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Advance Your Career in Machine Learning With AWS

Nowadays, there is a big demand for the numerous certifications that AWS offers. One such certification that verifies a candidate's skill, knowledge, and comprehension of machine learning is AWS Certified Machine Learning Specialist. If you have experience with machine learning, also known as deep learning, now is the time to enroll for this certification and get the AWS Certified Machine Learning Specialist credential.


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Andrew

2 years ago | 6 min read

Amazon Web Services has revolutionized the market by providing IT courses that genuinely improve a person's skill set and assist them in becoming a better version of themselves. It will be beneficial for you to pursue one or more of the certifications that AWS offers if you wish to specialize in a certain area. Nowadays, there is a big demand for the numerous certifications that AWS offers, and more and more people choose to seek both associate and professional level credentials. One such certification that verifies a candidate's skill, knowledge, and comprehension of machine learning is the AWS-certified machine learning specialist. If you have experience with machine learning, also known as deep learning, now is the time to enroll for this certification and get the AWS Certified Machine Learning Specialist credential.

AWS Machine Learning

Why Should You Pursue Your Career in AWS Machine Learning?

The field of machine learning is extremely broad. Starting in this field with merely academic foundations is difficult, but it is encouraged if you are serious about deep specialized knowledge in the subject. Though you don't have to approach it linearly and go through years of university-level education before you start experimenting and developing practical AI/ML solutions. AWS's claim that they want to give everyone access to AI and ML is more than simply a sales pitch. The range of options is extensive and serves developers with any degree of ML expertise, from cutting-edge ML academics to corporate users who are only interested in the outcomes.

Who Should Go For AWS Certified Machine Learning – Speciality Certification?

Those who work in improvement or data science positions should pursue the AWS Certified Machine Learning specialist certification. It confirms a candidate's proficiency in developing, deploying, and maintaining machine learning (ML) solutions for specific business issues. With an emphasis on solutions that use AWS services, the AWS Machine Learning Specialty Certification gives you the chance to check your general understanding of this domain from both a conceptual and practical standpoint. However, the exam is more than just a test of your AWS expertise. Actually, the exam spends much of its time covering fundamental Machine Learning, AI, and Deep Learning ideas. Building, implementing, deploying, and managing ML solutions for business problems is made easier by an AWS Certified Machine Learning Specialist. Establish the appropriate ML strategy, choose the best AWS administrations, and protect ML solutions.

What Does It Take To Become An AWS ML Specialist?

In addition to verifying your technical abilities, having an AWS Machine Learning credential can help you promote your accomplishments and grow your AWS experience. AWS Certified Machine Learning - Specialty is designed for those with more than a year of experience developing, architecting, or executing machine learning/deep learning workloads in the AWS Cloud. These people typically work in development or data science roles. You should have at least two years of hands-on experience designing, architecting, and executing ML or deep learning workloads in the AWS Cloud, as well as expertise with basic hyperparameter optimization. The capacity to express the idea behind fundamental ML methods and to comply with model training, deployment, and operational best practices. It's also advised to have prior knowledge of machine learning and deep learning frameworks. You must take and pass the AWS MLS-C01 Certified Machine Learning - Specialty exam to acquire this credential.

Perks Of Earning AWS ML – Speciality Credential

  • Verifies your ability to build, train, and implement the ML model on the AWS Cloud.
  • Gives you recognition on a global scale for your knowledge, skills, and expertise.
  • Listed as one of the highest-paying data-Tech qualifications worldwide.
  • Adds a credential to your resume, helping you stand out from the competition.
  • Increases your chances of obtaining more possibilities to advance your AWS Machine Learning Specialist.

Core Objectives Of AWS MLS-C01 Machine Learning – Specialty Exam

Verify your ability to develop, implement, deploy, and maintain ML solutions for business problems.

Identify and demonstrate the ideal ML technique for the given business challenge.

Determine the most suitable AWS administrations to implement ML solutions.

Develop and implement ML solutions that are adaptable, cost-effective, dependable, and secure.

