Name: Amazon SageMaker
Rating: 8.1 (53 reviews)
Author: Amazon AWS

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### Machine Learning Development
Users generally praise Amazon SageMaker's machine learning development capabilities. They appreciate the ease of building, training, and deploying machine learning models all within one platform. The flexibility to choose different machine types for each phase of development is highlighted as a valuable feature. Additionally, users find the support for various ML algorithms and frameworks, such as PyTorch, TensorFlow, and Keras, to be beneficial for their projects. The availability of preloaded sample notebooks and a vast repository of materials aids in getting started quickly and efficiently. Many users also mention the convenience of deploying ML models as HTTP endpoints, showcasing the platform's user-friendly approach to machine learning development.

### Model Training and Deployment
Users generally praise Amazon SageMaker for its model training and deployment capabilities. They appreciate the ease of deploying machine learning models as HTTP endpoints and the ability to quickly build, train, and deploy models at any scale. Some users find the flexibility to deploy custom algorithms using Docker particularly useful, along with the support for A/B testing and batch processing. However, there are concerns about unclear documentation for model monitoring post-deployment, the abundance of APIs leading to confusion, and the need for improvements in the user interface and project management features. Despite these drawbacks, the overall sentiment leans towards the effectiveness and efficiency of Amazon SageMaker in model training and deployment.

### Infrastructure and Scaling
Users generally praise Amazon SageMaker's integration with AWS services and its scalability features. While some users appreciate the ease of deploying machine learning models at any scale, others express concerns about the high cost of endpoints, especially when not in use. The platform's auto-scaling infrastructure and out-of-the-box versioning capabilities receive positive feedback, but there are criticisms regarding the lack of control over underlying infrastructure for teams with experts in this area. Additionally, users highlight the convenience of integrating SageMaker with other AWS ecosystem components, such as S3 and Lambda, but note the limited support for third-party software. Despite some drawbacks, Amazon SageMaker's infrastructure and scaling functionalities seem to offer a balance of benefits and challenges for users.

### Integration and API Management
Users find Amazon SageMaker's integration and API management capabilities to be a standout feature, particularly praising its ability to effortlessly connect with AWS services like S3, Secrets Manager, and Lambda. The flexibility to deploy custom algorithms using Docker is highlighted as a unique advantage, setting it apart from other cloud ML platforms. Additionally, the support for A/B testing, batch processing, lambda scheduling, reinforcement learning, and third-party software integration through the AWS Marketplace further enhances the platform's appeal. The well-managed GitHub repository and comprehensive documentation are also commended for providing users with the necessary resources to navigate and utilize these integration features effectively.

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Amazon SageMaker

AI Development

Likelihood to Recommend

Amazon strives to be Earth's most customer-centric company where people can find and discover virtually anything they want to buy online. By giving customers more of what they want - low prices, vast selection, and convenience - Amazon continues to grow and evolve as a world-class e-commerce platform.

Founded by Jeff Bezos, the Amazon.com website started in 1995 as a place to buy books because of the unique customer experience the Web could offer book lovers. Bezos believed that only the Internet could offer customers the convenience of browsing a selection of millions of book titles in a single sitting. During the first 30 days of business, Amazon fulfilled orders for customers in 50 states and 45 countries - all shipped from his Seattle-area garage.

Amazon's evolution from Web site to e-commerce partner to development platform is driven by the spirit of innovation that is part of the company's DNA. The world's brightest technology minds come to Amazon.com to research and develop technology that improves the lives of shoppers and sellers around the world.

AI Development Platforms include features that focus on data ingestion and preparation, AI model and framework availability, scale, and security.

Model catalog

Teams can choose from a wide range of AI models as a starting point.

Machine learning frameworks

A wide variety of machine learning frameworks are available and to be used when training models.

Data integration

Enterprise data can be ingested through APIs or prebuilt connectors and used to train or fine-tune models.

Data management

Ingested data can be stored and prepped, with structures like data lakehouses available to handle large amounts of data from disparate sources.

Data monitoring and version control

Teams can track which data is used in training at which point and roll back to previous versions as needed.

Automated model training

After teams begin the process, model training can continue autonomously, enabling faster deployment.

Managed scaling

The platform provides the computing resources needed when they’re needed, allowing users to scale training and use up or down.

Model deployment

After training is complete, models can be integrated into business applications using API endpoints or other convenient, standard methods.

Security and compliance

End to end encryption, GDPR compliance, SSO, role-based permissioning, and other precautions are available to protect proprietary business data.

Adobe Target

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Amazon SageMaker 2018-08-28 21:47:21

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Amazon SageMaker 2018-05-18 15:00:13

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Amazon Sagemaker has multiple applications and use cases in our organization. It is used to create machine learning models for our call center team to analyse frequently raised customer problems, widely accepted solutions. These models help in reducing operating cost by automating and optimizing processes with minimal manual intervention. The other usecase include product development which required decision making based on image processing.

Machine Learning at scale by deploying huge amount of training data
Accelerated data processing for faster outputs and learnings
Kubernetes integration for containerized deployments
Creating API endpoints for use by technical users

The UI can be eased up a bit for use by business analysts and non technical users
For huge amount of data pull from legacy solutions, the platform lags a bit
Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized

Studio Lab
Model Training
Pipelines
Kubernetes Integration

Machine Learning models help in reducing operating cost for manual intensive processes by deploying chatbots
Improvement in product roadmap for learning about customer feedback on an early stage
Supporting analytics and data science team to share correct insights and models with business teams

We use SageMaker in the engineering and data science departments to host Jupyter notebooks, periodically retrain models, and serve models in production. Data scientists work in Jupyter notebooks hosted on SageMaker notebook instances instead of their local machines. We often inject models into AWS-provided containers, and use SageMaker to provide a managed, auto-scaling HTTP interface.

SageMaker is useful as a managed Jupyter notebook server. Using the notebook instances' IAM roles to grant access to private S3 buckets and other AWS resources is great. Using SageMaker's lifecycle scripts and AWS Secrets Manager to inject connection strings and other secrets is great.
SageMaker is good at serving models. The interface it provides is often clunky, but a managed, auto-scaling model server is powerful.
SageMaker is opinionated about versioning machine learning models and useful if you agree with its opinions.

SageMaker does not allow you to schedule training jobs.
SageMaker does not provide a mechanism for easily tracking metrics logged during training.
We often fit feature extraction and model pipelines. We can inject the model artifacts into AWS-provided containers, but we cannot inject the feature extractors. We could provide our own container to SageMaker instead, but this is tantamount to serving the model ourselves.

We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

AWS Lambda, Amazon API Gateway, Stitch, Amazon Redshift

We are using the SageMaker service from AWS for POC, and to build the final model on the large dataset of healthcare domain under the R&amp;D department. SageMaker also provides hosting functionality, so that we can host a created model for the end-level application which is accessible through a simple API call from any application.

Provided an instance of Jupyter notebook for development script, which made it very easy to manage and develop any script.
Our system is cloud-based, and we are charged only for what we use and how long we use it.
We can choose multiple servers for Training, without any headache of distribution.
Most of the libraries are supported.
All training, testing, and models are stored on S3, so it's very easy to access whenever we require it.

It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

Using SageMaker, we can truly implement 'fail early, learn fast,' using an on-demand server for training.
It also saves your money from investing in a physical server for very rare use.
However, the pricing is high, but it will cost you only for what you use.

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Amazon SageMaker

Overview

What is Amazon SageMaker?