Process Optimisation Specialist

This role is responsible for developing ML modules and AI applications to optimize production processes, ensuring efficient and sustainable operations.

AutoML Expertise

Proficiency in developing AutoML modules that cover data preprocessing, model selection, and evaluation.

Machine Learning in Production

Target Group
Mid Level Employee

Level
Extended Know-How

Process Optimization Specialist Machine Learning in Production

In this Machine Learning in Production course, you will build intuition about designing a production ML system end-to-end: project scoping, data needs, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production like establishing a model baseline, addressing concept drift, and performing error analysis. You’ll follow a framework for developing, deploying, and continuously improving a productionized ML application. 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.

Provider
www.coursera.org

Target

Data scientists and machine learning practitioners
Software engineers interested in machine learning
Machine learning engineers focusing on deployment
Technical project managers in AI and machine learning projects
Professionals transitioning to machine learning roles

Sector

Information technology and software development
Data science and analytics
Artificial intelligence and machine learning
Business intelligence and data engineering

Area

Machine learning lifecycle management
Model deployment and maintenance
Production-level machine learning systems
Data preparation and error analysis
Software engineering best practices in AI applications

Method
Online

Certification
Yes

Duration
Flexible schedule, approx. 11 hours

Assessment
No

Cost
Free

Learning Outcomes

Identify key components of the ML project lifecycle and pipeline
Select the best deployment and monitoring patterns for different scenarios
Optimize model performance and metrics for crucial dataset slices
Solve production challenges involving various data types

Learning Content

Overview of the ML Lifecycle and Deployment
- Introduction to machine learning production systems
- Deploying production systems while handling evolving data
Modeling Challenges and Strategies
- Addressing error analysis and working with various data types
- Dealing with class imbalance and skewed datasets
Data Definition and Baseline
- Ensuring label consistency for classification problems
- Establishing and improving performance baselines