About this course

Course language: English

Course pacing: Self-Paced

Non Credit

This course is designed to provide a comprehensive understanding of Machine Learning Operations (MLOps), covering key concepts in machine learning models in production environments. Participants will gain insights into the lifecycle of machine learning projects, from data preparation and model development to deployment and monitoring.

Throughout the course, participants will engage in understanding of key concepts and theories in MLOps. By the end of the course, participants will be gain knowledge and insight to effectively manage machine learning projects in production environments and drive business value through AI-enabled applications.

• Chip, Huyen. "Designing machine learning systems: An iterative process for production-ready applications." (2022).
• Valliappa, Lakshmanan, Robinson Sara, and Munn Michael. "Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps." (2020)

Requirements

• Basic understanding of Machine Learning concepts and algorithms
• Understanding of software development practices and methodologies
• Basic knowledge of cloud computing platforms (e.g., AWS, Azure, GCP)
• Understanding of CI/CD pipelines and DevOps practices

Course Plan (200 minutes)

• 01- Intro to Machine Learning [Video, Material, Quiz] (20 minutes)
• 02- Machine Learning Workflow [Video, Material, Quiz] (25 minutes)
• 03- Common Challenges in Machine Learning [Video, Material, Quiz] (25 minutes)
• 04- Data Drift, Scale and Multiple Objectives [Video, Material, Quiz] (20 minutes)
• 05- Model Development [Video, Material, Quiz] (25 minutes)
• 06- Model Performance Evaluation [Video, Material, Quiz] (20 minutes)
• 07- Model Deployment [Video, Material, Quiz] (20 minutes)
• 08- Pipeline [Video, Material, Quiz] (25 minutes)

Instructor : Ari Wibisono, S.Kom., M.Kom [Link-->]

Ari Wibisono is a scholar in the Faculty of Computer Science at Universitas Indonesia. With a contribution to the fields of parallel, grid, and cloud systems, his research interests also encompass intelligent systems and big data. Ari Wibisono has authored numerous influential papers, including works on traffic big data prediction and vehicle speed measurement using advanced algorithms. His efforts have earned him over 700 citations, reflecting his impact on the academic community.

In addition to his research, Ari Wibisono is actively involved in projects that apply machine learning and data analytics to real-world problems. Ari Wibisono's expertise in both theoretical and applied aspects of computer science. Ari Wibisono has been involved in various teaching activities, contributing to the development of students in Data Analytics and Infrastructure Information Management fields. His commitment to education is reflected in his extensive teaching history, helping to shape future professionals in the ever-evolving tech landscape.

email: [email protected]