DA 212-O MLOps at Scale 3:1 (Summer 2022)
Course Instructor: Prof. Sashikumaar Ganesan, CDS
Course description: This course is aimed to be an advaned graduate-level (200-series) course. This course is aimed at building the foundation of scalable (parallel) computing for Artificial Intelligence and Machine learning (AI & ML)
Syllabus
Parallel Computer Architecture: Pipelining and super-scalar processor, SIMD vectorization, Caches, Multicore architectures, GPUs, Data access optimization.
Programming Models: Shared Memory Programming basics, Shared memory programming with OpenMP, Message-passing, MPI, CUDA, MapReduce.
Machine Learning at Scale: Automatic parallelization with Numba, Dask, PySpark, Keras, Distributed training with TensorFlow, Deploying TensorFlow models in AWS.
MLOps: Introduction to MLOps, Foundations, MLOps for containers, Continuous Integration, Continuous Deployment for ML models, Monitoring and Feedback
Prerequisites: DA 203-O: Introduction to Computing for AI & Machine Learning (or) DA 224-O Practical Machine Learning (or) consent of Instructor
Textbooks / References
- Georg Hager, Gerhard Wellen. Introduction to High-Performance Computing for Scientists and Engineers. CRC Press, 2010.
- Noah Gift and Alfredo Deza: Practical MLOps, 1st Edition, O’Reilly Media, Inc., 2021
- Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, O'Reilly Media, Inc. 2019
Grading:
- Assignments: 20
- Midterm: 20
- Project: 30
- End Term: 30