Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep
*** NEW Labs – A/B Testing, Multi-model endpoints ***
*** NEW section Emerging AI Trends and Social Issues. How to detect a biased solution, ensure model fairness and prove the fairness ***
*** New Endpoint focused section on how to make SageMaker Endpoint Changes with Zero Downtime ***
*** Lab notebook now use spot-training as the default option. Save over 60% in training costs ***
*** NEW: Nuts and Bolts of Optimization, quizzes ***
*** All code examples and Labs were updated to use version 2.x of the SageMaker Python SDK ***
*** Anomaly Detection with Random Cut Forest – Learn the intuition behind anomaly detection using Random Cut Forest. With labs. ***
*** Bring Your Own Algorithm – We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs ***
*** Timed Practice Test and additional lectures for Exam Preparation added
Welcome to AWS Machine Learning Specialty Course!
I am Chandra Lingam, and I am your instructor
In this course, you will gain first-hand SageMaker experience with many hands-on labs that demonstrates specific concepts
We start with how to set up your SageMaker environment
If you are new to ML, you will learn how to handle mixed data types, missing data, and how to verify the quality of the model
These topics are very important for an ML practitioner as well as for the certification exam
SageMaker uses containers to wrap your favorite algorithms and frameworks such as Pytorch, and TensorFlow
The advantage of a container-based approach is it provides a standard interface to build and deploy your models
It is also straightforward to convert your model into a production application
In a series of concise labs, you will in fact train, deploy, and invoke your first SageMaker model
Like any other software project, ML Solution also requires continuous improvement
We look at how to safely incorporate new changes in a production system, perform A/B testing, and even rollback changes when necessary
All with zero downtime to your application
We then look at emerging social trends on the fairness of Machine learning and AI systems.
What will you do if your users accuse your model as racially biased or gender-biased? How will you handle it?
In this section, we look at the concept of fairness, how to explain a decision made by the model, different types of bias, and how to measure them
We then look at Cloud security – how to protect your data and model from unauthorized use
You will also learn about recommender systems to incorporate features such as movie and product recommendation
The algorithms that you learn in the course are state of the art, and tuning them for your dataset is especially challenging
So, we look at how to tune your model with automated tools
You will gain experience in time series forecasting
Anomaly detection and building custom deep learning models
With the knowledge, you gain here and the included high-quality practice exam, you will easily achieve the certification!
And something unique that I offer my students is a weekly study group meeting to discuss and clarify any questions
I am looking forward to seeing you!
Thank you!
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-drivi…
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