The AWS Machine Learning specialty exam is definitely not an easy exam and you need to be very prepared to pass the exam. Even if you have taken and completed several courses aimed at passing the exam it will not completely prepare you for the kind of questions they will ask you at the exam. I have recently taken the last exam and passed it with a score of 90 % and have already taken and passed several other certificates. I have tried to prepare two practice exams based on my experience from the exam. I made sure that the distribution of questions is similar to what you can expect from the real exam and I have prepared the questions to be scenario-based. If you complete the two practice exams you will be much better prepared for the kind of questions they will ask you at the exam. The AWS Machine Learning is one of the hardest AWS certificates to obtain but it is not impossible if you are very well prepared for the kind of questions you will be asked in the exam. Its a very good certificate to have as it covers two of the hottest topics in IT - machine learning and cloud computing. This certificate proves that you are are able to complete a full machine learning project on the AWS platform - from ingesting the data and doing feature engineering to picking and training the models to deploying the models so it can easily used from outside the platform.

It validates an examinee’s ability to design, implement, deploy, and maintain ML solutions for given business problems. It will validate the candidate’s ability to:

• Select and justify the appropriate ML approach for a given business problem.

• Identify appropriate AWS services to implement ML solutions.

• Design and implement scalable, cost-optimized, reliable, and secure ML solutions.

Recommended AWS Knowledge

The successful candidate likely has one to two years of hands-on experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud, along with:

• The ability to express the intuition behind basic ML algorithms

• Experience performing basic hyperparameter optimization

• Experience with ML and deep learning frameworks

• The ability to follow model-training best practices

• The ability to follow deployment and operational best practices

Course syllabus description:

Domain 1: Data Engineering

• 1.1 Create data repositories for machine learning.

• 1.2 Identify and implement a data-ingestion solution.

• 1.3 Identify and implement a data-transformation solution.

Domain 2: Exploratory Data Analysis

• 2.1 Sanitize and prepare data for modeling.

• 2.2 Perform feature engineering.

• 2.3 Analyze and visualize data for machine learning.

Domain 3: Modeling

• 3.1 Frame business problems as machine learning problems.

• 3.2 Select the appropriate model(s) for a given machine learning problem.

• 3.3 Train machine learning models.

• 3.4 Perform hyperparameter optimization.

• 3.5 Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

• 4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

• 4.2 Recommend and implement the appropriate machine learning services and features for a given problem.

• 4.3 Apply basic AWS security practices to machine learning solutions.

• 4.4 Deploy and operationalize machine learning solutions.

Who this course is for:

• AWS Developer

• ML Developer

• AWS Engineer

BEST OF LUCK!!