Machine Learning Implementation and Operations
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.
- 4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
- 4.2 Recommend and implement the appropriate machine learning services and features for a given problem.
- 4.3 Apply basic AWS security practices to machine learning solutions.
- 4.4 Deploy and operationalize machine learning solutions.
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.
- For description of the exam structure see this article: AWS Machine Learning exam syllabus
- The AWS exam guide pdf can be downloaded from: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide.pdf
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.
Study guides for Machine Learning Implementation and Operations
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:…
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…
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 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…
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…
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 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 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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