Build ML Projects on AWS: Master SageMaker
Unlock the Power of AWS SageMaker: Mastering Fundamentals and Advancing Your Skills
Updated on Jun, 2025
Language - English
Development ,Data Science,Machine Learning
Lectures -8
Duration -54 mins
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Course Description
This course teaches building machine learning projects with AWS SageMaker-the integrated service that makes it easy to develop, train, and deploy machine learning models. While completing this course, participants learn the foundations of SageMaker, which include how data preparation should be done, advanced techniques in model training, and effective deployment strategies. It covers best practices as well as case studies of real-world projects, so learners can apply their new skills to actual projects with datasets like Iris and banking data. The class is great for data scientists and ML engineers interested in using AWS to do machine learning.
Goals
- Introduction to Amazon SageMaker: Discover what is possible with SageMaker in terms of its features and capabilities as a machine learning platform.
- Introduction to Machine Learning: Basic concepts of machine learning - supervised and unsupervised learning, algorithms, and models.
- Data Visualization: Learn some techniques on how you can visualize and understand your data using the tools and libraries available in SageMaker.
- Model Training: Understand how to train the machine learning models using SageMaker's infrastructure, including distributed training and hyperparameter tuning.
Prerequisites
Basic Cloud Computing Knowledge: It's essential to have a fundamental understanding of cloud computing concepts and services, as AWS SageMaker is a cloud-based machine learning platform.
Machine Learning Fundamentals: A solid understanding of machine learning concepts and algorithms is usually necessary. You should be familiar with supervised and unsupervised learning, regression, classification, and model evaluation.
Jupyter Notebooks: Many SageMaker courses use Jupyter notebooks for practical exercises. Familiarity with Jupyter notebooks is helpful.
Curriculum
Check out the detailed breakdown of what’s inside the course
Foundations of AWS SageMaker
1 Lectures
-
Introduction and Basics
03:57
03:57
SageMaker Data Preparation
1 Lectures
Advanced Model Training
1 Lectures
Model Deployment
1 Lectures
Best Practices in Machine Learning Operations
1 Lectures
AWS SageMaker Mastery
1 Lectures
Hands-On with the Iris Dataset
1 Lectures
Building Models for Banking Data
1 Lectures
Instructor Details
AKHIL VYDYULA
Data Scientist | Data & Analytics Specialist | EntrepreneurHello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.
My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.
I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.
Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.
Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.
I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!
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