Applications based on machine learning (ML) can provide tremendous business value. One of the advantages of running ML on the AWS Cloud is that you can continually optimize your workloads and reduce your costs. The development, training, maintenance, and performance tuning of ML models is an iterative process that requires continuous improvement. Determining the optimum state in the model while going through the permutations and combinations of model parameters and data dependencies to adjust is just one leg of the journey. There is more to optimizing the cost of ML than just algorithm performance and model tuning. This video explains how to apply such optimization to ML workloads and shares best practices for training and inference.
Visit our ML cost optimization Knowledge Hub:
pages.awscloud.com/EMEA-ML-Cost-Optimization.html
#MachineLearning #AmazonWebServices Subscribe to AWS Online Tech Talks On AWS:
youtube.com/@AWSOnlineTechTalks?sub_confirmation=1
Follow Amazon Web Services:
Official Website: aws.amazon.com/what-is-aws
Twitch: twitch.tv/aws
Twitter: twitter.com/awsdevelopers
Facebook: facebook.com/amazonwebservices
Instagram: instagram.com/amazonwebservices
☁️ AWS Online Tech Talks cover a wide range of topics and expertise levels through technical deep dives, demos, customer examples, and live Q&A with AWS experts. Builders can choose from bite-sized 15-minute sessions, insightful fireside chats, immersive virtual workshops, interactive office hours, or watch on-demand tech talks at your own pace. Join us to fuel your learning journey with AWS.
#AWS
- Saving cost on your machine learning training and inference on AWS ( Download)
- Save up to 71% on Your Machine Learning Inference Costs Using Amazon EC2 Inf1 - AWS Online Tech Talk ( Download)
- AWS re:Invent 2022 - How four customers reduced ML inference costs and drove innovation (CMP226) ( Download)
- Machine Learning in 15: Amazon SageMaker High-Performance Inference at Low Cost ( Download)
- Optimizing your Machine Learning costs on Amazon SageMaker with Savings Plans (April 2021) ( Download)
- AWS re:Invent 2022 - Deploy ML models for inference at high performance & low cost, ft AT&T (AIM302) ( Download)
- End To End Machine Learning Project Implementation Using AWS Sagemaker ( Download)
- AWS re:Invent 2019: Running lean architectures: How to be cost-effective on AWS (ARC209-R1) ( Download)
- Intro to ML Pipelines: Build Reliable AI Workflows - MLOps Workshop ( Download)
- How to build, train, and deploy machine learning models using Amazon SageMaker ( Download)
- AWS re:Invent 2022 - Choosing the right accelerator for training and inference (CMP207) ( Download)
- AWS Summit DC 2022 - Amazon SageMaker Inference explained: Which style is right for you ( Download)
- Well-Architected Machine Learning and Cost Optimizations on AWS ( Download)
- Deploying machine learning models for inference- AWS Virtual Workshop ( Download)
- AWS Summit SF 2022 - High-performance & cost-effective model deployment with Amazon SageMaker ( Download)
