Machine Learning Pipeline on AWS Training

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The Machine Learning Pipeline on AWS Course Overview

Overview

Enroll for 3-day The Machine Learning Pipeline on AWS training course from Koenig Solutions accredited by AWS. The Machine Learning Pipeline on AWS course developers will learn to solve real world business problems using it.

 

Through a blend of hands-on labs and interactive lectures, participants will learn best practices for problem solving using Amazon Sagemaker and be able to select and justify the appropriate ML approach for a given business problem.

 

Target Audience:

  • Candidates working as Developer.
  • Candidates working as Solutions Architect.
  • Candidates working as Data engineer.

 

The 1-on-1 Advantage
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Flexible Dates

  • • Choose Start Date
  • • Reschedule After Booking
  • • Weekend / Evening Option
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4-Hour Sessions

Destination Training
(Dubai/London/Sydney
New York/Delhi/Goa
Singapore)

Flexi
(On Demand Videos)

You will learn:

Module 1: Introduction to Machine Learning and the ML Pipeline
  • Overview of machine learning, including use cases, types of machine learning, and key concepts
  • Overview of the ML pipeline
  • Introduction to course projects and approach
  • Introduction to Amazon SageMaker
  • Demo: Amazon SageMaker and Jupyter notebooks
  • Hands-on: Amazon SageMaker and Jupyter notebooks
  • Overview of problem formulation and deciding if ML is the right solution
  • Converting a business problem into an ML problem
  • Demo: Amazon SageMaker Ground Truth
  • Hands-on: Amazon SageMaker Ground Truth
  • Practice problem formulation
  • Formulate problems for projects
  • Overview of data collection and integration, and techniques for data preprocessing and visualization
  • Practice preprocessing
  • Preprocess project data
  • Class discussion about projects
  • Choosing the right algorithm
  • Formatting and splitting your data for training
  • Loss functions and gradient descent for improving your model
  • Demo: Create a training job in Amazon SageMaker
  • How to evaluate classification models
  • How to evaluate regression models
  • Practice model training and evaluation
  • Train and evaluate project models
  • Initial project presentations
  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning
  • Demo: SageMaker hyperparameter optimization
  • Practice feature engineering and model tuning
  • Apply feature engineering and model tuning to projects
  • Final project presentations
  • How to deploy, inference, and monitor your model on Amazon SageMaker
  • Deploying ML at the edge
  • Demo: Creating an Amazon SageMaker endpoint
  • Post-assessment
  • Course wrap-up
Live Online Training (Duration : 32 Hours)
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

2500 + If you accept merging of other students. Incl. Official Courseware
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
2150 Per Participant Incl. Official Courseware
Online
03 - 06 Apr 09:00 AM - 05:00 PM CST
(8 Hours/Day)
Online
01 - 04 May 09:00 AM - 05:00 PM CST
(8 Hours/Day)
Course Prerequisites
  • Python programming language
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic understanding of working in a Jupyter notebook environment

After completing this course, you will learn to:

  • Select and justify the appropriate ML approach for a given business problem.
  • Train, evaluate, deploy, and tune an ML model in Amazon SageMaker.
  • Describe some of the best practices for designing scalable, cost-optimised, and secure ML pipelines in AWS.

 

Student Feedback  (Check Koenig Feedback on Trustpilot)

Q1 Say something about the Trainer? Q2 How is Koenig different from other training Companies? Q3 Will you come back to Koenig for training ?
Student Name Feedback
Abu
United States
A1. Yes, she was very clear & a strong mastery of both English & Azure platforms hence following was easy. She was also patient and was answering every question asked, she also shared material & tips on how to pass the exam, Well done, and keep it up.
Nick Barker
United States
A1. Calm and understanding, she not only kept the course running well but also took time to help those with lots of questions. Very well done.
Robin Johnson
United States
A2. Individual attention during training, excellent customer service and coordination. Neha has been ever so helpful and is definitely an asset to Koenig.
Shivani Prasad
United States
A1. The trainer was great and his teaching methods were incredible. He used different teaching methods to ensure I was learning well. A fun trainer with lots of patience and understanding.
Adam
United States
A1. The trainer was very knowledgeable and well prepared about the presentation/training subject, with a positive attitude towards students and making every effort in answering questions during the training session. Top-notch service!
Suresh
United States
A1. Exceptional... recommend to everyone!
Shyam Vaddakapett
United States
A1. My trainer, Mohit Kakkar was excellent as he focused on hands-on assignments to supplement the concepts of AZ 900. The training coordinator Jasmeet as well as the ops manager Sheshadri are also very professional and customer-centric. I recommend Koenig for training courses, and I will take some more courses with them soon.
Gilles Chirlias
United States
A1. Chavi was a great teacher, I have learned a lot from her. I would like to have her for the next training, which would be the AZ 301 and AZ 500.
Abdulrahman Saad Alshahrani
United States
A1. Excellent trainer. I will take another course with him someday. He deserves a good appreciation from his Manager.
Anonymous
United States
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1-on-1 Private - Select your start date. You will be the only student in the class.
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The Fee includes:
  • Official courseware
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Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1-on-1 you can select your own schedule, other students can be merged but you select the schedule. Choose 1-on-1 if published schedule do not meet your requirement. If you also want a private session, opt for 1-on-1 Public.
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