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.
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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
Video: What is Amazon SageMaker?
This is an introductory video outlining the main features of Amazon SageMaker.
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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
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
Study guides for Machine Learning Implementation and Operations
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 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…
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…
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….
Amazon Textract
Overview Amazon Textract is used to convert scanned documents to text. This includes text in tables and hand written form. When text is extracted it is returned with coordinates that identify a box shaped area on the document. This allows for auditing later since the text can be traced back to a specific area in…
Amazon Comprehend
Overview Amazon Comprehend is used to analyse text to reveal insights and relationships in unstructured data. The data can be any type of free form text such as emails or text messages. For sentiment analysis Amazon Comprehend can tell you the overall sentiment of the text identifying if it was favourable to the subject, or…
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…
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…
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…
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…
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:…
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…
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…
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…
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:…
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:…
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…
Credits
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