $95M to improve the diagnosis of cancer, he led the AI R&D team during its first 1.5 years and continues to advise its AI teams. He received a PhD in computer science from the University of California at San Diego, where he worked on brain-inspired algorithms for object recognition, neural networks, active vision, and cognitive modeling. He received an MS in computer science from the University of Southern California. Before joining RIT, he was a postdoctoral scholar at the California Institute of Technology, and later worked as a Research Technologist at NASA’s Jet Propulsion Laboratory, where he used deep learning to develop vision systems for autonomous ships. He is the recipient of the 2016 Rising Star Award and the 2019 Distinguished Scholarship Award in RIT’s College of Science. He is an IEEE Senior Member and has published over 50 refereed papers, many of which are in top venues across AI such as CVPR, ICCV, NeurIPS, AAAI, ICLR, ACL, etc.

Cofounder, Principal Data ScientistRoboflow.ai, BetaVector, General Assembly

Joseph Nelson

Joseph Nelson is co-founder and CEO of Roboflow, a computer vision developer tool. Roboflow enables anyone to build better computer vision models quickly. Joseph previously co-founded Represently (acq. 2018). He has been named Distinguished Faculty at General Assembly and worked at companies big (Facebook) and small (failed startups). Joseph is a managing partner at BetaVector, a data science consultancy he co-founded. He’s easily reached on Twitter: @josephofiowa

Applied Deep Learning: Building a Chess Object Detection Model with TensorFlow(Half-Day Training)

Principal Program ManagerMicrosoft

Manash Goswami

Manash Goswami is a product management leader with a passion to solve problems and build products that meet end-user needs. Grown from development – coding, test & verification, systems design & architecture, to product management and business development. Delivered consumer electronics products from smartwatch to tablets. Deep understanding of the Android, Chrome & Windows eco-system. Demonstrated leadership by managing through influence across a matrixed org structure. Capable of driving new initiative/agenda internally or with customers, enable collaboration across functional teams and drive for financial results. Excel at dealing with ambiguity and managing complex, cross-group interactions. Can successfully tie long-term strategic thinking with near-term execution.

Ml Inference on Mobile Device With Onnx Runtime(Tutorial)

Cloud ArchitectGoogle

Elliott Ning

Elliott Ning is a passionate technology advisor of Google for data analytics, infrastructure modernization, and AI to bring organizations business opportunities into reality. Previously he has worked in leading technology companies including Oracle and Huawei to transform enterprises by developing solutions and strategies on cloud platforms and data centers. Elliott holds two MS degrees in Software Engineering and Manufacturing Management, and extensive industrial certifications in IT and cloud.

Accelerating The Journey To Document Understanding AI(Tutorial)

Learn More About Our Program



Who Should Attend

Beginners

  • Beginners pursuing data science careers

  • Those trying to decide on the right path

  • Business Analysts and Data Analysts seeking new skills

Intermediates

  • Data Scientists

  • Data Engineers

  • ML Engineers

  • Software Engineers

Citizens

  • Citizen Data Scientists

  • Data Wranglers

  • Data Science 4 Good

  • Data Journalists 

Companies Represented at ODSC Bootcamp

We’re Proud to Have the Best and Brightest in Attendance

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