March 30th – April 1st, 2021

Machine Learning for Programmers

Learn the essentials to become a skilled Machine Learning Engineer
Register now

Leverage Your Programming Skills

Become 

Machine Learning Engineer

With the rapid growth of Artificial Intelligence comes rising demand for Machine Learning engineers and programmers. Ubiquitous AI-driven software that utilizes deep learning and machine learning models to enable conversational AI, autonomous machines, machine vision, and other AI technologies require serious engineering. These projects of the future promise to be some of the most exciting jobs in software engineering today.

An ML engineer works at the intersection of software engineering and data science. At ODSC, build on your programming skills to engineer the next generation of artificial intelligence-enabled software. You will learn from leading experts everything from data wrangling, modeling, and workflow, to essential deep learning and machine learning frameworks.

What You'll Learn

Talks + Workshops + Special Events on these topics:

UpSkill Topics

  • Essential Deep Learning Frameworks for ML Engineers

  • Essential Machine Learning Frameworks for ML Engineers

  • Automatic Machine Learning for ML Engineers

  • Leveraging Pre-trained Models

  • What is an ML Engineer 

  • Machine Learing Workflow and Pipelines for ML Engineers

  • Machine Learning at Scale 

  • NLP Models and Machine Translation

  • and more…

Languages & Frameworks

  • Tensorflow 2, PyTorch, Keras, Caffe 2.0, CNTK

  • Python scikit-learn, SciPy, Pandas, PyMC3,

  • R Programming, Keras, CARET

  • spaCy, AllenNLP, Stanford NLP

  • Spark, MLlib, Storm, Hadoop, Mahout

  • Kubernetes, Kafka, Zeppelin, Ignite

  • Apache Airflow, KubFlow, MLFlow

  • NLP Transformers, BERT, ULMFit, ElMo

  • Julia, Java, Jupyter Notebooks, NoSql, Neo4J

Some Current ML for Programmers Speakers

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)

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)

Chief Data ScientistTalla

Byron Galbraith, PhD

Byron Galbraith is the Chief Data Scientist and co-founder of Talla, where he works to translate the latest advancements in machine learning and natural language processing to build AI-powered conversational agents. Byron has a PhD in Cognitive and Neural Systems from Boston University and an MS in Bioinformatics from Marquette University. His research expertise includes brain-computer interfaces, neuromorphic robotics, spiking neural networks, high-performance computing, and natural language processing. Byron has also held several software engineering roles including back-end system engineer, full-stack web developer, office automation consultant, and game engine developer at companies ranging in size from a two-person startup to a multi-national enterprise.

Alternatives to Reinforcement Learning for Real World Problems(Talk)

Some Previous ML for Programmers Speakers

Director, GPU-accelerated machine learningNVIDIA

John Zedlewski

John Zedlewski is the director of GPU-accelerated machine learning on the NVIDIA Rapids team. Previously, he worked on deep learning for self-driving cars at NVIDIA, deep learning for radiology at Enlitic, and machine learning for structured healthcare data at Castlight. He has an MA/ABD in economics from Harvard with a focus in computational econometrics and an AB in computer science from Princeton.

GPU-accelerated Data Science with RAPIDS (Workshop)

Assistant Professor, | Co-founder & CEOUW | Snorkel AI

Alex Ratner, PhD

Alex Ratner has Ph.D. in computer science at Stanford, advised by Chris Re, where his research focuses on weak supervision: the idea of using higher-level, noisier input from domain experts to train complex state-of-the-art models where limited or no hand-labeled training data is available. He leads the development of the Snorkel framework (snorkel.stanford.edu) for weakly supervised ML, which has been applied to machine learning problems in domains like genomics, radiology, and political science. He is supported by a Stanford Bio-X SIGF fellowship.

End-to-end AI Application Development with Programmatic Supervision(Talk)

VP Data & Analytics, Cybersecurity Technology and ControlsJPMorgan Chase & Co

Charles Givre

Charles Givre recently joined JP Morgan Chase works as a data scientist and technical product manager in the cybersecurity and technology controls group. Prior to joining JP Morgan, Mr. Givre worked as a lead data scientist for Deutsche Bank. Mr. Givre worked as a Senior Lead Data Scientist for Booz Allen Hamilton for seven years where he worked in the intersection of cybersecurity and data science. At Booz Allen, Mr. Givre worked on one of Booz Allen’s largest analytic programs where he led data science efforts and worked to expand the role of data science in the program. Mr. Givre is passionate about teaching others data science and analytic skills and has taught data science classes all over the world at conferences, universities and for clients. Mr. Givre taught data science classes at BlackHat, the O’Reilly Security Conference, the Center for Research in Applied Cryptography and Cyber Security at Bar Ilan University. He is a sought-after speaker and has delivered presentations at major industry conferences such as Strata-Hadoop World, Open Data Science Conference and others. One of Mr. Givre’s research interests is increasing the productivity of data science and analytic teams, and towards that end, he has been working extensively to promote the use of Apache Drill in security applications and is a committer and PMC Member for the Drill project. Mr. Givre teaches online classes for O’Reilly about Drill and Security Data Science and is a coauthor for the O’Reilly book Learning Apache Drill. Prior to joining Booz Allen, Mr. Givre, worked as a counterterrorism analyst at the Central Intelligence Agency for five years. Mr. Givre holds a Masters Degree in Middle Eastern Studies from Brandeis University, as well as a Bachelors of Science in Computer Science and a Bachelor’s of Music both from the University of Arizona. Mr. Givre blogs at thedataist.com and tweets @cgivre.

Rapid Data Exploration and Analysis with Apache Drill(Half-Day Training)

Click Here For Full Lineup
2021 Speakers
See all our talks and hands-on workshop and training sessions
See all sessions

Some Previous ML for Programmers Speakers

40% off Ends Soon

Register Now

You Will Meet

  • Some of the world’s leading AI experts

  • Some of the best minds and authors behind today’s most popular AI platforms

  • Artificial Ingelligence and data science innovators

  • Data science & analytics specialists

  • Developers, engineers and programmers looking to build AI enabled software

  • Hundreds of attendees focused on AI engineering

  • CTOs and Chief Data Scientists from startups and Fortune 500 companies

  • Data scientists, data engineers, and AI platform experts

  • Peers from startups to Fortune 500 companies wrestling with large sets of consumer data

  • Representatives from Government agencies, universities, and other large institutions

Why Attend?

Immerse yourself in talks and workshops on AI Engineering frameworks, topics, and languages

Learn about AI Engineering from leading AI experts who authored and built many of the platforms in use today

Network and connect with like-minded attendees to discover your next job, service, product or startup

Get speaker insights and training in AI frameworks such as TensorFlow, MXNet, PyTorch, Spark, Storm, Drill, Keras, and other AI platforms

Sign Up for ODSC EAST | March 30th – April 1st, 2021

Register Now
Copyright ODSC 2020. All Rights Reserved