- Amazon SageMaker's built-in machine learning algorithms (XGBoost, BlazingText, Object Detection, etc.)
- Feature engineering techniques, including imputation, outliers, binning, and normalization
- High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
- Data engineering with S3, Glue, Kinesis, and DynamoDB
- Exploratory data analysis with scikit_learn, Athena, Apache Spark, and EMR
- Deep learning and hyperparameter tuning of deep neural networks
- Automatic model tuning and operations with SageMaker
- L1 and L2 regularization
- Applying security best practices to machine learning pipeline
The AWS Certified Machine Learning - Specialty Training is intended for individuals who perform a development or data science role.
BIT’s AWS Certified Machine Learning Specialty certification is also for developers and data scientists who want to validate their machine learning skills on the AWS platform. Specifically, the exam tests a candidate’s ability to design, build, deploy and maintain machine learning solutions. This oline course covers the four key test domains: Data Engineering, Exploratory Data Analysis, Modeling, and Machine Learning Implementation and Operations. The information and resources contained in this path will be vital in preparing to take the AWS Certified Machine Learning Specialty exam.
Machine learning is an advanced certification, and it's best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners.
- Exam: AWS Certified Machine Learning – Specialty (MLS-C01)
- Associate-level knowledge of AWS. Some existing familiarity with machine learning. Knowledge of architecting, developing, or running machine learning workloads in AWS. In addition, the AWS Certified Solutions Architect–Associate certification, or the AWS Certified Big Data Specialty certification are recommended before attempting the AWS Certified Machine Learning Specialty Training.
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