Machine Learning Implementation and Operations

Robotic arms in a factory building a car symbolising the Machine Learning production environment

This Domain is about the production environment and related features to make everything work. It comprises 20% of the exam marks. There are four sub-domains.

Building highly available fault tolerant systems in the production environment relies on separating components of a system into a loosely coupled distributed system. This ensures that failure in one part of the system is less able to effect other parts of the system. AWS services and features then enable decoupling are SQS, CloudWatch, CloudTrail and SageMaker Notebook end points.

Scalability is the property of a system to automatically provision more resources when needed and to scale back those resources to reduce waste when demand is low. AWS services and features that enable scalability are Autoscaling and containerised ML models, which are Docker images.


AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) Exam

This study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. The online resources that accompany this Study Guide include practice exams and assessments, electronic flashcards, and supplementary online resources. It is available in both paper and kindle version for immediate access. (Vist Amazon books)


Sample Machine Learning Implementation and Operations questions

This test has 5 questions randomly taken from 20 questions from the tests of the four sub-domains.

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4 Machine Learning Implementation and Operations

This quiz has five questions randomly selected from all the Machine Learning Implementation and Operations quiz questions.

1 / 5

What does an IAM Policy Statement comprises?

2 / 5

3 / 5

What are the steps to creating a SageMaker Endpoint?

4 / 5

The AI service <–?–> can be used for sentiment analysis?

1 words left

5 / 5

What frameworks does SageMaker support?

Study guides for Machine Learning Implementation and Operations

a birds next with an egg symbolising the production environment
ML implementation and Operation (Domain 4)

The Machine Learning Production Environment

When you launch a Machine Learning solution in production it needs to perform well to provide the business benefit it was designed for. There are two types of performance: This Study Guide focuses on the production environment. The production environment can be assessed using five measures: There are three curated videos in this Study Guide:…

A UK Guards soldier in a sentry box symbolizing AWS IAM security for Machine Learning
ML implementation and Operation (Domain 4)

AWS security for machine learning

Security is a vast subject and AWS even have their own Professional level certificate exam on this subject. Using the AWS course: Exam Readiness: AWS Certified Machine Learning – Specialty as a guide these revision notes give an overview of the main AWS security service Identity and Access Management (IAM) and then highlight security features…

a gym with exercise equipment to symbolize Machine Learning services from AWS
ML implementation and Operation (Domain 4)

Machine Learning services and features

Using SageMaker AI services is like visiting a well equipped gym, you just have to choose the right equipment for your goals. AWS has a wide range of Machine Learning services and capabilities, each one has its own advantages and disadvantages. Understanding your use case is key to selecting the most appropriate service. Questions These…

Pluralsight AWS Certified Machine Learning web page screen shot
Reviews

Pluralsight review – AWS Certified Machine Learning Specialty

Contains affiliate links. If you go to Whizlab’s website and make a purchase I may receive a small payment. The purchase price to you will be unchanged. Thank you for your support. The AWS Certified Machine Learning Specialty learning path from Pluralsight has six high quality video courses taught by expert instructors. Two are introductory…

Space X Falcon Super Heavy rocket launch symbolizing deploy a Machine Learning Model into production
ML implementation and Operation (Domain 4)

Deploy and operationalize machine learning solutions

This Study Guide describes how to deploy a Machine Learning Model into the production environment and to monitor it once it is deployed. The foundations of a reliable production environment are good Software Management and Software Engineering. The emerging job role of ML Ops, which is derived from Dev Ops, is focused on delivering the…

a canary in a carge to symbolize a Canary Deployment
ML implementation and Operation (Domain 4)

Canary deployment, Blue/Green deployment and A/B testing compared

Overview Canary deployment, Blue/Green deployment and A/B testing are methods to control risk. SageMaker Endpoints support these methods. Canary deployment A Canary release is a very risk averse deployment strategy. It involves directing a small proportion of the live traffic to the new production variant and checking that everything works as expected. The proportion of…

Amazon Study Guide for the AWS Machine Learning Speciality exam
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Amazon Study Guide review – AWS Certified Machine Learning Specialty

This Amazon Study Guide review is a review of the official Amazon study guide to accompany the exam. The study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. The online resources that accompany this Study Guide include practice exams and assessments, electronic…

Whizlabs AWS certified machine learning course with a robot hand
Reviews

Whizlabs review – AWS Certified Machine Learning Specialty

Need more practice with the exams? Check out Whizlab’s free test with 15 questions. They also have three practice tests (65 questions each) and five section tests (10-15 questions each). Money off promo codes are below. For the AWS Certified Machine Learning Specialty Whizlabs provides a practice tests, a video course and hands-on labs. These…

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