Build and End to End ML Projects on AWS SageMaker
What you'll learn
- 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.
Requirements
- 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.
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
Who this course is for:
- 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.
Instructor
- 4.0 Instructor Rating
- 1,140 Reviews
- 72,140 Students
- 46 Courses
Hello, I'm Akhil Vydyula — Lead Data Engineer at Publicis Sapient, and Former Senior Data Scientist at PwC.
With over 5 years of rich industry experience and a strong focus on the BFSI sector, I’ve led and delivered end-to-end data and analytics solutions that power strategic decisions and transform business outcomes.
At Publicis Sapient, I currently lead complex data engineering initiatives, leveraging my deep expertise in cloud-native platforms like AWS to architect robust, scalable data pipelines. My work spans across developing and optimizing ETL workflows using PySpark and Spark SQL, orchestrating data flows via EMR, Step Functions, and EventBridge, and driving real-time and batch data processing into PostgreSQL (RDS/Redshift) environments. I've also implemented AWS Glue and DMS to seamlessly replicate and transform large-scale on-premise data into cloud-native formats.
Previously, at PwC, I specialized in advanced analytics and machine learning within the Advisory Consulting practice. I’ve built and deployed predictive models using statistical analysis, regression, classification, clustering, and text mining—particularly for risk identification and decision modeling. My passion lies in transforming raw data into actionable insights through effective data storytelling and visualization.
In parallel to my corporate career, I bring over 5 years of teaching experience, mentoring hundreds of aspiring data professionals. I’m deeply committed to helping students break into the data industry by translating real-world challenges into practical learning experiences.
Whether it's building data pipelines, uncovering business insights, or shaping the next generation of data talent, I thrive at the intersection of technology, strategy, and impact.
Let’s connect if you're passionate about data, eager to learn, or looking to collaborate on meaningful, data-driven initiatives.