Mastering AWS Sage Maker: From Fundamentals to Advance
Unlock the Power of AWS SageMaker: Mastering Fundamentals and Advancing Your Skills
Updated on Jun, 2024
Language - English
Amazon AWS,AWS Certification,AWS Certified Developer - Associate
Lectures -8
Duration -54 mins
30-days Money-Back Guarantee
Get your team access to 10000+ top Tutorials Point courses anytime, anywhere.
Course Description
Course Description:
Unlock the full potential of AWS SageMaker and become a machine learning and data science expert with our comprehensive "Mastering AWS SageMaker" course. Whether you are a beginner looking to explore the world of machine learning or a seasoned professional seeking to enhance your skills, this course is your key to mastering the AWS SageMaker platform.
Course Highlights:
Fundamentals of AWS SageMaker: Begin your journey by understanding the core concepts of AWS SageMaker, cloud computing, and machine learning. You'll gain insights into the key components of SageMaker and how they fit into the machine learning workflow.
Data Preprocessing and Feature Engineering: Learn how to prepare and preprocess data for machine learning, an essential step in building robust models. Explore feature engineering techniques to extract meaningful insights from your data.
Model Building and Training: Dive into the heart of machine learning by creating, training, and fine-tuning models on SageMaker. Understand various algorithms, optimization strategies, and hyperparameter tuning for better model performance.
Deploying Models: Discover how to deploy your machine learning models into production with SageMaker. You'll explore best practices for deploying models at scale, ensuring high availability, and achieving optimal performance.
Automated Machine Learning (AutoML): Uncover the power of AutoML with SageMaker, allowing you to automate many aspects of the machine learning process, saving you time and effort in model development.
MLOps and Model Monitoring: Learn how to implement MLOps best practices and set up automated model monitoring to ensure your deployed models remain accurate and reliable.
Advanced Topics: Delve into advanced topics such as natural language processing (NLP), computer vision, and reinforcement learning on AWS SageMaker. Explore real-world use cases and applications.
Hands-On Projects: Throughout the course, you will work on practical projects and exercises, applying what you've learned to real-world scenarios.
Certification Preparation: If you're looking to earn AWS certification in machine learning, this course provides a strong foundation to help you succeed in your certification exam.
Who Should Enroll:
Data scientists and analysts
Software developers
Machine learning engineers
Data engineers
IT professionals
Anyone interested in mastering AWS SageMaker and machine learning
Goals
Course Description:
Unlock the full potential of AWS SageMaker and become a machine learning and data science expert with our comprehensive "Mastering AWS SageMaker" course. Whether you are a beginner looking to explore the world of machine learning or a seasoned professional seeking to enhance your skills, this course is your key to mastering the AWS SageMaker platform.
Course Highlights:
Fundamentals of AWS SageMaker: Begin your journey by understanding the core concepts of AWS SageMaker, cloud computing, and machine learning. You'll gain insights into the key components of SageMaker and how they fit into the machine learning workflow.
Data Preprocessing and Feature Engineering: Learn how to prepare and preprocess data for machine learning, an essential step in building robust models. Explore feature engineering techniques to extract meaningful insights from your data.
Model Building and Training: Dive into the heart of machine learning by creating, training, and fine-tuning models on SageMaker. Understand various algorithms, optimization strategies, and hyperparameter tuning for better model performance.
Deploying Models: Discover how to deploy your machine learning models into production with SageMaker. You'll explore best practices for deploying models at scale, ensuring high availability, and achieving optimal performance.
Automated Machine Learning (AutoML): Uncover the power of AutoML with SageMaker, allowing you to automate many aspects of the machine learning process, saving you time and effort in model development.
MLOps and Model Monitoring: Learn how to implement MLOps best practices and set up automated model monitoring to ensure your deployed models remain accurate and reliable.
Advanced Topics: Delve into advanced topics such as natural language processing (NLP), computer vision, and reinforcement learning on AWS SageMaker. Explore real-world use cases and applications.
Hands-On Projects: Throughout the course, you will work on practical projects and exercises, applying what you've learned to real-world scenarios.
Certification Preparation: If you're looking to earn AWS certification in machine learning, this course provides a strong foundation to help you succeed in your certification exam.
Who Should Enroll:
Data scientists and analysts
Software developers
Machine learning engineers
Data engineers
IT professionals
Anyone interested in mastering AWS SageMaker and machine learning
Prerequisites
Course Description:
Unlock the full potential of AWS SageMaker and become a machine learning and data science expert with our comprehensive "Mastering AWS SageMaker" course. Whether you are a beginner looking to explore the world of machine learning or a seasoned professional seeking to enhance your skills, this course is your key to mastering the AWS SageMaker platform.
