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AWS SageMaker
Learn to build, train, and deploy machine learning models at scale using Amazon SageMaker in real-world cloud environments.Preview AWS SageMaker course
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Learn to build, train, and deploy ML models using SageMaker Studio
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Implement end-to-end ML workflows using SageMaker Pipelines
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Utilize built-in algorithms and bring-your-own-model strategies
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Perform hyperparameter tuning and model evaluation
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Deploy scalable and cost-optimized endpoints in production
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Monitor and retrain models with SageMaker Model Monitor
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Machine Learning Engineers deploying models at scale
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Data Scientists transitioning to cloud-based platforms
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DevOps Professionals integrating ML with CI/CD workflows
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AI/ML Enthusiasts exploring managed ML services
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AWS Users expanding into ML services and automation
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Familiarize yourself with AWS basics before diving into SageMaker-specific modules.
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Use your AWS free-tier or sandbox account to practice with SageMaker Studio and notebooks.
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Follow hands-on labs closely and replicate them for different use cases.
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Bookmark concepts like Pipelines, Training Jobs, and Inference Endpoints.
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Experiment with real datasets and try bring-your-own-model (BYOM) setups.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of this course, you will be able to:
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Navigate the SageMaker ecosystem including Studio, Pipelines, and Projects
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Build ML models using built-in and custom algorithms
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Perform model training and tuning in cloud-based notebooks
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Deploy models as endpoints with autoscaling and monitoring
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Integrate SageMaker into larger AWS workflows (Lambda, S3, CloudWatch, etc.)
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Use SageMaker Clarify for bias detection and Model Monitor for retraining
Course Syllabus
Module 1: Introduction to AWS SageMaker
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Overview of SageMaker Architecture
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Key Components and Use Cases
Module 2: Working with SageMaker Studio and Notebooks
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Setting up Studio
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Data Exploration and Feature Engineering
Module 3: Model Training and Evaluation
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Built-in Algorithms
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Custom Model Training
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Hyperparameter Tuning
Module 4: Model Deployment and Hosting
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Deploying Real-time Endpoints
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Batch Transform Jobs
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A/B Testing
Module 5: Pipelines and Automation
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SageMaker Pipelines
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CI/CD with SageMaker Projects
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Integration with CodePipeline
Module 6: Monitoring and Optimization
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Model Monitor
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Clarify for Fairness and Bias Detection
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Logging and Cost Optimization
Module 7: Advanced Use Cases
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Image Classification with CNN
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Text Analysis and Sentiment Prediction
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Tabular Forecasting and Regression
Module 8: Security and Access Management
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IAM Roles and Policies
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Encryption and VPC Setup
Module 9: Interview Questions & Answers
Upon completion, learners will receive a Uplatz Certificate of Completion for AWS SageMaker. This certificate showcases proficiency in cloud-based ML model development, training pipelines, and deployment practices, aligned with real enterprise needs and AWS architecture standards.
AWS SageMaker opens doors to a wide range of roles in the AI and data ecosystem:
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Machine Learning Engineer (Cloud)
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Data Scientist – AWS Stack
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AI/ML Solution Architect
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MLOps Engineer
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Cloud AI Consultant
Organizations across finance, healthcare, e-commerce, and government actively recruit professionals skilled in SageMaker for production-scale model deployment.
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What is AWS SageMaker and why is it used?
Answer: SageMaker is a fully managed AWS service for building, training, and deploying ML models. It simplifies the machine learning workflow and provides scalable infrastructure, pre-built algorithms, and integration with other AWS services. -
What are the components of SageMaker?
Answer: Core components include SageMaker Studio (IDE), Notebooks, Training Jobs, Pipelines, Models, Endpoints, Model Monitor, and Clarify. These support the end-to-end ML lifecycle. -
How do you train a model in SageMaker?
Answer: You create a training job using either a built-in algorithm or a custom container, provide the dataset in S3, configure hyperparameters, and define the output path for model artifacts. -
What is the difference between real-time inference and batch transform?
Answer: Real-time inference serves predictions on demand via endpoints, while batch transform runs inference on large datasets asynchronously and saves results to S3. -
How does SageMaker Pipelines help in MLOps?
Answer: Pipelines automate the ML workflow including preprocessing, training, evaluation, and deployment. They enable CI/CD and reproducibility for ML models. -
What is SageMaker Clarify?
Answer: Clarify is a tool for detecting bias in datasets and models. It also provides explainability insights using SHAP values and supports compliance monitoring. -
What is hyperparameter tuning in SageMaker?
Answer: SageMaker can automatically tune model hyperparameters using Bayesian optimization to find the best combination for highest model accuracy. -
How can SageMaker models be monitored in production?
Answer: Using Model Monitor, you can track data drift, prediction errors, and generate alerts. It supports custom metrics and integrates with CloudWatch. -
What are some advantages of SageMaker over building your own ML infrastructure?
Answer: It reduces setup time, scales automatically, provides managed security, integrates with AWS services, and enables faster experimentation with pre-configured environments. -
Can you deploy a pre-trained model in SageMaker?
Answer: Yes. You can bring your own model by packaging it in a Docker container or using pre-trained model artifacts. SageMaker provides BYOM support via script mode or custom containers.