Ementorhub

What you'll learn

Identify key components of the ML project lifecycle, pipeline & select the best deployment & monitoring patterns for different production scenarios.

Optimize model performance and metrics by prioritizing disproportionately important examples that represent key slices of a dataset.

Solve production challenges regarding structured, unstructured, small, and big data, how label consistency is essential, and how you can improve it.

Skills you'll gain

Applied Machine Learning

Artificial Intelligence and Machine Learning (AI/ML)

Machine Learning

Debugging

Data Validation

Feature Engineering

Data Pipelines

Continuous Monitoring

Application Deployment

MLOps (Machine Learning Operations)

Data Quality

Software Development Life Cycle

Continuous Deployment

There are 3 modules in this course

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need experience preparing your projects for deployment as well. Machine learning engineering for production combines the foundational concepts of machine learning with the skills and best practices of modern software development necessary to successfully deploy and maintain ML systems in real-world environments. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Modeling Challenges and Strategies Week 3: Data Definition and Baseline

Week 2: Modeling Challenges and Strategies

Week 3: Data Definition and Baseline