Course Highlights:
Fundamentals of AWS SageMaker: Begin your journey by understanding the core concepts of AWS SageMaker, cloud computing, and machine learning. You'll gain insights into the key components of SageMaker and how they fit into the machine learning workflow.
Data Preprocessing and Feature Engineering: Learn how to prepare and preprocess data for machine learning, an essential step in building robust models. Explore feature engineering techniques to extract meaningful insights from your data.
Model Building and Training: Dive into the heart of machine learning by creating, training, and fine-tuning models on SageMaker. Understand various algorithms, optimization strategies, and hyperparameter tuning for better model performance.
Deploying Models: Discover how to deploy your machine learning models into production with SageMaker. You'll explore best practices for deploying models at scale, ensuring high availability, and achieving optimal performance.
Automated Machine Learning (AutoML): Uncover the power of AutoML with SageMaker, allowing you to automate many aspects of the machine learning process, saving you time and effort in model development.
MLOps and Model Monitoring: Learn how to implement MLOps best practices and set up automated model monitoring to ensure your deployed models remain accurate and reliable.
Advanced Topics: Delve into advanced topics such as natural language processing (NLP), computer vision, and reinforcement learning on AWS SageMaker. Explore real-world use cases and applications.
Hands-On Projects: Throughout the course, you will work on practical projects and exercises, applying what you've learned to real-world scenarios.
Certification Preparation: If you're looking to earn AWS certification in machine learning, this course provides a strong foundation to help you succeed in your certification exam.
Who Should Enroll:
Data scientists and analysts
Software developers
Machine learning engineers
Data engineers
IT professionals
Anyone interested in mastering AWS SageMaker and machine learning
Curriculum
Check out the detailed breakdown of what’s inside the course
Foundations of AWS SageMaker: Module 1 - Introduction and Basics
1 Lectures
-
Foundations of AWS SageMaker: Module 1 - Introduction and Basics
03:57
03:57
SageMaker Data Preparation Essentials
1 Lectures
Advanced Model Training with SageMaker: Distributed Training and Debugging
1 Lectures
Effective Model Deployment with Amazon SageMaker: Strategies for Success
1 Lectures
SageMaker Excellence: Best Practices and Case Studies in Machine Learning Operat
1 Lectures
AWS SageMaker Mastery: From Data to User Interface - Unleashing Functional Scena
1 Lectures
Leveraging AWS SageMaker: Hands-On Machine Learning with the Iris Dataset
1 Lectures
Leveraging AWS SageMaker: Building Machine Learning Models for Banking Data
1 Lectures
Instructor Details
AKHIL VYDYULA
Hello, I'm Akhil, an Associate Consultant at Atos India with a focus on the Advisory Consulting practice, specializing in Data and Analytics. My professional journey has led me through various facets of data analysis and modeling, particularly in the BFSI sector, where I've had the privilege of overseeing the full lifecycle of development and execution.
My skill set encompasses a wide range of data-related tasks, including data wrangling, feature engineering, algorithm development, model training, and implementation. I thrive on leveraging data mining techniques such as statistical analysis, hypothesis testing, regression analysis, as well as both unsupervised and supervised machine learning processes to extract meaningful insights and drive data-informed decisions. I'm particularly passionate about risk identification through decision models, and I've honed my expertise in Machine Learning Algorithms, Data/Text Mining techniques, and Data Visualization to effectively address these challenges.
Currently, I'm immersed in an exciting Amazon cloud project that involves end-to-end development of ETL processing. In this role, I craft ETL processing code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, execute scripts using EMR services, and load consolidated data into Postgres SQL (RDS/Redshift) on a full, incremental, and live basis. To streamline this process, I've automated it by creating jobs in Step functions, which trigger EMR instances to run scripts in a specific order and send notifications upon execution status changes. The scheduling of these Step functions is achieved through event bridge rules.
Additionally, I've worked extensively with AWS Glue, using it to replicate source data from on-premises systems to raw-layer S3 buckets via AWS DMS services. One of my key strengths lies in my ability to understand the nuances of data and apply the right transformations to convert data from multiple tables into key-value pairs. Furthermore, I've optimized the performance of stored procedures in Postgres SQL to execute second-level transformations by efficiently joining multiple tables and loading the data into final tables.
I'm passionate about harnessing the power of data to drive actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, feel free to connect with me. Let's explore the endless possibilities that data analytics has to offer!
Course Certificate
Use your certificate to make a career change or to advance in your current career.
Our students work
with the Best
Related Video Courses
View MoreAnnual Membership
Become a valued member of Tutorials Point and enjoy unlimited access to our vast library of top-rated Video Courses
Subscribe now
Online Certifications
Master prominent technologies at full length and become a valued certified professional.
Explore Now