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Prepare your business for the future!

Use Machine Learning to get actionable insights.

Certified AWS Machine Learning

Amazon Sagemaker


Certified Microsoft Azure AI Engineer


Prepare data for machine learning


Create model in Jupyter notebook

Train & Tune

Train your model, hyperparameter optimization


Deploy and manage models on powerful infrastructure

As a machine learning engineer, who is experienced with AWS, I may use a variety of services to prepare, build, train, tune, and deploy machine learning models.

For data preparation, I may use Amazon S3 for storage and Amazon Glue for data cleaning and transformation.

To build models, I use Amazon SageMaker, a fully managed service that provides a Jupyter notebook interface for building models.

For training and tuning, I use Amazon SageMaker’s built-in algorithms, or bring my own algorithms using Amazon Elastic Container Registry (ECR) or Amazon Elastic Container Service (ECS) to run them on SageMaker’s managed infrastructure.

For hyperparameter optimization, I use Amazon SageMaker’s built-in capabilities or use AWS Step Functions and Amazon SageMaker Automatic Model Tuning to automate the process.

Finally, for deployment, I may use Amazon SageMaker’s built-in hosting capabilities to deploy models to a variety of environments, such as on edge devices, on-premises, or on other cloud services. I may also use AWS Lambda and AWS API Gateway to deploy models as serverless functions and expose them as RESTful APIs.