AWS Machine Learning Specialty Cheat sheet – Blog

AWS Machine Learning Specialty Cheat sheet – Blog

The AWS machine learning specialty is the most difficult of all the certificates that Amazon offers. This type of IT certification can be difficult to obtain and requires a deep understanding of the subject. You can achieve this certification if you have the right resources and a well-planned approach. Our Ultimate Cheat Sheet will help you get there. The AWS Machine Learning Specialty cheat sheet gives you the right mix of tools and approaches to help get this highly sought-after certification. Before we get to the cheatsheet, let’s take a look at the specifics of the test.
What is AWS Machine Learning Specialty and how can it help you?
AWS Machine Learning specialty certification exam can be used for Amazon Web Services offering. It allows a developer to use algorithms to identify patterns in end-user information, build mathematical models based upon these patterns, then create and execute predictive apps. This exam tests a candidate’s ability use AWS Cloud to build, train, tune, deploy, and maintain machine learning (ML). It evaluates a candidate’s ability to build, deploy, manage, and maintain machine learning (ML), solutions for a variety business problems. It will show that the candidate is able to:
Choose and justify the best ML approach to solve a business problem.
Identify the appropriate AWS services for implementing ML solutions.
Implement scalable, cost-optimized and reliable ML solutions that are secure, reliable, and scalable
AWS Machine Learning Certification Prerequisites:
According to Amazon, candidates for the AWS machine-learning specialty test must have the following knowledge and experience:
First, 1-2 years experience in developing, architecting or running ML/deep-learning workloads on AWS Cloud.
The ability to express intuition behind basic ML algorithms is then gained.
Also, experience in basic hyperparameter optimization.
Additionally, experience with deep learning frameworks and ML.
The ability to follow best practices in model-training.
Finally, the ability follow operational best practices and deployment.
Cheat Sheet : AWS Machine Learning Specialty
The AWS Machine Learning Specialty cheat sheet is all you need to start your revisions. It will give you a quick overview of the materials that you will need to pass the test. It is your ticket to your certificate.
1. Exam Objectives: Familiarize yourself
Gather all course and test regulations information. Before you start your exam preparations, it is important to familiarize yourself with the exam course. The exam’s outline is the course outline. It covers all the important test elements and ideas that will be covered on the exam. The Exam Guide is required to pass the exam. This AWS Machine Learning Certification Course covers the following domains:
Domain 1: Data Engineering
First, create data repositories to support machine learning. This module is described in Amazon documentation: Use Amazon S3 as a repository for data, Amazon Redshift as a source of data, Amazon RDS Database to access Amazon ML Datasources.
Secondly, you must identify and implement a data ingestion solution. (AWS Documentation – Data Ingestion Methods in AWS. Learn how data is ingested using Amazon SageMaker and a Data Lake. How Kinect Energy ingests information to forecast energy prices.
Third, identify and implement a data transformation solution. (AWS Documentation:N-gram Transformation,Orthogonal Sparse Bigram (OSB) Transformation,Lowercase Transformation,Data Rearrangement: Create datasource based on a section of the input data)
Domain 2: Exploratory Data Analysis
First, clean and prepare the data for modeling. (AWS Documentation:Prepare your data in Amazon Machine Learning,Use Amazon SageMaker Ground Truth for Data Labeling,Prepare data in Amazon SageMaker)
Secondly, perform feature engineering. (AWS Documentation:Understanding the Importance of Feature Transformation,Feature Processing in Amazon Machine Learning,Feature Processing using Spark & Scikit-learn in SageMaker)
Finally, analyze and visualize data to support machine learning. (AWS Documentation:Analyzing Data with Amazon Machine Learning,Explore, Analyze & Process data,Visualizing the distribution of data,Visualizing insights for binary models,Visualizing insights for Regression models)
Domain 3: Modelling
First, consider business problems as machine-learning problems. (AWS Documentation:Resources from AWS: Formulating the Problem,Resources from Amazon: Solving Business Problems with Amazon ML)
Next, choose the right model(s) to solve the given machine learning problem. (AWS Documentation.Amazon Machine Learning: Types and ML Models).
You can also train machine learning models. (AWS Documentation:Build, Train, and Deploy a Machine Learning Model with SageMaker,Train a Model with Amazon SageMaker,Incremental training of model in SageMaker,Training with Amazon EC2 Spot Instances,Train a Deep Learning model)
Also, optimize hyperparameters. (AWS Documentation:Understanding the Training Parameters,Hyperparameters available in Amazon ML,How does Hyperparameter Tuning work?,Defining Hyperparameter Ranges,Best Practices for Hyperparameter Tuning)
Final, evaluate machine learning models. (AWS Documentation – Binary Model Insights and Multiclass Model Insights. Regression Model Insight. Understand the Cross-validation technique to evaluate ML Models. Evaluating Model Fit: Overfitting vs. Underfitting.
Domain 4: Machine Learning Implementation & Operations
First, create machine learning solutions that improve performance, availability, scalability and fault tolerance. (AWS Documentation):Review the ML Model’s Predictive performance, Deploy Multiple Instances Across Availability Zones. Amazon SageMaker: Infinitely Scalable Machine Learning Methodms. Read this Whitepaper: Power Machine Learning on Scale.
Afterwards, recomm.