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1 You Can Visit All Seven Continents. But Should You? 26:46
108. How to Solve Lambda Python Cold Starts
Manage episode 389516288 series 2980070
In this episode, we discuss how you can use Python for data science workloads on AWS Lambda. We cover the pros and cons of using Lambda for these workloads compared to other AWS services. We benchmark cold start times and performance for different Lambda deployment options like zip packages, layers, and container images. The results show container images can provide faster cold starts than zip packages once the caches are warmed up. We summarize the optimizations AWS has made to enable performant container image deployments. Overall, Lambda can be a good fit for certain data science workloads, especially those that are bursty and need high concurrency.
💰 SPONSORS 💰 AWS Bites is brought to you by fourTheorem, an Advanced AWS Partner. If you are moving to AWS or need a partner to help you go faster, check us out at fourtheorem.com ! In this episode, we mentioned the following resources.
- Our blog post detailing our research on how to optimise Python Data Science in AWS Lambda: https://fourtheorem.com/optimise-python-data-science-aws-lambda/
- The repository with our benchmarks and related visualizations: https://github.com/fourTheorem/lambda-datasci-perf
- On-demand Container Loading on AWS Lambda (AWS Paper): https://arxiv.org/abs/2305.13162
Do you have any AWS questions you would like us to address? Leave a comment here or connect with us on X, formerly Twitter: - https://twitter.com/eoins - https://twitter.com/loige
146 tập
Manage episode 389516288 series 2980070
In this episode, we discuss how you can use Python for data science workloads on AWS Lambda. We cover the pros and cons of using Lambda for these workloads compared to other AWS services. We benchmark cold start times and performance for different Lambda deployment options like zip packages, layers, and container images. The results show container images can provide faster cold starts than zip packages once the caches are warmed up. We summarize the optimizations AWS has made to enable performant container image deployments. Overall, Lambda can be a good fit for certain data science workloads, especially those that are bursty and need high concurrency.
💰 SPONSORS 💰 AWS Bites is brought to you by fourTheorem, an Advanced AWS Partner. If you are moving to AWS or need a partner to help you go faster, check us out at fourtheorem.com ! In this episode, we mentioned the following resources.
- Our blog post detailing our research on how to optimise Python Data Science in AWS Lambda: https://fourtheorem.com/optimise-python-data-science-aws-lambda/
- The repository with our benchmarks and related visualizations: https://github.com/fourTheorem/lambda-datasci-perf
- On-demand Container Loading on AWS Lambda (AWS Paper): https://arxiv.org/abs/2305.13162
Do you have any AWS questions you would like us to address? Leave a comment here or connect with us on X, formerly Twitter: - https://twitter.com/eoins - https://twitter.com/loige
146 tập
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1 141. Step Functions with JSONata and Variables 15:43

1 140. DuckDB Meets AWS: A Match Made in Cloud 17:38

1 139. Building Great APIs with Powertools 24:32

1 138. How Do You Become A Cloud Architect? 39:02



1 135. Serverless is making a comeback where you least expect it 21:46

1 133. Building Businesses in the Cloud with Fiona McKenna 28:28


1 131. What do you do about CloudFormation Drift? 19:44

1 130. Growing in Tech with Farrah Campbell 29:55


1 128. Writing a book about Rust & Lambda 26:58

1 127. Which Load Balancer should you use? 24:47

1 125. A first look at CloudFront Hosting Toolkit 33:36

1 123. What do you need to know about DynamoDB? 35:45



1 119. The state of AWS 2024 (AnsWeRS community survey commentary) 39:34

1 118. The landing zone: Managing multiple AWS accounts 25:48

1 117. What do EBS and a jellyfish have in common? 21:03

1 116. What is RAM (Resource Access Manager)? 13:45

1 115. What can you do with Permissions Boundaries? 13:01

1 114. What's up with LLRT, AWS' new Lambda Runtime? 30:34

1 113. How do you revoke leaked credentials? 11:34

1 112. What is a Service Control Policy (SCP)? 18:47

1 111. How we run a Cloud Consulting business 45:45

1 110. Why should you use Lambda for Machine Learning? 24:28

1 109. What is the AWS Project Development Kit (PDK)? 28:41

1 108. How to Solve Lambda Python Cold Starts 20:52

1 107. Expert opinions from re:Invent 2023 20:45



1 103. Building GetAI Features with Bedrock 20:54

1 102. Getting Ampt with Jeremy Daly 1:10:47

1 101. Package and Distribute Lambda Functions for fun and profit 18:13

1 100. Exploring Ampt, a new way to build cloud apps on AWS 23:34

1 99. The fears of adopting AWS (and how to fight them) 23:03

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