Certainly! Here are the minimum required prerequisites for successfully undertaking training in The Machine Learning Pipeline on AWS course:
- Basic understanding of machine learning concepts, including supervised and unsupervised learning, and familiarity with common use cases.
- Fundamental knowledge of Python programming, as it is commonly used in machine learning projects and hands-on exercises.
- Familiarity with AWS services, particularly Amazon SageMaker, as the course includes lessons on utilizing this service for machine learning tasks.
- Experience with Jupyter notebooks or similar interactive computing environments, as they will be used for demonstrations and hands-on activities.
- Basic understanding of data handling and preprocessing techniques, which are essential for preparing data for machine learning models.
- An analytical mindset and problem-solving skills to effectively convert business problems into machine learning problems and work through the stages of the ML pipeline.
These prerequisites are designed to ensure that participants are prepared to engage with the course material effectively and are able to take full advantage of the training program.
Target Audience for The Machine Learning Pipeline on AWS
The Machine Learning Pipeline on AWS course equips participants with practical AWS ML skills for real-world applications.
- Data Scientists
- Machine Learning Engineers
- Data Engineers
- Software Developers interested in machine learning
- IT Professionals aiming to implement machine learning on AWS
- AWS Cloud Practitioners
- Cloud Solutions Architects
- Technical Project Managers overseeing ML projects
- Business Analysts looking to understand ML workflows
- AI/ML Consultants
- Research Scientists and Academics interested in deploying ML models
- Data Analysts aspiring to transition to machine learning roles
- Product Managers wanting to leverage machine learning in product development
- Students and Educators in computer science and related fields
Learning Objectives - What you will Learn in this The Machine Learning Pipeline on AWS?
Introduction:
The Machine Learning Pipeline on AWS course provides a comprehensive journey through the essentials of machine learning, leveraging Amazon SageMaker, and culminates in the deployment of ML models.
Learning Objectives and Outcomes:
- Understand the fundamentals of machine learning, including its use cases, types, and key concepts.
- Gain proficiency in the different stages of the ML pipeline and their significance in project implementation.
- Become familiar with Amazon SageMaker and its integration with Jupyter notebooks for developing ML models.
- Learn to formulate real-world problems into machine learning challenges and identify when ML is the appropriate solution.
- Develop skills in data preprocessing, visualization, and utilization of Amazon SageMaker Ground Truth for data labeling.
- Acquire the ability to select appropriate algorithms for model training and understand the role of loss functions and gradient descent.
- Master the techniques for evaluating the performance of classification and regression models within the AWS environment.
- Explore advanced concepts in feature engineering, including extraction, selection, and transformation, and practice hyperparameter tuning with Amazon SageMaker.
- Learn to deploy, infer, and monitor ML models on Amazon SageMaker, including deploying ML at the edge.
- Complete the course with the capability to build, train, tune, and deploy ML models on AWS, culminating in a final project presentation.
Intermediate
