Amazon SageMaker refers to a completely-managed service. It helps data scientists as well as developers in easily and quickly building, training, and deploying ML models. Our course has been designed to cover a complete overview of this service and take a closer look at its three main components. These components are related to training, hosting and notebooks.
The Amazon SageMaker course is crafted by keeping data scientists and other such professionals in mind. It equips learners with an overall understanding and knowledge of this Studio, an ML integrated development environment. Learners can also explore the navigation, functionalities and setup of this Studio. Dive in to learn data processing techniques for ML-readiness & bias detection.
Overview of AWS SageMaker Course
The AWS SageMaker online course includes model development. This encompasses tuning, debugging and evaluation with this Studio. Inference and Deployment modules are behind teaching more about effectively managing models and automating ML workflows. We offer monitoring lessons that focus mainly on detecting drifts and maintaining model quality. Insights are provided on Resource updates and management for efficient operation.
Our Amazon SageMaker training first introduces aspirants to the plethora of opportunities that tag along the fast growing technology – artificial intelligence. We then move on to learn about the job responsibilities fulfilled by a data scientist. Training, validation and testing of the data model are implemented perfectly in their infrastructure or system. Get to know the distinct features of this tool like large data processing, end-to-end ML algorithm and much more. All these procedures can be implemented to know better about this exceptional service.
What Will You Learn in SageMaker Training
- Basics of machine learning using Amazon SageMaker
- Creating and using SageMaker notebooks
- Data preparation for machine learning
- Training models using built-in algorithms
- Using TensorFlow, PyTorch, and XGBoost
- Hyperparameter tuning
- Deploying models for inference
- Monitoring and managing deployed models
SageMaker Course Objectives
- Aspirants gain familiarity with top and most popular ML frameworks. These include TensorFlow, Apache MXNet & Apache Spark.
- Learn to run a training job, create a model with this service & hyperparameter tuning job.
- Gain in-depth knowledge & understanding of reinforcement learning through Amazon SageMaker RL.
- Learn about protecting data in transit & at rest using encryption in this service.
- Understand the uses of Jupyter notebooks as well as the role they play in interactive & collaborative ML development.
- Acquire practical skills & best practices knowledge around deploying ML models.
Target Audience for Amazon SageMaker Training?
This course by kodestree has been designed solely for individuals who want to gain a better understanding of machine learning technology. These are those who’re keen on deploying the same for real-world applications. Many professionals benefit from our course and can opt for it to advance their career. It’s also a great way to prepare for the related SageMaker certification exam.
- Data Scientists
- Data Engineers
- Machine Learning Engineers
- Data Analysts
- Financial Analysts
- Cloud Architects
- Technical Managers
Prerequisites for AWS SageMaker Online Course
Our course has been designed meticulously for AWS beginners. Even though there are no fixed prerequisites, a basic cloud computing understanding can be helpful.
