Description
The Machine Learning Pipeline on AWS course offers an immersive project-based learning environment that explores the iterative process of building and deploying Machine Learning (ML) models to solve real business problems. Throughout the course, learners will gain a comprehensive understanding of each phase of the ML process pipeline through instructor presentations and demonstrations.
In this course, learners will have the opportunity to apply their knowledge to complete a project focused on solving one of three business problems: fraud detection, recommendation engines, or flight delays. Through hands-on exercises and practical implementation, participants will develop skills in building, training, evaluating, tuning, and deploying ML models using Amazon SageMaker.
By the end of the course, students will successfully build, train, evaluate, tune, and deploy an ML model using Amazon SageMaker to address their selected business problem. This comprehensive project-based approach allows learners to gain practical experience and demonstrate their ability to apply ML techniques to real-world scenarios.
Interactive Training with Zoom
Join our Zoom-based courses to benefit from real-time discussions, immediate solutions to problems, and guidance from our seasoned instructors.
Audience
Developers
Solutions Architects
Data Engineers
Professionals wanting to learn about ML pipelines using Amazon SageMaker
Did you know? According to Global Knowledge's 2022 IT Skills and Salary Report, AWS Certified Solutions Architect - Associate was one of the top-paying IT certifications, with an average salary of over $130,000.
Prerequisites
Familiarity with the Python programming language
A fundamental knowledge of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
Basic experience working with Jupyter notebooks
"The ones who are crazy enough to think they can change the world, are the ones that do." - Steve Jobs
Deliverables
Gain a comprehensive understanding of the ML process pipeline and its different stages, from data preprocessing to model deployment
Explore various data preprocessing techniques to clean, transform, and prepare data for training ML models
Develop skills in feature engineering and selection to optimise model performance and interpretability
Learn different ML algorithms and techniques applicable to the selected business problem, such as classification, regression, or clustering
Understand how to evaluate and validate ML models using appropriate metrics and techniques
Explore methods for hyperparameter tuning to optimize model performance and generalisation
Gain insights into deploying ML models using Amazon SageMaker for real-time or batch predictions
Learn best practices for creating scalable, cost-effective, and secure machine learning pipelines in the AWS environment
Develop the ability to apply ML techniques and AWS services to solve a genuine business problem effectively
Acquire knowledge of model monitoring and retraining strategies to ensure model performance over time
Gain proficiency in effectively communicating and presenting ML solutions and insights to stakeholders
Earn a Certificate
Earn a certificate to showcase your skills on LinkedIn, enhancing your professional credibility.
"Cloud training initiatives can have a return on investment of up to 2–6x." - Source: ACG: The ROI of Cloud Training
