AWS SageMaker - Certified Machine Learning Specialty Exam

• You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud
• AWS Built-in algorithms, Bring Your Own, Ready-to-use AI capabilities
• Complete Guide to AWS Certified Machine Learning – Specialty (MLS-C01)
• Includes a high-quality Timed practice test(a lot of courses charge a separate fee for practice test)

Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep

*** NEW Labs - A/BTesting, Multi-model endpoints ***

*** NEW section Emerging AITrends 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!