You are an AWS customer support. Based on the context in question-answer format delimited in triple delimiter, answer the question. ```Question: What is Managed Spot Training?Answer: Managed Spot Training with Amazon SageMaker lets you train your ML models using Amazon EC2 Spot instances, while reducing the cost of training your models by up to 90%. Question: Which AWS Regions are supported with Managed Spot Training?Answer: Managed Spot Training is supported in all AWS Regions where Amazon SageMaker is currently available.
 Question: How are Savings Plans for Amazon SageMaker different from Compute Savings Plans for Amazon EC2?Answer: The difference between Savings Plans for Amazon SageMaker and Savings Plans for EC2 is in the services they include. SageMaker Savings Plans apply only to SageMaker ML Instance usage. Question: Which instances can I use with Managed Spot Training?Answer: Managed Spot Training can be used with all instances supported in Amazon SageMaker.
 Question: What components of Amazon SageMaker can be added to Amazon SageMaker Pipelines?Answer: The components available through Amazon SageMaker Studio, including Amazon SageMaker Amazon Clarify, Amazon SageMaker Data Wrangler, Amazon SageMaker Feature Store, Amazon SageMaker Experiments, Amazon SageMaker Debugger, and Amazon SageMaker Model Monitor, can be added to SageMaker Pipelines. Question: What are Amazon SageMaker Savings Plans?Answer: Amazon SageMaker Savings Plans offer a flexible usage-based pricing model for Amazon SageMaker in exchange for a commitment to a consistent amount of usage (measured in $/hour) for a one- or three-year term. Amazon SageMaker Savings Plans provide the most flexibility and help to reduce your costs by up to 64%. These plans automatically apply to eligible SageMaker ML instance usages, including SageMaker Studio notebooks, SageMaker On-Demand notebooks, SageMaker Processing, SageMaker Data Wrangler, SageMaker Training, SageMaker Real-Time Inference, and SageMaker Batch Transform regardless of instance family, size, or Region. For example, you can change usage from a CPU instance ml.c5.xlarge running in US East (Ohio) to an ml.Inf1 instance in US West (Oregon) for inference workloads at any time and automatically continue to pay the Savings Plans price. Question: Can I use SageMaker Inference Recommender in one Region and benchmark in different Regions?Answer: At launch, we will support all Regions supported by Amazon SageMaker, except the AWS China Regions. Question: How does Amazon SageMaker Components for Kube Your answer should have exactly 5 paragraphs. Make sure that the 4th paragraph starts with the word "thieves" and that each paragraph is separated by two new lines.
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