Boston | April 13th – April 17th, 2020

Machine Learning for Programmers

Learn the essentials to become a skilled Machine Learning Engineer

Leverage Your Programming Skills

Become a

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 of Current ML for Programmers Speakers


Click Here For Full Lineup
2020 Speakers

Sample Talk, Workshop, and Training Sessions

Machine Learning for Programmers Sessions
Wednesday, April 15th
Thursday, April 16th
Friday, April 17th
Wednesday, April 15th
Thursday, April 16th
Friday, April 17th
09:00 - 13:00
Advanced Machine Learning: Pipelines and Evaluation Metrics

Training | Machine Learning | ML for Programmers | Advanced

 

scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This training will cover some of the more advanced aspects of scikit-learn, such as building complex machine learning pipelines and advanced model evaluation. Model evaluation is an underappreciated aspect of machine learning, but using the right metric to measure success is critical. Practitioners are often faced with imbalanced classification tasks, where accuracy can be uninformative or misleading. We will discuss other metrics, when to use them, and how to compute them with scikit-learn. We will also look into how to build processing pipelines using scikit-learn, to chain multiple preprocessing techniques together with supervised models, and how to tune complex pipelines…more details

Advanced Machine Learning: Pipelines and Evaluation Metrics image
Andreas Mueller, PhD
Author, Research Scientist, Core Contributor of scikit-learn | Columbia Data Science Institute
09:00 - 10:30
Quick Package Development in R and Python (from “Python or R” to “Python and R”)

Workshop | ML for Programmers | Kick-starter | Beginner-Intermediate

 

When building a data science team it is important to document and record work flows in both R and python. A data science department can use installable git controlled packages to form a foundation of code and best practices. In addition, libraries dramatically cut down the time and effort required for a team to bring work to production. Additionally, package development is the first step for engineers and scientists aiming to contribute their unique ideas back to the community.

This workshop aims to teach the basics of package development in both R and python in 90 minutes. We will touch upon why a data science team should strive to be fully fluent in both languages. We will show simple R package development and simple Python package development. Finally, we will demonstrate how one can use an open source package to test the interface similarity between R and Python packages designed to support identical workflowsmore details

 

Quick Package Development in R and Python (from “Python or R” to “Python and R”) image
Theodore Bakanas
Senior Data Scientist | Uptake
Quick Package Development in R and Python (from “Python or R” to “Python and R”) image
Zhi Lu, PhD
Data Scientist | Uptake
14:00 - 18:00
Building Data Narratives: an End-to-End Machine Learning Practicum

Traning | Data Vizualization | ML for Programmers | Beginner-Intermediate


This workshop will take one through the steps associated with an end-to-end machine learning campaign: data retrieval; data curation; model construction, evaluation, selection and interpretation; and reporting. Particular attention will be paid to reporting, i.e., building a narrative. Examples will be presented demonstrating how one might generate multiple output formats (e.g., HTML pages, presentation slides, PDF documents) starting with the same code base.

The workshop will have three main foci:
1. infrastructure: instantiating the computational environment; loading packages; loading data
2. computation: data curation, transformation, and analysis; model construction, evaluation, and interpretation
3. communication: creating tables, charts, and graphs; weaving all components into data narrative…more details

 

Building Data Narratives: an End-to-End Machine Learning Practicum image
Paul J Kowalczyk, PhD
Senior Data Scientist | Solvay
14:00 - 15:30
From Research to Production: Performant Cross-platform ML/DNN Model Inferencing on Cloud and Edge with ONNX Runtime

Workshop | ML for Programmers | Deep Learning | Intermediate

 

Powerful Machine Learning models trained using various frameworks such as scikit-learn, PyTorch, TensorFlow, Keras, and others can often be challenging to deploy, maintain, and performantly operationalize for latency-sensitive customer scenarios. Using the standard Open Neural Network Exchange (ONNX) model format and the open source cross-platform ONNX Runtime inference engine, these models can be scalably deployed to cloud solutions on Azure as well as local devices ranging from Windows, Mac, and Linux to various IoT hardware. Once converted to the interoperable ONNX format, the same model can be served using the cross-platform ONNX Runtime inference engine across a wide variety of technology stacks to provide maximum flexibility and reduce deployment friction.

In this workshop, we will demonstrate the versatility and power of ONNX and ONNX Runtime by converting a traditional ML scikit-learnpipeline to ONNX, followed by exporting a PyTorch-trained Deep Neural Network model to ONNX. These models will then be deployed to Azure as a cloud service using Azure Machine Learning services, and to Windows or Mac devices for on-device inferencingmore details

From Research to Production: Performant Cross-platform ML/DNN Model Inferencing on Cloud and Edge with ONNX Runtime image
Faith Xu
Senior Program Manager | Microsoft
From Research to Production: Performant Cross-platform ML/DNN Model Inferencing on Cloud and Edge with ONNX Runtime image
Prabhat Roy
Data Scientist | Microsoft
14:00 - 18:00
Advanced Machine Learning with scikit-learn: Imbalanced Classification and Text Data

Training | Machine Learning | ML for Programmers | Advanced

 

scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This training will cover some advanced topics in using scikit-learn and how to build your own models or feature extraction methods that are compatible with scikit-learn. We will also discuss different approaches to feature selection and resampling methods for imbalanced data. Finally, we’ll discuss how to do the classification of text data using the bag-of-words model and its variants…more details

