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Amazon SageMaker description
Amazon SageMaker is a cloud-based platform that streamlines the process of creating, training, and deploying machine learning models. It provides all the tools needed for each stage, removing technical roadblocks and allowing businesses to leverage the power of machine learning regardless of their current expertise. This makes it easier for companies to use data for predictions, improving efficiency in various applications.
Who is Amazon SageMaker best for
Amazon SageMaker is a comprehensive machine learning platform ideal for businesses wanting to build, train, and deploy ML models efficiently. Its scalability and AWS integration are praised, but users note potential cost challenges and a complex interface. Consider SageMaker if your business needs robust cloud-based ML capabilities and you're prepared for AWS's ecosystem.
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Best fit for enterprise companies.
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Suitable for various industries seeking to leverage AI/ML.
Amazon SageMaker features
| Supported Amazon SageMaker offers a variety of pre-trained models via SageMaker JumpStart, AWS Marketplace, and built-in algorithms, which can be fine-tuned for specific tasks. |
| Supported Amazon SageMaker offers comprehensive support for deploying machine learning models in a cloud environment, including various deployment options, scalable infrastructure, and integration with other AWS services. |
| Supported Amazon SageMaker fully supports GPU training for accelerated model training, offering various GPU instances, distributed training capabilities, and custom GPU-supported containers. |
| Supported SageMaker simplifies AI model creation through features like Model Builder and Canvas, improving efficiency. |
| Supported SageMaker supports metrics like accuracy, precision, recall, and F1-score. |
| Supported SageMaker supports model parallelism using its distributed model parallel library. |
Amazon SageMaker reviews
We've summarised 39 Amazon SageMaker reviews (Amazon SageMaker G2 reviews) and summarised the main points below.
- Highly scalable and compute-powerful, easily handles large datasets.
- Seamless integration with other AWS services like S3, Lambda, and data storage solutions.
- Provides built-in algorithms and frameworks that simplify model building.
- Offers pre-trained models that expedite development for common tasks.
- Easy to build, train, and deploy ML models at scale.
- Supports distributed training and a variety of machine learning frameworks.
- Wide range of instance types to optimize performance and cost.
- Excellent for deploying models in production with features like A/B testing and autoscaling.
- Difficult to estimate pricing accurately, even with the AWS pricing calculator.
- Running larger models for testing purposes can lead to unexpected costs.
- User interface is cluttered and feels like a client tool hosted on the web.
- Limited control over CI/CD pipelines, could be more self-managed.
- SageMaker Pipelines UI is less intuitive than alternatives like Airflow or Azure.
- Studio and Image terminals are not as user-friendly as dedicated IDEs like IntelliJ.
- Steep learning curve, especially for beginners without coding experience.
- Limited documentation and online resources for troubleshooting and learning advanced features.
- Can be expensive, especially for large projects or extended usage.
Amazon SageMaker pricing
The commentary is based on 5 reviews from Amazon SageMaker G2 reviews.
SageMaker pricing is generally seen as reasonable for its compute power and scalability. However, users have noted the difficulty in predicting costs, especially with larger models and varying configurations. While some find it light on the pocket, others find the GPU-enabled options and initial learning curve expensive.
Users sentiment
See the Amazon SageMaker pricing page.
Amazon SageMaker alternatives
amazon-sagemaker vs Vertex AIBetter for users without coding experience and those seeking simpler model building. A more user-friendly interface is reported. Integrates with Google Cloud products. Rated higher by users. However, it has a steeper learning curve and can be more expensive.Read more
SAS Visual Data Mining and Machine LearningBetter suited for enterprise customers with existing data science teams. Offers a visual interface and automation capabilities, but has a steeper learning curve and higher cost. Not as tightly integrated with cloud services. Has less momentum than SageMaker.Read more
SuperAnnotateBetter for creating labeled datasets for AI model training. Has strong project management and automation features. Is growing faster than Amazon SageMaker.Read more
Supervisely Computer Vision PlatformMore focused on computer vision tasks. Caters to larger companies and research institutions needing data labeling and model training. Has more momentum in terms of website traffic growth.Read more
CVAT.aiMore focused on data annotation for computer vision. Has significantly more momentum currently. Offers both cloud-based and on-premise deployment.Read more
Saturn CloudBetter for collaboration and has more positive user reviews. Simpler user interface and more intuitive platform. Amazon SageMaker alternative. Has received positive feedback for ease of use.Read more
Amazon SageMaker FAQ
What is Amazon SageMaker and what does Amazon SageMaker do?
Amazon SageMaker is a fully managed machine learning platform that empowers developers and data scientists to build, train, and deploy ML models quickly. It offers a comprehensive suite of tools for every stage of the ML lifecycle, from data preparation to model deployment and monitoring.
How does Amazon SageMaker integrate with other tools?
Amazon SageMaker seamlessly integrates with other AWS services like S3, Lambda, and various data storage solutions. This streamlines the machine learning process, from data preparation and model training to deployment and monitoring. It supports various machine learning frameworks and offers diverse instance types for optimized performance.
What the main competitors of Amazon SageMaker?
Top alternatives to Amazon SageMaker include Vertex AI, Azure Machine Learning, DataRobot, and H2O.ai. These competitors offer similar machine learning capabilities, often with varying pricing structures and specialized features.
Is Amazon SageMaker legit?
Yes, Amazon SageMaker is a legitimate and safe cloud-based machine learning platform offered by Amazon Web Services (AWS). It's a popular choice for businesses seeking to build, train, and deploy machine learning models, offering scalability and integration with other AWS services. While it has a steep learning curve and can be expensive, its power and features make it a viable option.
How much does Amazon SageMaker cost?
Amazon SageMaker pricing depends on the resources used, such as instance type, storage, and data processing. There is no fixed pricing structure publicly available. Contact Amazon for a customized quote.
Is Amazon SageMaker customer service good?
Customer reviews indicate that Amazon SageMaker's customer support is generally responsive and helpful. Users appreciate the readily available technical assistance and product knowledge. However, some have mentioned limited documentation for troubleshooting.
Reviewed by
Michal has worked at startups for many years and writes about topics relating to software selection and IT management. As a former consultant for Bain, a business advisory company, he also knows how to understand needs of any business and find solutions to its problems.
Tymon is a seasoned CTO who loves finding the perfect tools for any task. He recently headed up the tech department at Batmaid, a well-known Swiss company, where he managed about 60 software purchases, including CX, HR, Payroll, Marketing automation and various developer tools.