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Amazon SageMaker – Build, train, and deploy machine learning models at scale
- gk1 9y agoThis blog post also includes screenshots: https://aws.amazon.com/blogs/aws/sagemaker/ https://aws.amazon.com/blogs/aws/sagemaker/
- kernel_sanders 9y agoSageMaker {1G} Human Druid - Sage {G}, Tap: Create 0/1 Plant Token named Seed of Knowledge Sacrifice {X} Plants: Look at the top X cards of opponent's library 1/1
- orangejewce 9y agoI thought MTG too.
- xtracto 9y agoI wonder how does this compare to offerings like DataRobot and the like.
- GFischer 9y ago"The process of incorporating machine learning into an application often involves a team of experts tuning and tinkering for months with inconsistent setups. Businesses and developers want an end-to-end, development to production pipeline for machine learning" This seems to be a complete pipeline, including deployment to production. Edit: DataRobot too, I misread. Edit2: DataRobot seems to offer a "codeless" approach. Thanks for sharing, I'll check it out. I was wondering at the difference with Microsoft Azure Machine Learning Studio (which I haven't used yet).
- michaelbarton 9y agoIf anyone from AWS is in this forum, could you comment if the custom Docker training in sagemaker can also be used for general optimisation of any dockerised objective function, e.g. bayesian hyper parameter training? In the blog post example there is this python code: def train( channel_input_dirs, hyperparameters, output_data_dir, model_dir, num_gpus, hosts, current_host): Would I also write some kind of similar function for scoring the result of the training? To provide some context, I work in bioinformatics where some of our algorithms have 100s of parameters. This is not ML where we want to classify or predict but rather optimise the parameters for a given objective function. If sagemaker allows general optimisation in an AWS lambda like way, that would be very useful.
- tomfaulhaber 9y agoEngineer on the SageMaker team here. There are no restrictions on the types of algorithms that you can optimize using the HyperParameterOptimization service. SageMaker is designed for machine learning which means it's optimized for algorithms that process a lot of data to develop a model where each run of the algorithm may generate an objective function value (or potentially many such as the value may change during training). If this structure fits your problem, SageMaker could be useful to you even if your problem isn't strictly "machine learning."
- michaelbarton 9y agoThat's great. Thank you for following up so quickly. Could you point me to where I could read about this, or see an example? I took a quick search on the documentation and I could see anything on a cursory pass: http://docs.aws.amazon.com/search/doc-search.html?searchPath=documentation-guide&searchQuery=HyperParameterOptimization http://docs.aws.amazon.com/search/doc-search.html?searchPath...
- tomfaulhaber 9y agoSorry, for the very delayed reply here. The hyperparameter optimization feature is still in preview (though the rest of SageMaker is in general availability). We'll be putting up a page within the next week or so for you to request access.