This guide covers the essential details of the AWS Certified Machine Learning - Specialty certification and machine learning terminologies, and provides recommended resources for best practices.
Artificial intelligence (AI) is an extensive branch of computer science primarily focused on developing systems that can think intelligently, like humans. There are various techniques used to develop an AI system that are categorized into different subsets such as machine learning (ML) and deep learning (DL).
Machine learning is the process of empowering computer systems to automatically learn and progressively improve their performance over time. Often the machine is trained by using a sample of structured data, like vegetables with details such as weight, color, shape, and type (label) properties. Then the trained model applies the learning on unlabeled data to identify the type of vegetables.
Deep learning is the process of making machines that can think and process data like the human brain, i.e. by identifying patterns and classification techniques such as identifying the type of animal in an image.
Data is a critical aspect of machine learning. Data can be categorized into two types:
Labeled data contains details about the object and the desired result details as well. The result (label or tag) column’s details are often obtained from a human, and that’s why labeled datasets are more expensive to obtain.
Unlabeled data does not contain any result column, so it cannot be used to train machines. There are various machine learning techniques that can be applied to identify a similar type of data and create groups, for example, groups of profitable or loss-making stocks from unlabeled data.
There are many types of ML techniques that can be applied to different types of data and problems.
The AWS Certified Machine Learning - Specialty certification is intended for data scientists or professional machine learning developers. This certification focuses on deep aspects of data manipulation and optimal machine learning solution development using AWS services and tools.
There are no prerequisites to take the exam, though this certification requires basic STEM knowledge as well as problem-solving and analytical skills. The skills recommended to be successful in the machine learning field are:
AWS offers a wide variety of tools and services for machine learning solution development, including:
Apart from AWS machine learning services, the AWS Certified Machine Learning - Specialty certification also requires the knowledge of other domains such as programming languages, databases, etc.
Once you study the training and practice material thoroughly, the final step is to schedule the test. The crucial attributes for the test are:
Format: The exam is comprised of multiple-choice questions, and answers can have multiple correct choices. Marks are only given if only the correct choices are selected.
Scores: The criteria for passing scores is set by using statistical analysis (scaled scoring models) and is subject to change. Points are not given for incorrect answers.
Method: The exam can be taken online (proctored exam) or given at a physical test center provided by PSI or Pearson VUE. The benefit of opting for a physical test center is the opportunity to meet other developers and make new connections.
For an online proctored exam, applicants must be able to speak English to communicate with a proctor, who will monitor the testing environment. Online proctoring exams are not available for candidates in mainland China, Japan, Slovenia, or South Korea. More details are available here. Find the additional information about system requirements and policies here.
Time: The duration of the exam is 170 minutes, though it could vary in the future depending on the content.
Charges: There is a one-time fee of US$300 for an AWS Certified Machine Learning - Specialty exam, and the practice exam fee is US$40.
Beta Program: Amazon has a beta program for certification with changes to the exam’s outline or new certifications. Early access is available to a limited number of candidates (on a first come, first served basis) who can take the beta exam as well as the stable exam once it’s out of beta). This allows applicants to take the exam twice without any additional fee. The beta program also provides the benefit of 50% off of the standard exam pricing.
Additional Details: The exam can be rescheduled up to 24 hours before the scheduled exam time; otherwise there will be no refund and the next exam can be scheduled only after 24 hours. In case of unsuccessful attempts, the next exam can be scheduled after 14 days with the same fee, though you can use vouchers to retake the exam.
The AWS Certified Machine Learning - Specialty certification is valid for three years. The certificates will be available within five working days after a positive exam result.
Details about content and pricing vary, so make sure to verify it here.
Machine learning is one of the most demanding skills in the job market, and the AWS Certified Machine Learning - Specialty certification qualifies you for various positions such as Data Scientist and Machine Learning Engineer.
Pluralsight offers great resources on AWS Certified Machine Learning Specialty. The curated learning paths are:
List of helpful resources such as AWS Whitepapers and study material for best practices and insights.
Hopefully, this guide explained the necessary details to get started with the AWS Certified Machine Learning - Specialty certification. Good luck with your certification.