Get certified as AWS Machine Learning — Specialty



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Get certified as AWS Machine Learning — Specialty

In this article, I provide feedback about my experience with AWS Machine Learning Specialty Exam (which I cleared with 95%).

Why you should take it ?

Take this exam if you want to boost your Data Science / Machine Learning Engineering career within the world of AWS or any other cloud provider. The high-level concepts learned while preparing for the ceritifcation can be easily applied to other cloud providers as well.

The offer is pretty similar, only the names of the services do change (e.g. AWS’s SageMaker is equivalent to Azure Machine Learning (Azure) or Google DataLab + Google CloudML combo in the world of GCP)

Future employers / clients will be more confortable hiring you as the certification validates the following abilities:

  • Select and justify the appropriate ML approach for a given business problem
  • Identify appropriate AWS services to implement ML solutions
  • Design and implement scalable, cost-optimized, reliable, and secure ML solutions

Overview of the exam and services to focus on

Unlike any other AWS certification, 80% of the exam questions aren’t about AWS services.

  • Machine Learning (80% of exam questions)
  • AWS Services (20% of exam questions)

The questions cover 4 different domains (they come in random orders)

  • Domain 1: Data Engineering (20% of Examination)
  • Domain 2: Exploratory Data Analysis (24% of Examination)
  • Domain 3: Modeling (36% of Examination)
  • Domain 4: Machine Learning Implementation and Operations (20%)

Here are the key concepts / AWS services to focus on during exam preparations :

Data Engineering:

  • AWS Services : Amazon S3 , Amazon FSx For Lustre , Amazon EFS , Amazon EBS , Kinesis (Data Streams, Firehose and Data Analytics) , Amazon DMS , Amazon Data Pipeline , Amazon Batch
  • Concepts : Data ingestion (batch and stream) , Data cleaning , ETL pipelines , Building Data Lakes on Amazon S3 , S3 lifecycle configuration and storage tiers

Exploratory Data Analysis

  • AWS Services : Amazon SageMaker GroundTruth , AWS Glue , Amazon EMR , Amazon Mechanical Turk , A2I (Augmented AI) , QuickSight
  • Concepts : Data Cleaning , Data labeling , Levaraging SageMaker’s Pipe mode , Data Viz techniques , Featue Engineering

Modeling

  • AWS Services : Amazon Sagemaker , SageMaker’s Automatic Model Tuning , SageMaker Python SDK , Amazon Comprehend , Amazon Transcribe , Amazon Rekognition , Amazon Polly , Amazon Lex , Amazon TextRact , Amazon BlazingText , Amazon Object2Vec , Amazon Translate
  • Amazon built-in ML algorithms : Linear Learner , K-Means , PCA , Factorization Machines , Random Cut Forest , Neural Topic Modeling , LDA , XGBoost , Seq2seq , DeepAR , Object Detection , Image Classification , Semantic Segmentation
  • Concepts : Hyperparameter tuning , Supervised , Unsupervised , Reinforcement learning , Deep Learning , Overfitting — Underfitting , Early Stopping , Classification metrics , Confusion Matrix , Model evaluation

Machine Learning Implementation and Operations

  • AWS Services : Amazon Elastic Inference , SageMaker Inference Pipeline , SageMaker Neo , CloudWatch , CloudTrail , Augmented AI (A2I)
  • Concepts : ML monitoring , data governance , Multi-model endpoints , Data encryption best practices , Lifecyle configuration scripts , Optimizing models for edge-devices

Preparation resources

1.Study material:

Official AWS material

  1. Machine Learning Terminology and Process
  2. Machine Learning Algorithms
  3. Math for Machine Learning
  4. AWS Foundations: Machine Learning Basics
  5. AWS Machine Learning Lens
  6. Machine Learning Best Practices in Financial Services
  7. Neural Networks
  8. Introduction to Artificial Intelligence
  9. Amazon Sagemaker

Wonderful Youtube Channel : StatQuest

2. MOOC : Frank Kane and Stephan Mareek’s wonderful AWS Machine Learning Specialty preparation course on Udemy

What I like about this MOOC ?

  • Hands-on labs (Data Engineering , Feature Engineering , Model Tuning with SageMaker)
  • Frank Kane is a former Amazon employee who happens to be a Big Data and Machine Learning Expert. He did a great job explaining the ML part of the course
  • Stephane Maarek : 9x AWS Certified and Kafka Expert. He knows a whole lot about AWS Services and how they mix together. His explanations are pretty clear.

3. Practice exams: Tutorial Dojo’s practice AWS ML Specialty Practice Exams

Share and clap if you find it helpful. Thank you 🙏

AI/ML

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