Practical Data Science on the AWS Cloud Specialization
Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.
The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Amazon Web Services
What you’ll learn
- Prepare data, detect statistical data biases, perform feature engineering at scale to train models, & train, evaluate, & tune models with AutoML
- Store & manage ML features using a feature store, & debug, profile, tune, & evaluate models while tracking data lineage and model artifacts
- Build, deploy, monitor, & operationalize end-to-end machine learning pipelines
- Build data labeling and human-in-the-loop pipelines to improve model performance with human intelligence
How to Enroll: Practical Data Science on the AWS Cloud Specialization
- Choose your desired certificate program on the Amazon website.
- Create an Amazon if you don’t have one.
- Select specific courses within your chosen program.
- Enroll in courses, and pay if necessary.
- Access course materials and complete requirements.
- Prepare for and take certification exams if required.
- Earn your certificate upon successful completion.
- Be aware of maintenance or renewal requirements, if applicable.