This is a podcast where we talk all-about real life experiences of dealing with data and machine learning tools, techniques and personalities. We cover not just the technical aspects but also the "life" aspects of working in the field.
Note: Opinions expressed are my own and do not express the views or opinions of my employer.
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This is a podcast where we talk all-about real life experiences of dealing with data and machine learning tools, techniques and personalities. We cover not just the technical aspects but also the "life" aspects of working in the field.
Note: Opinions expressed are my own and do not express the views or opinions of my employer.
23: Let’s Talk AWS SageMaker for ML Model Deployment
The Data Life Podcast
19 minutes 46 seconds
5 years ago
23: Let’s Talk AWS SageMaker for ML Model Deployment
In this episode, we talk about Amazon SageMaker and how it can help with ML model development including model building, training and deployment. We cover 3 advantages in each of these 3 areas.
We cover points such as:
1. Host ML endpoints for deploying models to thousands or millions of users.
2. Saving costs for model training using SageMaker.
3. Use CloudWatch logs with SageMaker endpoints to debug ML models.
4. Use preconfigured environments or models provided by AWS.
5. Automatically save model artifacts in AWS S3 as you train in SageMaker.
6. Use of version control for SageMaker notebooks with Github.
and more…
Please rate, subscribe and share this episode with anyone who might find SageMaker useful in their work. I feel that SageMaker is a great tool and want to share about it with data scientists.
For comments/feedback/questions or if you think I have missed something in the episode, please reach out to me at LinkedIn: https://www.linkedin.com/in/sanketgupta107/
The Data Life Podcast
This is a podcast where we talk all-about real life experiences of dealing with data and machine learning tools, techniques and personalities. We cover not just the technical aspects but also the "life" aspects of working in the field.
Note: Opinions expressed are my own and do not express the views or opinions of my employer.
