Top things to know about Amazon Sagemaker
February 21, 2022
Top things to know about Amazon Sagemaker
Score 9 out of 10
Vetted Review
Verified User
Overall Satisfaction with Amazon SageMaker
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.
Pros
- 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
Cons
- 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
Do you think Amazon SageMaker delivers good value for the price?
Yes
Are you happy with Amazon SageMaker's feature set?
Yes
Did Amazon SageMaker live up to sales and marketing promises?
Yes
Did implementation of Amazon SageMaker go as expected?
Yes
Would you buy Amazon SageMaker again?
Yes
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