All Software Architecture, All the Time
June 10-13, 2019
San Jose, CA

Managed machine learning systems and internet of things

Noah Gift (UC Davis ), Robert Jordan (Pragmatic AI Labs)
1:30pm–5:00pm Tuesday, June 11, 2019
Secondary topics:  Framework-focused
Average rating: **...
(2.25, 4 ratings)

Who is this presentation for?

  • Technical leaders, software developers, business and analytics professionals, and junior data scientists

Level

Beginner

Prerequisite knowledge

  • A basic understanding of Python, data science, and the cloud

Materials or downloads needed in advance

  • A Google Colaboratory account
  • A laptop (with Python 3.6 and the Jupyter Notebook installed if you can't use Google Colab notebooks)
  • XCode 9.4 or higher installed (useful but not required)
  • An AWS, GCP, and Azure Account (useful but not required)

What you'll learn

  • Utilize hardware AI to build products including TPUs, GPUs, FPGAs, and A11 Bionic
  • Compare, choose, and implement auto and managed machine learning systems including Google Cloud AutoML, AWS Sagemaker, and Azure Machine Learning Studio
  • Perform IoT programming fundamentals with Python, AWS IoT Greengrass, AWS Deep Lens, and a Raspberry Pi
  • Build production machine learning models with AWS Sagemaker
  • Create iOS Core ML2 applications with Swift playgrounds
  • Train a computer to identify numbers in handwriting and identify cars in pictures

Description

The next evolution of AI and ML is cloud native, managed platforms and custom hardware AI, inclusive of things like custom AI chips, internet of things devices, and more. AWS has Sagemaker, which allows for a fully managed build, train, and deployment lifecycle, including automatic hyperparameter tuning. Microsoft Azure has Azure ML Studio, which includes high-level tools that allow for drag-and-drop workspace machine learning workflows. Google has AutoML, which allows developers with limited machine learning expertise to train high-quality models by automatically inferring the correct hyperparameters and model to use.

Noah Gift and Robert Jordan teach you how to use these managed platforms to create solutions in a fraction of the time as a “roll your own" ML solution. Additionally, learn to compare how each cloud-managed solution compares and be able to pick the right solution for the task at hand. Learn to use these hardware devices to create end-to-end ML solutions by integrating both IoT and on-device ML models. Along the way, Noah and Robert dive into two real-world examples: Apple, which has developed a dedicated AI chip used in the Core ML2 framework, and AWS DeepLens, which can run Sagemaker-trained models. Both hardware AI and managed ML services are tightly coupled and rapidly innovating. In the very near future, it’s possible all production machine learning will be using hardware AI components of some variety (IoT, GPU, TPUs) and managed and auto ML solutions.

Photo of Noah Gift

Noah Gift

UC Davis

Noah Gift is lecturer and consultant at both the UC Davis Graduate School of Management MSBA Program and the Graduate Data Science Program at Northwestern, where he designs and teaches graduate machine learning, AI, data science courses and consults on machine learning and cloud architecture for students and faculty. These responsibilities including leading a multicloud certification initiative for students. As the founder of Pragmatic AI Labs, he also consults with companies on machine learning, cloud architecture, and CTO-level concerns. In the last 10 years, he’s been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had global scale. His previous roles have included CTO, general manager, consulting CTO, consulting chief data scientist, and cloud architect, at companies such as ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios, and Linden Lab. As an SME on machine learning for AWS, he helped created the AWS machine learning certification.

Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on machine learning, AWS Certified Solutions Architect and AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. He has published close to 100 technical publications, including two books on subjects ranging from cloud machine learning to DevOps, for companies like Forbes, IBM, Red Hat, Microsoft, O’Reilly, and Pearson. He’s also led workshops and talks around the world, for organizations including NASA, PayPal, PyCon, Strata, and Foo Camp. His most recent book is Pragmatic AI: An Introduction to Cloud-Based Machine Learning (Pearson), and his most recent video series is Essential Machine Learning and AI with Python and Jupyter Notebook LiveLessons. He holds an MBA from UC Davis, an MS in computer information systems from Cal State Los Angeles, and a BS in nutritional science from Cal Poly San Luis Obispo.

Photo of Robert Jordan

Robert Jordan

Pragmatic AI Labs

Robert Jordan is a visionary architect with over 20 years designing, implementing, and deploying production applications for some of the world’s largest media and scientific customers. He has successfully led projects on all major cloud platforms and is currently certified on both AWS and GCP platforms.

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Comments

Abdulrahman Mansour Sanad | TECHNICAL MANAGER
07/08/2019 7:06am PDT

How I can download the presentation?

Prabhat Menon | SENIOR ARCHITECT
06/14/2019 6:21am PDT

Thanks Noah! I appreciate the advise and overview of different developments in Hardware AI and cloud offerings… Appreciate it!

Picture of Noah Gift
Noah Gift | MACHINE LEARNING LECTURER
06/10/2019 12:38pm PDT

@prabhat

almost everything will be browser based. I would recommend a GCP account

Prabhat Menon | SENIOR ARCHITECT
06/08/2019 7:04pm PDT

Hi, Does this tutorial need a Mac and whether a Win 10 or Linux laptop work? What IDE would be recommended? Spyder/anaconda. Thanks!