Who is AWS certified Machine Learning Specialist? | Why you should learn AWS ML? | AWS ML certification Benefits | AWS Machine Learning – Specialty Exam Goals | Prerequisites for AWS ML | Exam Details | Exam Topics | AWS ML Hands-on Guides | Who This Certification is for? | Exam Retake Policy | FAQ’s
For those who work in improvement or data science, the AWS Certified Machine Learning specialty certification is available. It verifies a candidate’s capacity to develop, carry out, deploy, and hold machine learning (ML) solutions for specified business issues.
Data science and machine learning are fields with numerous job opportunities. Competition is increasing as the variety of available jobs expands. As a result, whether you need to find work, change careers, or advance in your career, you want something that will set you apart from the throng. This is where the certification comes in handy. It verifies the ability to create and deploy machine learning (ML) solutions on AWS. It also measures understanding of essential ideas.
“According to the report by Paysa, the Amazon Machine Learning Scientist’s average salary is $214,484.”
Who Is AWS Certified Machine Learning Specialist?
AWS-certified Machine Learning Specialists assist businesses in developing, putting into practice, deploying, and managing ML solutions for operational issues. Establish the appropriate ML strategy, choose the best AWS administrations, and safeguard ML solutions.
Note : Read Our Blog Post on Modeling With AWS Machine Learning.
Why You Should Learn AWS ML
Reasons to Learn AWS ML
In addition to verifying your technical abilities, having an AWS certification can help you promote your accomplishments and grow your AWS experience.
Following are a few benefits of holding an AWS certification :
- Credibility: Having a certification will improve both your credibility and your knowledge of the services. Credentials will provide you access to more options.
- Obtaining the title of “AWS Certified”: This would open up several career options for professionals and recent graduates in AWS-related initiatives.
- Enhances abilities: Utilizing an AWS you can develop your skill set and lower the risks involved in putting an AWS project into action.
- Help you get a job: If you’re a professional or a fresher the tag “AWS Certified” will fetch you a lot of job opportunities in AWS-related projects.
- Higher Pay: AWS is the highest earning certification in the US, yet certification does not ensure a higher salary.
- Builds your business for you: Employers want AWS-certified individuals because they help with business development. It is one of the prerequisites for higher-tier AWS Partner Network memberships.
- Large advantages: You can obtain market support, AWS use credits, training subsidies, and other benefits by joining the AWS Partner Network.
Check Also: Free AWS Training and Certifications
AWS Machine Learning Certification Benefits
Benefits of an AWS Machine Learning Certification
The following are some advantages of having an AWS-approved machine learning certification:
- Confirms your ability to build, train, and deploy the machine learning model utilizing the AWS Cloud.
- Gives you recognition on a global scale for your knowledge, skills, and expertise.
- One of the highest-paying data-Tech certificates worldwide.
- Adds a qualification to your resume, helping you to stand out from the competition.
- Enhances your ability to obtain more opportunities to advance as an AWS engineer.
AWS Machine Learning – Specialty Exam Goals
AWS Certified Machine Learning Specialty exam goals are:
1. Demonstrate your ability to create, build, implement, and maintain ML solutions for business problems.
2. Select and validate the precise ML technique for the specified business issue.
3. Select the relevant AWS administrations to put ML solutions into action.
4. Design and implement adaptable, cost-effective, reliable, and secure ML solutions.
Note: Our Blog Post on Deep Learning On AWS, For More Information.
Prerequisites for AWS ML
1. 1-2 years of experience designing, implementing, or executing machine learning/deep learning workloads on the AWS Cloud
2. The capability to convey the intuition underlying fundamental ML techniques
3. Proven proficiency with fundamental hyperparameter optimization
4. Proficient in machine learning and deep learning frameworks
5. The capability to adhere to excellent practices in model-training
6. Being able to adhere to deployment and operating best practices
Exam Details
- Certification Name: AWS Certified Machine Learning Specialty
- Exam Duration: 180 minutes to complete the exam
- Exam Cost: 300 USD (Practice exam: 40 USD)
- Exam Format: Multiple choice, multiple answers
- Exam Language: Available in English, Korean, Japanese, and Chinese
- Number of Questions: 65 (approx)
- Passing Score: a minimum passing score of 750 out of 1000
- Validity: 3 years
You can schedule your AWS Certified Machine Learning Specialty exam by going to the official site.
Exam Topics
The AWS Certified Machine Learning Specialty exam focuses on the following 4 domains:
Also Check Our Blog On Amazon Comprehend.
1) Data Engineering (20% of Examination)
This segment should be quite sincere for everybody who has revealed in working with the AWS big data stack or has previously taken the big data AWS Certification. The main data engineering services are:
- AWS streaming tools
- Kinesis Firehose
- Kinesis Analytics
- Kinesis Data Streams
- Storage/Database
- S3
- RDS
- DynamoDB
- AWS Analytics stack
- Athena
- Glue
It isn’t critical to understand most of these tools in wonderful detail, however, you may need to understand what all of them do and when they should be used.
Read Our Blog Post On Amazon SageMaker.
