Overview

Whether you work at a software company that needs to improve customer retention, a financial services company that needs to mitigate risk, or a retail company interested in predicting customer purchasing behavior, your organization is tasked with preparing, managing, and gleaning insights from large volumes of data without wasting critical resources. Traditional CPU-driven data science workflows can be cumbersome, but with the power of GPUs, your teams can make sense of data quickly to drive business decisions.
In this Deep Learning Institute (DLI) workshop, developers will learn how to build and execute end-to-end GPU accelerated data science workflows that enable them to quickly explore, iterate, and get their work into production.
Using the RAPIDS accelerated data science libraries, developers will apply a wide variety of GPU-accelerated machine learning algorithms, including XGBoost, cuGRAPH’s single-source shortest path, and cuML’s KNN, DBSCAN, and logistic regression to perform data analysis at scale.
All workshop attendees get access to fully configured, GPU-accelerated servers in the cloud, guidance from a DLI-certified instructor, and the opportunity to network with other developers, data scientists, and researchers.
Attendees can earn a certificate to prove subject matter competency and support professional growth.
Why DLI Hands-On Training?
  • Build deep learning, accelerated computing, and accelerated data science applications for industries such as autonomous vehicles, healthcare, manufacturing, media and entertainment, robotics, smart cities, and more.
  • Gain real-world expertise through content designed in collaboration with industry leaders, such as the Children’s Hospital of Los Angeles, Mayo Clinic, PwC, and Uber.
  • Access content anywhere, anytime with a fully configured, GPU-accelerated workstation in the cloud.
  • Earn an NVIDIA DLI certificate to demonstrate subject matter competency and support career growth.
  • Work with the most widely used, industry-standard software, tools, and frameworks.
Technologies: RAPIDS, cuDF, XGBoost, cuML, cuGraph, Dask, cuPy, pandas, NumPy, Bokeh, data science, data analytics, machine learning, deep learning.

Read more

Prerequisites

Prerequisites:
Read more

Delegates will learn how to

In this workshop, developers will learn how to:
  • Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames
  • Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms
  • Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time
  • Rapidly achieve massive-scale graph analytics using cuGraph routines
Read more

Outline

Introduction
GPU-Accelerated Data Manipulation
Ingest and prepare several datasets (some larger-than-memory) for use in multiple machine learning exercises later in the workshop.
  • Read data directly to single and multiple GPUs with cuDF and Dask cuDF.
  • Prepare population, road network, and clinic information for machine learning tasks on the GPU with cuDF.
GPU-Accelerated Machine Learning
Apply several essential machine learning techniques to the data that was prepared in the first section.
  • Use supervised and unsupervised GPU-accelerated algorithms with cuML.
  • Train XGBoost models with Dask on multiple GPUs.
  • Create and analyze graph data on the GPU with cuGraph.
Project: Data Analysis to Save the UK
Apply new GPU-accelerated data manipulation and analysis skills with population-scale data to help stave off a simulated epidemic affecting the
entire UK population.
  • Use RAPIDS to integrate multiple massive datasets and perform real-world analysis.
  • Pivot and iterate on your analysis as the simulated epidemic provides new data for each simulated day.
Assessment and Q&A
Related Training
If your organization is interested in applying accelerated data science to healthcare, we recommend the online, self-paced course Data Science Workflows for Deep Learning in Medical Applications. Your team will learn how to organize and augment a medical images dataset and validate these techniques by using a convolutional neural network (CNN). Get started here.
Additional Resources
DLI offers other hands-on training and educational resources in data science, deep learning, and accelerated
computing, including:
Self-paced, online courses on accelerated data science, deep learning, accelerated computing, and more at
Instructor-led workshops on deep learning for computer vision, multi-GPUs, healthcare, industrial inspection, robotics, intelligent video analytics, and more at www.nvidia.com/dli
Blogs, webinars, and other resources on data science at www.nvidia.com/datascience
Read more

Why choose QA

Special Notices

Please note - Our first dates for this course will be scheduled from March 2020.

Data, Analytics & AI learning paths

Want to boost your career in data, analytics and AI? Click on the roles below to see QA's learning pathways, specially designed to give you the skills to succeed.

= Required
= Certification

Frequently asked questions

See all of our FAQs

How can I create an account on myQA.com?

There are a number of ways to create an account. If you are a self-funder, simply select the "Create account" option on the login page.

If you have been booked onto a course by your company, you will receive a confirmation email. From this email, select "Sign into myQA" and you will be taken to the "Create account" page. Complete all of the details and select "Create account".

If you have the booking number you can also go here and select the "I have a booking number" option. Enter the booking reference and your surname. If the details match, you will be taken to the "Create account" page from where you can enter your details and confirm your account.

Find more answers to frequently asked questions in our FAQs: Bookings & Cancellations page.

How do QA’s virtual classroom courses work?

Our virtual classroom courses allow you to access award-winning classroom training, without leaving your home or office. Our learning professionals are specially trained on how to interact with remote attendees and our remote labs ensure all participants can take part in hands-on exercises wherever they are.

We use the WebEx video conferencing platform by Cisco. Before you book, check that you meet the WebEx system requirements and run a test meeting (more details in the link below) to ensure the software is compatible with your firewall settings. If it doesn’t work, try adjusting your settings or contact your IT department about permitting the website.

Learn more about our Virtual Classrooms.

How do QA’s online courses work?

QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course. When you book a QA online learning course you will receive immediate access to it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.

All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.

Learn more about QA’s online courses.

When will I receive my joining instructions?

Joining instructions for QA courses are sent two weeks prior to the course start date, or immediately if the booking is confirmed within this timeframe. For course bookings made via QA but delivered by a third-party supplier, joining instructions are sent to attendees prior to the training course, but timescales vary depending on each supplier’s terms. Read more FAQs.

When will I receive my certificate?

Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.

Contact Us

Please contact us for more information