Checkout our recent webinar and learn more about how this course can enhance your career!
We’re collaborating with machine learning practitioner and instructor Luigi Patruno to bring his 6-week course, Building, Deploying, and Monitoring Machine Learning Models with Amazon SageMaker, to the TWIML Community. Running machine learning systems in production is hard work. If you’re a data scientist or ML engineer responsible for building models, you shouldn’t spend all your time configuring complex infrastructure to deploy your models. Yet that’s what happens in data science teams struggling to put their models in production.
But it doesn’t have to be this way. The best way to leverage what big tech companies have learned about running production ML systems is to use a centralized ML platform. In this course, Luigi will teach you how to use Amazon SageMaker, an end-to-end machine learning platform, to build, deploy, and monitor machine learning models in production.
SageMaker is built to solve the challenges of operating machine learning models at scale in a reliable and fault tolerant manner. No more countless hours spent figuring out how to distribute ML algorithms over clusters. No more configuring networking rules, manually spinning up EC2 instances, or writing APIs. SageMaker makes it easy to run large scale experiments, record experimental metadata, deploy optimized models, and monitor your models so you can focus on what you do best: building powerful predictive models.
TWIML’s 2020 Data Science Survey has shown AWS SageMaker to be one of the most sought after ML production tools and we couldn’t be more excited to bring this course to our community. Build your knowledge, get up to speed on one of the hottest ML products of the year, and enhance your career by joining us for this course!
Frequently Asked Questions
- How do I enroll in the course? Enrollment can be completed via the course enrollment page here. Enroll today and use the discount code TWIML to get an additional 10% off!
- What will I learn in this course? The techniques taught in this course will help you build scalable, efficient, and fault-tolerant machine learning systems. You will learn to use Amazon SageMaker to:
- Train machine learning models using top frameworks like scikit-learn, xgboost, tensorflow, and PyTorch
- Run large scale experiments like hyperparameter sweeps over a distributed cluster without needing to know anything about EC2
- Easily collect and analyze data from experiments.
- Perform offline inference on a batch of data
- Deploy models as persistent endpoints that automatically scale with demand.
- Monitor deployed endpoints to detect concept drift.
- What does this course include? The hands-on resources and materials to enhance your learning throughout this course include:
- Condensed overview of the challenges of running production machine learning systems
- In-depth explanations of how Amazon SageMaker solves production ML challenges
- A step-by-step walkthrough of setting up your own SageMaker Studio development environment and connecting to a GitHub repository
- Jupyter Notebooks containing sample code demonstrating how to train, deploy and monitor ML models.
- Screencasts walking you through how to use the AWS console to interact with SageMaker and S3
- Recommendations on the best ways to set up common ML workflows like automated model retraining
- How will this course benefit me and my career? The benefits of taking this course include:
- Equip yourself with the best practices and patterns for production ML systems.
- Improve your marketability as an ML professional by gaining highly in demand MLOps skills
- Learn directly from a trusted industry expert and advanced practitioner.
- Who is this course for? This course is designed for the following types of people:
- ML engineers and data scientists who want to deploy their models to production.
- Managers and Leaders of Data Science teams who wish to enable efficient practices within their organizations.
- Software developers seeking to move into machine learning.
- Site Reliability Engineers (SREs) and other DevOps responsible for deploying ML models.
- Note: If you aren’t familiar with Amazon Web Services, no problem! We’ll provide overviews of the relevant services used in the course.
- How long will each course run? What is the level of effort expected? The study group for this course will kick-off at 9am PT / 12pm ET on August 8th. These hour and a half sessions will run for six consecutive weeks with a targeted completion date of September 12th.
- Is there a discount for TWIML participants? Glad you asked. Yes, to kick off this partnership, Luigi has agreed to extend a discount to TWIML community members who register. Please use discount code TWIML when you enroll to get the TWIML community discount!
- What programming languages/frameworks are used in this course? This course utilizes Python 3 as the main programming language. In order to interact with Amazon SageMaker, we rely on the SageMaker Python SDK and the SageMaker Experiments Python SDK. Additionally, we’ll train models using the scikit-learn, XGBoost, Tensorflow, and PyTorch frameworks and associated Python clients.
- Is TWIML paid as part of this arrangement? Yes, we are a Luigi Patruno / Teachable affiliate and get a commission as part of the partnership. Whatever we earn through this relationship will help support our broader community and educational efforts. That said, we would never recommend a course we didn’t think was a good use of your time and a good value.
- How long will students have access to the course materials? After you enroll, you will have access to the materials indefinitely.
- How long will enrollment for the course be open? How long will the discount be available? While the course itself is fundamentally designed for self-paced study, with Luigi running a live weekly study group, we will be closing enrollment on Friday, August 7th at 11:59 PT. The TWIML discount will be available until the close of enrollment.
- Will the weekly study group sessions be open to anyone? Luigi’s weekly study group sessions are intended for enrollees and will assume that learners have at least gone over that week’s lectures at a high level.
- Is there a detailed syllabus? Yes, the syllabus can be found here.
- What can I expect from the weekly online sessions? The weekly online sessions are live study group sessions presented by Luigi, the instructor and author of the Building, Deploying, and Monitoring Machine Learning Models with Amazon SageMaker course. At the sessions, Luigi will present a summary of that week’s course materials and open the floor for student discussion.
- What if I cannot participate in the weekly online sessions? The weekly study group sessions will be recorded and will be available to TWIML enrollees.
- What is the refund policy? There is a 14-day refund policy on the course.