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.

Scroll down for test app questions …

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.

Video: What is Amazon SageMaker?

This is an introductory video outlining the main features of Amazon SageMaker.

A 14.25 minute video by Mike Chambers

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Whizlabs AWS Certified Machine Learning Specialty

Practice Exams with 271 questions, Video Lectures and Hands-on Labs from Whizlabs

Whizlab’s AWS Certified Machine Learning Specialty Practice tests are designed by experts to simulate the real exam scenario. The questions are based on the exam syllabus outlined by official documentation. These practice tests are provided to the candidates to gain more confidence in exam preparation and self-evaluate them against the exam content.

Practice test content

  • Free Practice test – 15 questions
  • Practice test 1 – 65 questions
  • Practice test 2 – 65 questions
  • Practice test 3 – 65 questions
Whizlabs AWS certified machine learning course with a robot hand

Section test content

  • Core ML Concepts – 10 questions
  • Data Engineering – 11 questions
  • Exploratory Data Analysis – 13 questions
  • Modeling – 15 questions
  • Machine Learning Implementation and Operations – 12 questions

Machine Learning Implementation and Operations test questions

10

4 Machine Learning Implementation and Operations

Five questions from a test bank of 55 questions about domain 4, Machine Learning Implementation and Operations

1 / 5

<–?–> is an open source Interface. It is independent of the major IT vendors, but the main contributor is a Google engineer. The APIs make TensorFlow easier to work with. They are simple and consistent to minimise the number of user actions for common use cases.

5 characters left

2 / 5

How are SageMaker features used in the EMR Spark environment?

3 / 5

<–?–> is the ability for a Machine Learning application to remain in operation even if some of the components used to build the system fail.

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4 / 5

What are models that run inside SageMaker deployed on?

5 / 5

What are the Server Side Encryption methods used with Machine Learning?

Study guides for Machine Learning Implementation and Operations

a photo of a red parrot to symbolize the amazon ai service amazon poly
ML implementation and Operation (Domain 4)

Amazon Poly

Overview Amazon Polly converts text to speech, allowing you to build speech enabled services. Polly can translate text to speech (TTS) to produce realistic voice messages to which a user can take action, or respond as part of a conversation. Video: Text-to-Speech with Amazon Polly Key features of Amazon Poly Amazon Poly use cases Video:…

a photo of a young woman with shopping bags to symbolize the amazon ai service amazon personalize
ML implementation and Operation (Domain 4)

Amazon Personalize

Overview Amazon Personalize draws on features that Amazon incorporates into their own retail website. This includes personalization experiences, including specific product recommendations, personalized product re-ranking, and customized direct marketing. The Amazon Personalize AI service provides personalisation with AWS doing all the heavy lifting of providing the Machine Learning infrastructure to train and deploy the model….

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…

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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…

a photo of three call phones on a wall to symbolize the chatbot amazon ai service amazon lex
ML implementation and Operation (Domain 4)

Amazon Lex

Overview Lex provides natural language chatbot capability. It is based on the same technology as Amazon Alexa. With Lex a user can communicate by voice as part of a conversation to achieve their desired goal or intent. Lex analyses what the user says, this is termed an utterance. From a few text examples Lex can…

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 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. Scroll to…

a photo of a man sitting in a classroom taking notes to symbolize the amazon ai service amazon transcribe which converts speech to text
ML implementation and Operation (Domain 4)

Amazon Transcribe

Overview Amazon Transcribe converts speech to text, by using Automatic Speech Recognition (ASR) technology which is the same underlying technology used by Amazon Alexa. Transcribe can work with multiple languages and speakers and incorporate custom vocabulary provided by the user. Transcribe can be configured to remove sensitive text, such as PII information or swearing. Video:…

a photo of brightly coloured flat flower models with goodbye written and many languages to synbolize the amazon ai service amazon translate
ML implementation and Operation (Domain 4)

Amazon Translate

Overview Amazon Translate translates text from one language to another. You can translate individual words, phrases, or entire documents. An API is provided, enabling either real-time or batch translation of text from the source language to the target language. Video: What is Amazon Translate? Key features Use cases Translate use cases have three features in…

a photo of a desk with a graph drawn on a sheet of paper to sumbolize the forcast aws ai service
ML implementation and Operation (Domain 4)

Amazon Forecast

Overview Amazon Forecast uses historical time series data combined with user provided parameter data to generate predictions. The service requires time series data as an input. This can be argumented with local weather data. The desired quantile or mean forcast can be selected and the forcast is output as a CSV file. The output is…

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…

a photo showing a photo album with photos to symbloiz the amazon ai service amazon rekognition
ML implementation and Operation (Domain 4)

Amazon Rekognition

Amazon Rekognition overview Rekognition is an AWS SageMaker AI service for image recognition. Rekognition identifies objects, people, text, scenes and activities in images and video. Rekognition has both built in recognition capabilities and custom tags which allow you to label objects and people important to your business. When a tag, or label is identified Rekognition…

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 photo of the bare gavel surface with a battered metal sign with the word truth to symbolize amazon sagemaker ground truth
ML implementation and Operation (Domain 4)

Amazon SageMaker Ground Truth

Ground Truth Overview Amazon SageMaker Ground Truth is a service you can use to manually label data. This provides high quality labelled data in the preprocessing stage to be used to train Supervised Learning models. Training data is sent to AWS and they take care of the rest returning your data with attached labels processed by…

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ML implementation and Operation (Domain 4)

Amazon AI Services

Overview of Amazon AI Services AI services are AWS’s premiere value-add services. They are easy to use and incorporate to enhance existing systems or as completely new systems. Because they are services AWS does all the heavy lifting leaving the user to interact with the services without having to set up infrastructure or other supporting…

Credits

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