2) Exploratory Data Analysis (24% of Examination)
This segment of the exam isn’t specific to AWS, it’s about cleaning data and feature engineering such as:
- Normalization
- One-Hot encoding
- Handling missing values
3) Modeling (36% of Examination)
This segment of the exam focuses on SageMaker which is AWS’s signature fully managed ML service. However, most of the questions require an understanding of the ML concept (outside of AWS) consisting of algorithm selection and tuning deep learning models. The main part of this segment is:
- SageMaker algorithms
- Model tuning concepts
- regularisation
- learning rates
- dropout
- gradient descent
- Model metrics
- sensitivity
- accuracy
- specificity
- F1
- recall
- Optimization techniques
Do Read : Our Blog Post On Amazon Rekognition.
4) Machine Learning Implementation and Operations (20% of Examination)
This segment of the exam focuses on machine learning solutions for:
- Performance
- Resiliency
- Scalability
- Fault tolerance
- Basic AWS security
- Deploy and operationalize
Do Check : Our Blog Post On Amazon Lex.
AWS ML Hands-on Guides
For AWS ML we have a list of 32 Step-by-Step Activity Guides (Hands-On Labs) for you to practice and have a clear knowledge of the concepts both theoretically and practically. The list of activity guides is as follows:
- Lab 1: Introduction to Python Basics
- Lab 2: Introducing the Pandas Library
- Lab 3: Mean, Mode & Median
- Lab 4: Percentiles and Moments
- Lab 5: Standard Deviation
- Lab 6: Using matplotlib & Seaborn
- Lab 7: Linear Regression
- Lab 8: Polynomial Regression & Multiple Regression
- Lab 9: K-Fold cross-validation to avoid overfitting
- Lab 10: Handling outliers
- Lab 11: AWS Free Tier Account Setup and Overview
- Lab 12: Creating AWS S3 Bucket and Uploading Data into S3
- Lab 13:Introduction to Amazon S3
- Lab 14: AWS Glue Data Catalog & Crawlers
- Lab 15: Running ETL Job Using Glue
- Lab 16: Kinesis Data Streams & Kinesis Data Firehose
- Lab: 17: Running Data Analytics using Kinesis
- Lab 18: Amazon Athena With Glue Integration
- Lab 19: Overview of Amazon Quicksight
- Lab 20: Hadoop Overview & Elastic MapReduce (EMR)
- Lab 21: Apache Spark on EMR
- Lab 22: EMR Notebooks, Security, and Instance Types
- Lab 23: Amazon Lex
- Lab 24: Amazon Polly
- Lab 25: Amazon Rekognition
- Lab 26: Amazon Translate
- Lab 27: Image Classification Algorithm
- Lab 28: Learner algorithm
- Lab 29: Random Cut Forest (RCF) Algorithm
- Lab 30: Monitoring with CloudWatch
- Lab 31: Logging with CloudWatch
- Lab 32: Logging in SageMaker API Calls with AWS CloudTrail
Who This Certification Is For?
AWS Certified Machine Learning Specialty exam is for:
- Candidates with a data science interest.
- Business Decisive Individuals
- Developers
- Platform Data Engineers
- One who wants to develop a career in machine learning
Note: Do Check Our Blog Post On Data Engineering With AWS Machine Learning.
Exam Retake Policy
- The candidate desires to attend 14 days earlier than they’re eligible to retake the exam.
- The candidate can take any number of examinations tries till he passes.
- For each exam attempt, the candidate needs to pay the whole registration rate. but the beta exam takers can take the exam once most effective.
Frequently Asked Questions
How can I enroll in the AWS Machine Learning certification program?
You can arrange your exam by visiting the AWS Cloud website. To schedule the exam, you'll need an Amazon account, so create one and complete the necessary paperwork.
How may an AWS Certification exam be rescheduled?
Up to 24 hours prior to your planned appointment, you may postpone or cancel your exam without incurring additional costs.
What training is advised before taking the AWS ML Certification exam?
The AWS ML exam has no prerequisites, to my knowledge. You can take the Amazon AWS certification exam right away. Following are some suggestions for test preparation: 1. Practical experience in designing, constructing, or managing ML/deep workloads for learning on the AWS Cloud. 2. Experience with deep learning and machine learning frameworks. 3. The aptitude and basic sense to pinpoint the goal of the ML algorithms. 4. The ability to attempt simple hyperparameter optimization. 5. The ability to combine deployment and operational best practices.
I hope this blog helps you in clearing your doubts regarding the AWS-certified Machine Learning Specialist Exam, what are the topics covered within the exam, certification benefits, and more.
Related References
- AWS Certificate Manager: Overview, Features and How it Works?
- AWS Certified Solutions Architect Associate SAA-C03 Exam
- AWS Database Services – Amazon RDS, Aurora, DynamoDB, ElastiCache
- Multi-Account Management Using AWS Organizations
- AWS Certified Solutions Architect: Roles & Responsibilities
- Amazon Kinesis Overview, Features And Benefits
- AWS Route 53 Introduction
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