AWS SageMaker

AWS-SageMaker

AWS SageMaker is a comprehensive machine learning platform provided by Amazon Web Services (AWS) that facilitates the entire machine learning lifecycle. It offers a fully-managed environment where users can build, train, and deploy machine learning models efficiently. With features like Jupyter Notebook integration, SageMaker simplifies data analysis, model development, and validation. Users can easily access data stored in AWS S3, perform hyperparameter tuning, and create API endpoints for model deployment. This platform is designed to cater to both beginners and experienced data scientists, making machine learning more accessible and scalable in the cloud environment.

Towards AI

SageMaker is one of the core AI offerings from AWS — that helps us through all stages in the machine learning life cycle. It provides us with a simple Jupyter Notebook UI that can be used to script…

XGBoost in Amazon SageMaker

Towards Data Science

What is SageMaker? SageMaker is Amazon Web Services’ (AWS) machine learning platform that works in the cloud. It is fully-managed and allows one to perform an entire data science workflow on the…

How to Read Data Files on S3 from Amazon SageMaker

Towards Data Science

Amazon SageMaker is a powerful, cloud-hosted Jupyter Notebook service offered by Amazon Web Services (AWS). It’s used to create, train, and deploy machine learning models, but it’s also great for…

Aws Sagemaker: Train, Test & Deploy Models

Analytics Vidhya

AWS Sagemaker is a Machine Learning end to end service that solves the problem of training, tuning, and deploying Machine Learning models. It provides us with a Jupyter Notebook instance that runs on…...

Using Amazon SageMaker

Dive intro Deep Learning Book

Deep learning applications may demand so much computational resource that easily goes beyond what your local machine can offer. Cloud computing services allow you to run GPU-intensive code of this boo...

Using AWS SageMaker’s Linear Learner to Solve Regression Problems

Towards Data Science

AWS SageMaker is booming and proving to be one of the top services for building ML models and pipelines on the cloud. One of the best features of SageMaker is the wide array of in-built algorithms…

Hyperparameter Tuning and Image Classification in AWS Sagemaker using Jumpstart

Level Up Coding

Photo by Mehmet Ali Peker on Unsplash AWS provides an extensive array of tools designed to enhance efficiency for developers and business owners in different aspects. For data scientists and developer...

AWS SageMaker: AI’s Next Game Changer

Towards Data Science

Today AWS SageMaker was released, and it is awesome. I have mentioned in previous articles that we do mostly AWS deployments for our clients. Smaller models fit in a DigitalOcean droplet, but CNNs…

Deploy your own model with AWS Sagemaker

Analytics Vidhya

Sagemaker is a fully managed machine learning service,which provides you support to build models using built-in-algorithms, with native support for bring-your-own-algorithms and ML frameworks such as…...

Automating AWS SageMaker notebooks

Towards Data Science

SageMaker provides multiple tools and functionalities to label, build, train and deploy machine learning models at a scale. One of the most popular ones is Notebooks Instances which are used to…

Deploying a Custom Docker Model with SageMaker to a Serverless Front-end with S3

Towards Data Science

Deploying a model with AWS SageMaker is a great way to allow users or customers to interact with it. While you can use the many algorithms and models that come with SageMaker’s Python SDK, there are…

Deploying Your Llama Model via vLLM using SageMaker Endpoint

Towards Data Science

SageMaker is an AWS service that consists of a large suite of tools and services to manage a machine learning lifecycle. Its inference service is known as SageMaker endpoint. Under the hood, it is ess...