AWS Machine Learning Specialty Exam Study Notes
I recently completed the AWS Machine Learning Specialty Exam. As part of my studying process, I created a set of notes, which include high-level overviews of all the topics I needed to know, along with a glossary of key terminology.
Based on the feedback I received for my previous set of exam study notes that I shared for the Solutions Architect Associate exam, I have decided to release my notes for this exam as well, in a series of blogs!
One thing to note is that these are just overall summaries, so I would not recommend solely using these for your studies, but to use as an overview or as some reading to do the week of your exam.
This exam covers 4 main domains:
- Data Engineering
- Exploratory Data Analysis
- Modelling
- Machine Learning Implementation and Operations
Topics
To answer questions related to these domains, you need to be familiar with machine learning theory and various AWS services. Click on the links to the different blogs below to start working through the summaries I have created:
- Types of Data
- Data Storage
- Data Preparation & Processing Techniques
- AWS Data Preparation Services
- Data Visualisations
- Data Streaming with AWS Kinesis
Still to come
I am still working on the rest of my study note blogs, so watch this space for the next chapters of my notes to be released, they will include:
- Algorithms
- Model Evaluation and optimisation techniques
- AWS ML Managed services
- Security
- and some more exam hints and tips
