Artificial Intelligence , Computer Vision Engineering
Distributed Machine Learning Engineering: Scalable AI Systems, GPU Computing & High-Performance Model Training
User Avatar
Teacher

Ragul Muthukumar

Last Update:

July 11, 2024

Review:

Master distributed Machine Learning by building scalable AI training pipelines using PyTorch Distributed, TensorFlow Distributed, Ray, Horovod, Kubernetes, CUDA, NCCL, Apache Spark, and cloud-native GPU infrastructure for enterprise AI workloads.

Distributed Machine Learning Engineering: Scalable AI Systems, GPU Computing & High-Performance Model Training
User Avatar

Distributed Machine Learning Engineering: Scalable AI Systems, GPU Computing & High-Performance Model Training

5 Weeks
All levels
10 lessons
0 quizzes
0 students

Modern Artificial Intelligence systems require distributed computing architectures capable of processing petabyte-scale datasets and training billion-parameter models efficiently. This course provides a comprehensive understanding of distributed machine learning, parallel computing, GPU acceleration, cluster orchestration, and high-performance AI infrastructure used by enterprise organizations.

Students will learn data parallelism, model parallelism, pipeline parallelism, distributed optimization algorithms, mixed precision training, GPU communication protocols, workload scheduling, distributed inference, and scalable AI deployment. Hands-on implementation is performed using PyTorch Distributed Data Parallel (DDP), TensorFlow Distributed Strategy, Ray, Horovod, CUDA, NCCL, Docker, Kubernetes, Apache Spark, and cloud GPU clusters.

By the end of the course, learners will be capable of architecting, optimizing, deploying, and monitoring distributed AI systems for production environments across cloud and on-premise infrastructures.

Curriculum

  • 1 Section
  • 10 Lessons
  • 5 Weeks
Expand all sectionsCollapse all sections
A CUDA-capable NVIDIA GPU is recommended for hands-on training, but cloud GPU services can also be used.
The course covers PyTorch Distributed (DDP), TensorFlow Distributed Strategy, Horovod, Ray, Apache Spark, Kubernetes, and Docker.
Yes. You'll implement data parallelism, model parallelism, pipeline parallelism, gradient synchronization, and mixed precision training.
Yes. You'll deploy distributed AI workloads using Kubernetes and cloud AI platforms such as AWS SageMaker, Azure Machine Learning, and Google Vertex AI.
Yes. You'll build enterprise-grade distributed training pipelines, scalable inference services, GPU monitoring dashboards, and production AI infrastructure.
Yes. Topics include CUDA optimization, NCCL communication, TensorRT acceleration, ONNX Runtime, model quantization, pruning, and efficient GPU memory utilization.
Yes. A certificate of completion is awarded after successfully finishing the course, assessments, labs, and capstone project.

Features

  • 10 Enterprise Technical Modules
  • 70+ Technical Lessons
  • 18 Hands-on Labs
  • Distributed AI Infrastructure Projects

Target audiences

  • Machine Learning Engineers
  • AI Infrastructure Engineers
  • MLOps Engineers
  • Deep Learning Engineers
  • Cloud AI Architects

Leave a Reply

image
Distributed Machine Learning Engineering: Scalable AI Systems, GPU Computing & High-Performance Model Training
$24.00
- 68% OFF
  • Instructor : Ragul Muthukumar
  • Lectures : 10
  • Duration : 5 weeks
  • Enrolled : 0 students
  • Language: :English

Payment :

image

Related courses