Advanced Machine Learning with scikit-learn: Imbalanced Classification and Text Data image
Andreas Mueller, PhD
Author, Research Scientist, Core Contributor of scikit-learn | Columbia Data Science Institute
15:00 - 15:45
Predictive Maintenance: Zero to Deployment in Manufacturing

Talk | Machine Learning | ML for Programmers | Intermediate

 

The manufacturing industry is going through it’s IV industrial revolution where machines are connected, and data is harvested to discover deeper insights and solved problems to achieve a competitive edge in the market. There are various applications in industry 4.0, such as machine connectivity, machine productivity monitoring, predictive maintenance, predictive quality, inventory optimization, supply chain optimization, data discovery, and the possibilities are unlimited. Hence, there is a significant need for more reliable and robust approaches to achieve the benefits of various applications. By integrating the philosophy of lean manufacturing and “A small success eventually leads to bigger success,” one case of predictive maintenance (PdM) led to a global scale implementation by developing new technologies using ML & DL models and deploying them on an industrial scale. This case study discusses different levels of analytics, basics of predictive maintenance, anomaly detection, remaining useful life prediction techniques, combining data and SME knowledge, ML models, and how they can be deployed in manufacturing for different use cases. This case study also discusses various modeling and deployment challenges of implementing predictive maintenance in manufacturing…more details

Predictive Maintenance: Zero to Deployment in Manufacturing image
Nagdev Amruthnath, PhD
Data Scientist III | DENSO North America
15:00 - 15:45
Accelerate ML Lifecycle with Kubernetes and Containerized Data Science Tools

Talk | ML for Programmers | MLOps & Data Engineering | Beginner-Intermediate

 

Kubernetes & container platforms provide desired agility, flexibility, scalability, & portability for data scientists to train, test, & deploy ML models quickly, without IT dependency. The session will provide an overview of containers and Kubernetes, and how these technologies can help solve the challenges faced by data scientists, ML engineers, and application developers. Next, we will review the key capabilities required in a containers and kubernetes platform to help data scientists easily use technologies like Jupyter Notebooks, ML frameworks, programming languages to innovate faster. Finally we will share the available platform options (e.g. Red Hat OpenShift, KubeFlow, etc.), and some examples of how data scientists are accelerating their ML initiatives with containers and kubernetes platform…more details

 

Accelerate ML Lifecycle with Kubernetes and Containerized Data Science Tools image
Abhinav Joshi
Sr. Principal Marketing Manager | Red Hat
Accelerate ML Lifecycle with Kubernetes and Containerized Data Science Tools image
Tushar Katarki
Sr. Principal Product Manager | Red Hat
15:00 - 15:45
Looking from Above: Object Detection and Other Computer Vision Tasks on Satellite Imagery

Talk | Machine Learning | ML for Programmers | Intermediate

 

The talk aims to introduce the attendees to the application of computer vision techniques to overhead imagery such as satellite, aerial and drone imagery. The emphasis is on object detection on satellite images as we share our learnings from dealing with those datasets (such as the xView Object Detection Challenge). The attendees will learn about challenges common in such datasets, such as scale variance and images with more than three channels, as well as approaches to address them and the results of our experiments. The session will also introduce datasets available in the growing field of CV on remote sensing data, as well as various use cases in disaster relief, refugee tracking, animal population counting, geospatial intelligence for business decisions and defense. The attendees will walk away with a good grasp of what is possible when machine learning at scale is applied to geospatial data…more details

Looking from Above: Object Detection and Other Computer Vision Tasks on Satellite Imagery image
Xiaoyong Zhu
Senior Data Scientist | Microsoft
16:00 - 17:30
Consume, Control and Serve REST APIs with R

Workshop | ML for Programmers | Open-source | Beginner-Intermediate

 

From a consumption perspective, we discuss how to work with the httr, curl, and jsonlite packages. We show examples of constructing API requests, submitting them, and working with the responses. We discuss tips and tricks to optimize the workflow.
to interface with web REST APIs for many purposes.

From a command & control perspective, we discuss several packages included in the cloudyr (http://cloudyr.github.io/) project, which allow you to manage and execute tasks in the cloud. We show examples of creating resources and augmenting your data using APIs.

Last, we discuss the plumber R package, which allows users to expose existing R code as a service available to others on the Web. We discuss how to take models and functions and expose them as APIs so others can use them…more details

Consume, Control and Serve REST APIs with R image
Marck Vaisman
Sr. Cloud Solutions Architect - Data/AI | Microsoft
16:15 - 17:00
The Hamiltonian Monte Carlo Revolution is Open Source: Probabilistic Programming with PyMC3

Talk | ML for Programmers | Beginner

 

In the last ten years, there have been a number of advancements in the study of Hamiltonian Monte Carlo algorithms that have enabled effective Bayesian statistical computation for much more complicated models than were previously feasible. These algorithmic advancements have been accompanied by a number of open source probabilistic programming packages that make them accessible to programmers and statisticians. PyMC3 is one such package written in Python and supported by NumFOCUS. This talk will give an introduction to probabilistic programming with PyMC3. No preexisting knowledge of Bayesian statistics is necessary; a working knowledge of Python will be helpful…more details

The Hamiltonian Monte Carlo Revolution is Open Source: Probabilistic Programming with PyMC3 image
Austin Rochford
Chief Data Scientist | Monetate Labs
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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 | April 13th – April 17th, 2020

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