Domains Covered in AWS MLS-C01 Exam:

The following 4 domains are the major focus of the AWS Certified Machine Learning Specialty exam:

1) 20% Of Exam Covers Data Engineering

Anyone who has experience with the AWS big data stack or who has already earned their big data AWS Certification should find this section to be extremely serious. The following are the primary data engineering services:

  • AWS streaming tools
  • Kinesis Firehose
  • Kinesis Analytics
  • Kinesis Data Streams
  • Storage/Database
  • S3
  • RDS
  • DynamoDB
  • AWS Analytics stack
  • Athena
  • Glue

The majority of these tools don't require a deep understanding, but it's still important to know what they all accomplish and when to utilize them.

2) 24% Of Exam Covers Exploratory Data Analysis

This exam section deals with data cleaning and feature engineering, not specifically AWS, and covers things like:

  • Normalization
  • One-Hot encoding
  • Handling missing values

3) 36% OF Exam Covers Modeling

The emphasis of this exam section is SageMaker, the company's renowned fully managed machine learning service. However, the majority of the questions call for knowledge of the ML concept, which includes deep learning model tuning and algorithm selection outside of AWS. This segment's main focus is:

  • SageMaker algorithms
  • Model tuning concepts
  • regularisation
  • learning rates
  • dropout
  • gradient descent
  • Model metrics
  • sensitivity
  • accuracy
  • specificity
  • F1
  • recall
  • Optimization techniques

4) 20% Of Exam Covers Machine Learning Implementation and Operations

This section of the exam is dedicated to machine learning solutions for:

  • Performance
  • Resiliency
  • Scalability
  • Fault tolerance
  • Basic AWS security
  • Deploy and operationalize

Study Resources To Prepare For The AWS MLS-C01 Exam

I've included both general study resources for the topic of machine learning and study materials specifically for the AWS certification exam. You can skip over the sections below that you are already familiar with. One would do well to begin by reviewing the official exam outline and sample questions published by AWS in order to become comfortable with the exam structure.

AWS MLS-C01 Exam Guide [PDF]: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide.pdf

AWS MLS-C01 Exam Sample Questions [PDF]: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Sample-Questions.pdf

Many people agree that the book by Aurélien Géron which includes practical learning tasks utilizing the most recent tools, is one of the greatest introductions to the subject:

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/

The following video is advised for a deeper comprehension of linear algebra. Among the most understandable explanations, I've ever heard.

https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

I suggest the YouTube channel StatQuest with Josh Starmer for an explanation of various Machine Learning algorithms and statistics ideas. Even if his introductions have a peculiar style, once you get used to them, the explanations and graphics are really helpful.

https://www.youtube.com/c/joshstarmer/playlists

You would certainly need to perform hands-on activities if you want to fully comprehend AWS SageMaker. Run a handful of the sample notebooks offered by AWS to show how SageMaker functions. Reading through the given notebooks will give you a decent understanding of the process, API calls, hyperparameters, etc. even if you don't have the time to run every example algorithm.

https://github.com/aws/amazon-sagemaker-examples

Reading significant portions of the SageMaker developer documentation would also be helpful.

https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.htmlhttps://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf [PDF]

Some additional prep resources

Emily Webber: SageMaker Deep Dive

https://www.youtube.com/playlist?list=PLhr1KZpdzukcOr_6j_zmSrvYnLUtgqsZz

Julien Simon: SageMaker Hands-on

https://www.youtube.com/c/juliensimonfr/playlists

AWS Machine Learning MLS-C01 Practice Tests

It is strongly advised that you take at least one practice test in addition to the official sample questions to determine your level of exam readiness. Since there are just 20 questions on the official AWS practice test, you won't get the entire 3-hour exam experience from it. Additionally, you won't get a thorough description of the questions you answered wrong or correctly. As you take this test, be ready to make a note of the questions and subjects that you will need to review more thoroughly. Many people in the AWS community who have just passed the test, including myself, suggest using these AWS MLS-C01 questions. These MLS-C01 AWS ML Specialty practice test questions closely resemble the format of the real exam and cover every subject area necessary to pass the exam. The exam questions appear to be evenly divided into two categories: (1) those that ask you to correctly identify one specific ML concept or AWS service feature and choose an answer based on the use cases provided, and (2) those that ask you to combine several ML concepts and/or AWS services to come up with an answer to the scenario. Even in the first category, the questions go well beyond what you would generally see in a question bank for an Associate level test.

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