Learn MLOps Engineering on AWS Course Online
Course Prerequisites
The prerequisites for MLOps Engineering on AWS Training are as follows:
• Basic knowledge in Machine Learning and Python programming
• Working experience in Linux-based Operating System
• Good understanding of Continuous Delivery and Continuous Integration using DevOps tools
• Familiarity with AWS Cloud Services
• Understanding of Containers (Docker and Kubernetes) and container orchestration
• Understanding of Infrastructure as Code concepts
• Experience in building CI/CD pipelines with Jenkins
Target Audience
The target audience for MLOps Engineering on AWS training would be technology professionals who have experience in building and deploying Machine Learning models, as well as individuals who want to obtain expertise in managing and executing an MLOps workflow on the AWS Cloud
This course is also geared towards teams in organizations developing an MLOps pipeline around their existing machine learning models to make them more automated and simplified
Specific roles that would benefit from this course include ML developers, ML engineers, data scientists, cloud engineers, DevOps engineers and operations professionals
This training is ideal for those who want to gain an in-depth understanding of the process, tools and technologies needed to design, execute and manage MLOps on AWSLearning Objectives of MLOps Engineering on AWS
MLOps Engineering on AWS Training provides an understanding of the fundamentals, methods and best practices for helping developers and DevOps teams use automation, scalability and high availability in achieving a successful deployment of machine learning models on AWS.
The objectives of this training include:
1. Understand the core concepts of MLOps and the key components of its architecture
2. How to prepare, deploy, and operate ML models on AWS services, such as Amazon Sagemaker, Amazon EC2, and Amazon ECS
3. Configure data management and analytics pipelines on AWS
4. Deploy ML models in production on AWS layers
5. Monitor and optimize ML applications with Amazon Cloudwatch
6. Automate common tasks related to machine learning lifecycle using AWS CloudFormation and automation scripts
7. Understand the security and compliance requirements of deploying machine learning models to production on AWS.
BENEFITS OF LEARNING FROM WEBBRAINS
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Who can Learn MLOps Engineering on AWS Course ?
College Students
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Application Process
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