Premium Hands-on Workshop: ML-Ops, Cloud for Successful ML Products

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Refunds up to 7 days before event

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Event description
This is a workshop that focuses on machine learning in deployment environment and related best practices

About this Event

Workshop Overview

In this hands-on workshop, you’ll learn about MLOps which includes the technologies, processes and mindset required for a successful and efficient machine learning development and operation lifecycle. This equips you to track, monitor, certify and reuse every asset built or used throughout the lifetime of an ML project. We’ll walk you through the best practices for building reproducible experiments, developing ML models, launching them in the wild and monitoring their health during their lifetime. As a cloud-native concept, we cover the materials on three major cloud providers; Microsoft Azure, Amazon Web Services (AWS) and Google Cloud Platform (GCP) with the primary focus on Microsoft Azure. After the workshop, you will be able to build end-to-end machine learning pipelines based on best practices on most major public cloud platforms.

Note: for most of the concepts covered in this workshop, participants can use their free tier cloud account but for some functionalities credit (rough estimate $50) is required. We assume that participants make arrangements for this credit on their own.

Important Dates

Early Bird Deadline: September 26, 2019

Refund Deadline: October 3, 2019

Sales end on: October 9, 2019

Please note that this workshop will happen on 3 separate evenings:

Session 1: October 10, 2019

Session 2: October 17, 2019

Session 3: October 24, 2019

"Why should I care about ML-Ops?"

Understanding what it takes to have a successful deployment strategy is a key differentiator in the competitive analytics space. Deploying machine learning models into the production environment is challenging to the extent that many teams fail at it. There are several factors contributing to this but one of the main reasons is the disconnect between the lifecycle of ML development and ML operations. This gulf makes the integration of an ML model into the broader application sometimes impossible. Recent cloud-native technological innovations have helped narrow down this gap to the degree that some data science teams work as part of the engineering unit and own the full ML lifecycle, from development to deployment and operations. These innovations, aka MLOps, are a set of tools, processes and more importantly mindset which aim to build reproducible, testable, and maintainable machine learning models that work continuously well in the wild.

Why you should attend

In this 3-session intensive workshop, we will bring you up to speed with everything needed to build a strong background in ML-Ops. It will be a combination of theory and hands-on applications on Azure, AWS, and GCP.

This workshop is built on the instructors extensive experience in academia and industry on related topics.

This workshop is the first in its series and paves the way theoretically and technically for many application specific workshops to follow.

Join in-person or online

Watch and rewatch the video recordings as often as you need during the 3 weeks of the workshop, and interact with other participants and TAs on our Slack

Need more information?

Send us any questions you might have (events@ai.science), or

Join us for a Q&A session for an opportunity to hear from the instructor and ask them questions about the workshop: https://mlops-lnl.eventbrite.ca

Target Audience

Data Scientists, Machine Learning Engineers, Software Engineers, Students, Any ML/DS practitioner with basic understanding in ML, Other IT roles

Prerequisites

> Familiarity with Python

> Basic familiarity with Cloud functionalities such as spinning up VMs, creating databases, working with Functions etc

  • Aure: https://bit.ly/2AfJAvH
  • AWS: https://bit.ly/2kp5Gbg
  • GCP: https://bit.ly/2tZzgCU

> High-level knowledge of ML

> Activate Free or Pay-as-you-go subscription on Azure and/or AWS and/or GCP before the class

  • https://azure.microsoft.com/en-us/free/
  • https://aws.amazon.com/free/
  • https://cloud.google.com/free/?hl=ru

Learning Outcomes

In this workshop you will learn how to build a full ML pipeline from data prep to model deployment and operations

  • Mastering Machine Learning Model Development LifeCycle
  • Understanding challenges related to ML model in production setting and how to resolve those challenges
  • Getting hands-on industry-level experience on how to leverage a cloud environment to streamline the ML lifecycle

Pre-workshop reading material

  • http://aka.ms/mlops
  • https://github.com/awslabs/amazon-sagemaker-mlops-workshop
  • https://github.com/AlexIoannides/kubernetes-ml-ops
  • https://mlops.org/posts/

Learning Material

All online and in-person participants will have access to the following learning material:

  • Slides from the sessions
  • Hands on notebooks
  • Video recording of the sessions (you can use the videos to watch the parts that you missed, or re-watch any parts that are still unclear for you; access to videos beyond one week after the workshop is available to be purchased; see tickets >> add-ons)

Instructor

Hossein Sarshar

Sr. Data Scientist, Microsoft

Hossein Sarshar is a Senior Data Scientist at Microsoft which helps Microsoft enterprise customers solve their data science problems on Microsoft Azure. Prior to his 5-year data science career, he was a software engineer for almost a decade. Hossein holds a bachelor's and a master's degree in Computer Science with focus on parallel & distributed computing and machine learning, respectively.

Course Modules

The workshop happens on 3 evenings, and covers the following topics:

Day 1: Cloud, DevOps and MLOps fundamentals:

  • Cloud concepts, technologies and architecture patterns for Machine Learning
  • DevOps and software engineering best practices required for DS/ML practitioners
  • Hands-on: Build reproducible ML experiments

Day 2: Model management and governance

  • Dockers and containers
  • Kubernetes or other container orchestration options
  • Model Lake - Microservice design for machine learning models
  • Test and certify ML models in CI/CD pipeline
  • Hands-on: integrate the ML model into CI/DC pipeline

Day 3: Model deployment and consumption

  • Deployment strategies
  • Production requirements - model scalability and others
  • Model monitoring
  • Auto-retraining and AutoML
  • Tests in the wild
  • Hands-on: Build a full CI/CD pipeline from model development to model consumption

█░ Kick-starter style

Please note that our technical workshops are run kick-starter style in the sense that they will only happen if a certain amount of funds are raised by the 2-week deadline prior to the event (Sept 26, 2019):

Funds raised so far to enable the workshop:

██████████ 100%

Learning Packages

You can customize your learning package. Click on tickets, select your base package (in-person or online), and then tailor your experience the way you want using add-ons.

There is early bird public discount starting at 40% and decreasing by 10% every week until the end of early bird on Sept 26th, 2019

Discount Codes

There are discount codes available

  • AISC members receive discounts according to their participation credit; refer to the slack channel for more detail

Referral Program

AISC Members, have personalized referral discount codes up to 50% that they can share with their friends and coworkers. Each code can be used 3 times. In order to ensure that people are using the code with your permission they need to enter your email address when registering

Non-members, can enjoy a group discount (10% on top of the current early bird discount) by forming groups of 3+ and registering together. So, go talk to your friends or coworkers right now so that you all save 10% more together.

Date and Time

Refund Policy

Refunds up to 7 days before event

Eventbrite's fee is nonrefundable.

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