Is AWS Machine-Learning Certification worth it? Blog

The AWS Machine Learning Specialty certification (MLS-C01), was created to test your knowledge in applied machine and deep learning. This certification is valid both within AWS but also outside of it. Even the most experienced data scientists and machine-learning developers find the exam difficult, even if they have done extensive preparation. To learn machine learning and artificial Intelligence skills, the AWS Machine Learning exam requires hands-on experience and appropriate resources. Some people won’t go beyond taking courses to help pass the AWS Machine Learning exam. Let’s start by learning a little bit about the exam.
AWS Machine Learning Specialty MLS-C01
AWS Certified Machine Learning – Specialty exam (MLS-C01), is for those who work in data science or development. This exam validates the examinee’s ability use AWS Cloud to build and tune ML models.
It evaluates the candidate’s ability to implement, maintain, and monitor machine learning solutions for specific business problems. It will assess the candidate’s ability:
Choose the best ML approach to solve a business problem and justify it.
Find the best AWS services to implement ML solutions.
Implement scalable, cost-effective and reliable machine learning solutions that are both reliable and secure.
Exam overview
The AWS Machine Learning Specialist Certification exam consists of 65 scenario-based questions. These questions assess a candidate’s ability to solve different business problems. This exam is a speciality exam and has a time limit that lasts 170 minutes. The AWS Machine Learning Certification cost is $300. Prices may vary depending on where you live. You can schedule the exam through Pearson VUE, or PSI.
Multiple-choice and multiple response questions are available. The AWS machine-learning speciality exam is graded from 1 to 1000. 750 is the passing score. The Amazon Web Services machine-learning speciality exam can be taken in English, Japanese and Korean.
Who should take the exam
Amazon suggests that candidates taking the exam have the following knowledge or experience:
You should have at least two years experience in developing, architecting, and running ML/deep-learning workloads on AWS Cloud.
The ability to express intuition underlying basic ML algorithms is a result.
You should also have experience with basic hyperparameter optimization.
Experience with deep learning frameworks and machine learning is required.
You must also be able to follow model-training best practices.
Finally, you must be able and willing to follow deployment and operational best practices.
Let’s now get to the main point.
Is AWS Machine-Learning Certification worth it?
The ecosystem is growing rapidly and traditional education pathways are finding it difficult to keep up. AWS Machine Learning certification can be displayed on your resume as a sign of technical knowledge and critical thinking. Managers and employers recognize that the certification demonstrates a deep understanding of algorithms, frameworks and best practices and the ability to apply this knowledge to real-world AWS solutions.
Only Carnegie Mellon University offers a bachelor’s degree in machine learning, despite the industry’s desperate need. All other programs require a master’s or doctoral degree, which can lead to massive student debt. The AWS Certified Machine Learning – Specialty exam is $300 and the practice exam is $40. The test takes 180 minutes, while preparation can take up to 40 hours.
We will now look at the course outline to learn more about the exam.
Syllabus outline
You will be tested in the following areas by the Amazon AWS Machine Learning Certification exam. Domain compositions can also be fixed. Take a look at the AWS Machine Learning Certification Course Outline.
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, Deploy, and Deploy Machine Learning Models with SageMaker. Train a Model with Amazon SageMaker