AWS Certified Machine Learning – Specialty
Preparatory Course
The AWS Certified Machine Learning – Specialty certification is an advanced exam from Amazon Web Services that validates the technical expertise of machine learning (ML) professionals applied to the cloud. Designed for data scientists, engineers, and developers, the exam demonstrates the ability to design, build, train, and deploy scalable and effective ML solutions using AWS services, covering the entire machine learning lifecycle—from problem definition to model monitoring and optimization.
The certification preparation course is ideal for those who want to acquire practical, in-depth machine learning skills focused on the AWS platform. Covering everything from data collection and preparation to algorithm selection, model training, and deployment, the course provides comprehensive training to confidently take the AWS Certified Machine Learning – Specialty exam. It prepares participants to correctly interpret the exam requirements, master the main services involved, and apply best practices in real-world environments.
By completing the course and passing the exam, professionals reinforce their technical authority in cloud ML and stand out in an increasingly competitive market. Certification not only validates high-level skills but also expands career opportunities in strategic areas of artificial intelligence and cloud computing. Participating in the course is, therefore, robust preparation for the exam and a solid investment in professional growth.
Benefits of Certification
Earning the AWS Certified Machine Learning – Specialty certification adds significant value to your professional profile, demonstrating technical expertise in a strategic and growing field. It can open doors to more challenging and well-paying opportunities, as well as strengthen your credibility with employers, clients, and technical teams. Furthermore, certification encourages a deeper understanding of cloud ML best practices, fostering more effective and efficient solutions.
Who is it for?
This certification is ideal for data scientists, machine learning engineers, developers, and analysts working with artificial intelligence who want to consolidate their knowledge on AWS. It's also recommended for professionals who want to lead ML projects safely and accurately, taking full advantage of the services offered by AWS. Whether for advancing your technical career or assuming strategic positions, this certification is perfectly aligned with those seeking to excel in the field of data science and machine learning in cloud environments.
Prerequisites
While the AWS Certified Machine Learning – Specialty exam doesn't require any mandatory prerequisites, AWS recommends that candidates have between one and two years of hands-on experience in development or data science, with familiarity with machine learning services in the AWS Cloud. Understanding the fundamentals of machine learning algorithms, data engineering practices, model evaluation, and cloud architecture is essential. Basic knowledge of machine learning and data science is also recommended, as well as prior experience with AWS services focused on data collection, preparation, and analysis. For an even stronger foundation, candidates are recommended to hold certifications such as the AWS Certified Solutions Architect – Associate, AWS Certified Developer – Associate, or, optionally, the AWS Certified Cloud Practitioner.
Exam Composition: Structure and Duration
The exam consists of multiple-choice and multiple-answer questions and lasts a total of 180 minutes.
Program content
1. Machine Learning Fundamentals on AWS
- Introduction to the main Machine Learning services on AWS
- Overview of Amazon Sage Maker for model development and training
- Complete ML Workflow: Data Collection, Preparation, Training, and Evaluation
- Fundamental concepts of Machine Learning: Supervised, unsupervised and reinforcement learning
- This section covers the main exam domains of the AWS Certified Machine Learning Specialty exam.
2. Data Collection and Preparation
- Data Collection with Amazon S3, Amazon RDS, and DynamoDB
- Data Preparation and Cleaning with AWS Glue and AWS Data Wrangler
- Performing feature engineering to create and select the most relevant variables before applying Machine Learning
- Using Amazon Athena to Query Data and Prepare for Machine Learning
- Data storage and management in data lakes on AWS
3. Creating and Training Machine Learning Models
- Model Training with Amazon SageMaker
- Using pre-trained and custom algorithms in SageMaker
- Hyperparameter optimization and ML model tuning (hyperparameter tuning)
- Distributing training loads across multiple servers with SageMaker
4. Model Evaluation and Adjustment
- Performance evaluation of ML models
- Using validation and testing metrics for model adjustments
- Using the confusion matrix to evaluate classification models
- Implementation of overfitting and underfitting techniques
- Implementing automated pipelines with SageMaker Pipelines
5. Model Deployment and Monitoring
- Deploying Models to Production with Amazon SageMaker
- Using endpoints and APIs for real-time inference (endpoint deployment)
- Model Monitoring with Amazon CloudWatch and SageMaker Model Monitor
- Model Management in Production with SageMaker MLOps
6. Advanced Algorithms and Models
- Application of supervised and unsupervised learning algorithms
- Using Deep Learning with Amazon SageMaker
- Implementation of neural networks, CNNs and RNNs
- Integrating ML frameworks like TensorFlow, PyTorch, and MXNet on AWS
- Using transfer learning with ML frameworks (e.g., TensorFlow or PyTorch) on AWS
7. AWS Certified Machine Learning – Specialty Exam Preparation
- AWS Certified Machine Learning – Specialty Exam Format
- Exam simulations and practice questions
- Exam Tips: Answer Strategies
- Review of key topics and concepts
