Tau Legion

Sagemaker AI Development (Soon, it will have a mind) Through Amazon SageMaker, Artificial Intelligence, and Machine Learning with Python Course

Tau Legion’s Development Goals

1. Focused learning algorithm with a trained model into Amazon Web Services’ SageMaker.
2. Built-in scale on-demand
3. Data management with use of a Data Lake solution
4. Clean Interface for application using API Gateway and Lambda
5. Amazon tools integration to secure, catalog, tranform datasets into visualization

My Expectations

1. Foundation understanding of SageMaker
2. Create a robust machine
3. Smartly incorporate Amazon tools into Tau Legion’s ecosystem

Amazon SageMaker, Artificial Intelligence, and Machine Learning with Python Course

This project’s course focuses on:

1. Cloud-based machine learning style.
2. Learning the most useful algorithms, reducing wasting time diving through oceans of information and techniques
3. Cloud-based service allows for integration applications and support for a wide variety of programming languages.
4. Whether using small data or big data, the elastic nature of the AWS cloud allows for efficient managements and resources.
5. Lastly, no upfront cost or commitment – pay only for what I need and use

Hands-on Labs

In this course, I will work through hands-on labs to develop solutions to some challenging problems.

What does this project offer me? Here are a few things this course has:

AWS SageMaker

• How to deploy a Notebook instance on the AWS Cloud
• Understanding into algorithms provided by SageMaker service
• Learn how to train, optimize and deploy the models

AI Services

In the AI Services section,

• Learn about a set of pre-trained services that can directly integrate with applications
• Within a few minutes, build image and video analysis applications, similiar to face recognition. I have built an OpenCV, SSD, and MTCNN for facial recognition, prior to Tau_Legion
• Develop solutions for natural language processing, like finding sentiment, text translation, and conversational chatbots. Additionally, I have developed an Seq2Seq Chabot.

Integration

• Learning algorithms is one part of the story - Understand know how to integrate the trained models in the application
• Learn how to host models, scale on-demand, handle failures
• Provide a clean interface for the applications using Lambda and API Gateway

Data Lake

• Data management is one of the most complex and time-consuming activities when working on machine learning projects.

• With AWS, a variety of powerful tools for ingesting, cataloging, transforming, securing, visualization of your data assets.

• To collect the aforementioned data, thhe project will have a data lake solution in this course.

Machine Learning Certification

• This project is on my path to becoming AWS Machine Learning Specialty Certified.

Source Code

• The source code for this project available on Git that ensures latest code availablity

Developer

My name is Jeremy Wood, and I am excited about developing this project through the instruction of Chandra Lingam. I have completed more than 10 machine learning models. This will be my first developed solely in the cloud environment.

Note from Instructor

My name is Chandra Lingam, and I am the instructor for this course. I have over 50,000 thousand students and spend considerable amount of time keeping myself up-to-date and teach cloud technologies from the basics.

I have the following AWS Certifications: Solutions Architect, Developer, SysOps, Solutions Architect Professional, Machine Learning Specialty. I am looking forward to meeting you.

Course Lectures are available on Undemy’s Learning Platforms: