Build ML Projects on AWS Master SageMaker

Unlock the Power of AWS SageMaker: Mastering Fundamentals and Advancing Your Skills

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Last Updated: 2024-12-23 05:12:23 English Course Type: Self Learning

Introduction to Amazon SageMaker: Explore the features and capabilities of SageMaker as a machine learning platform.
Introduction to Machine Learning: Understand the basics of machine learning, including supervised and unsupervised learning, algorithms, and models.
Data Visualization: Explore techniques for visualizing and understanding your data using tools and libraries available in SageMaker.
Model Training: Understand how to train machine learning models using SageMaker's infrastructure, including distributed training and hyperparameter tuning.

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

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.

Data Scientists: Data scientists looking to expand their machine learning and data modeling skills with a focus on using AWS SageMaker for developing, training, and deploying machine learning models.
Machine Learning Engineers: Machine learning engineers interested in mastering the tools and techniques within SageMaker to build, test, and deploy machine learning models at scale.
Software Developers: Developers who want to integrate machine learning into their applications and are interested in using SageMaker to streamline model development and deployment.
Business Analysts: Business analysts looking to gain insights from data using machine learning techniques and SageMaker.
AWS Enthusiasts: Those who are already familiar with AWS services and want to explore SageMaker's capabilities.

Section 1 : Foundations of AWS SageMaker 1 Lectures 00:03:57
Lecture 1 :
Introduction and Basics Preview
Section 2 : SageMaker Data Preparation 1 Lectures 00:05:14
Lecture 1 :
SageMaker Data Preparation Essentials
Section 3 : Advanced Model Training 1 Lectures 00:03:20
Lecture 1 :
Advanced Model Training with SageMaker: Distributed Training and Debugging
Section 4 : Model Deployment 1 Lectures 00:15:40
Lecture 1 :
Effective Model Deployment with Amazon SageMaker: Strategies for Success
Section 5 : Best Practices in Machine Learning Operations 1 Lectures 00:02:16
Lecture 1 :
SageMaker Excellence: Best Practices and Case Studies in Machine Learning ..
Section 6 : AWS SageMaker Mastery 1 Lectures 00:09:10
Lecture 1 :
AWS SageMaker Mastery: From Data to User Interface - Unleashing Functional ..
Section 7 : Hands-On with the Iris Dataset 1 Lectures 00:01:34
Lecture 1 :
Leveraging AWS SageMaker: Hands-On Machine Learning with the Iris Dataset
Section 8 : Building Models for Banking Data 1 Lectures 00:12:51
Lecture 1 :
Leveraging AWS SageMaker: Building Machine Learning Models for Banking Data

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AKHIL VYDYULA,

180 Course Views

4 Courses

Hello, 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!