MLOps Class - Admission Call

"Welcome to the MLOps with Amazon SageMaker course, a transformative journey into the world of machine learning operations and deployment, empowered by the robust capabilities of Amazon SageMaker.

Throughout this comprehensive course, participants will dive into the intricate intersection of machine learning and operations, learning how to streamline and automate the end-to-end machine learning lifecycle. Amazon SageMaker will serve as our guiding beacon, providing a unified platform for building, training, deploying, and managing machine learning models at scale.

Students will delve into foundational machine learning concepts, understanding data preparation, feature engineering, model training, and evaluation. Leveraging SageMaker's powerful capabilities, they will explore various algorithms, experimenting with model architectures to optimize performance.

The course will emphasize MLOps practices, where students will learn to orchestrate continuous integration and continuous deployment (CI/CD) pipelines for machine learning models. SageMaker's integration with popular DevOps tools will be highlighted, enabling participants to automate model deployments, monitor model performance, and manage version control seamlessly.

Moreover, participants will gain hands-on experience in model deployment and inference, understanding how to create robust, scalable, and cost-effective machine learning architectures using SageMaker's managed services. They will explore model monitoring, A/B testing, and gradual deployment strategies, ensuring the reliability and efficiency of deployed models in real-world scenarios.

By the conclusion of this course, students will have acquired a comprehensive skill set in MLOps methodologies using Amazon SageMaker, empowering them to architect, deploy, and manage machine learning models effectively. They will be equipped to navigate the complexities of deploying machine learning solutions, fostering innovation and efficiency within organizations leveraging the power of AI."

--- Course Content ---

Linux Basics:

1. Operating System Understanding (Kernel, shell, process)

2. Directory operations

3. File System

4. Linux Networking

5. Web server

Software Development Lifecycle:

1. Stages in SDLC

2. Git and GitHub

3. Three tier application build and deploy.

4. Jenkins

Cloud (Amazon Web Services)

1. IAM

2. Compute (EC2)

3. Storage (S3)

4. Databases (RDS)

5. AWS Glue

6. AWS EMR

7. AWS Redshift

Machine Learning 001

1. Core Python

2. Pandas and Numpy

3. Data visualization

4. Exploratory Data Analysis

5. Linear Regression

6. Deep Learning

MLOps

1. End to End Model Deployment, Monitoring, and Governance using SageMaker

2. Docker and Kubernetes (EKS)

3. End to End Model Deployment, Monitoring, and Governance using Kubeflow.

4. Assignments

Pls book a call to discuss more on Pricing and timing of class