At this time, I’m extraordinarily comfortable to announce Amazon SageMaker Feature Store, a brand new functionality of Amazon SageMaker that makes it simple for knowledge scientists and machine studying engineers to securely retailer, uncover and share curated knowledge utilized in coaching and prediction workflows.

For all of the significance of choosing the fitting algorithm to coach machine studying (ML) fashions, skilled practitioners know the way essential it’s to feed it with high-quality knowledge. Cleansing knowledge is an effective first step, and ML workflows routinely embrace steps to fill lacking values, take away outliers, and so forth. Then, they typically transfer on to remodeling knowledge, utilizing a mixture of widespread and arcane methods often called “feature engineering.”

Merely put, the aim of characteristic engineering is to remodel your knowledge and to extend its expressiveness in order that the algorithm could study higher. As an example, many columnar datasets embrace strings, akin to road addresses. To most ML algorithms, strings are meaningless, they usually have to be encoded in a numerical illustration. Thus, you would substitute road addresses with GPS coordinates, a way more expressive technique to study the idea of location. In different phrases, if knowledge is the brand new oil, then characteristic engineering is the refining course of that turns it into high-octane jet gasoline that helps fashions get to stratospheric accuracy.

Certainly, ML practitioners spend loads of time crafting characteristic engineering code, making use of it to their preliminary datasets, coaching fashions on the engineered datasets, and evaluating mannequin accuracy. Given the experimental nature of this work, even the smallest venture will result in a number of iterations. The identical characteristic engineering code is usually run time and again, losing time and compute sources on repeating the identical operations. In giant organizations, this will trigger a fair larger lack of productiveness, as totally different groups typically run an identical jobs, and even write duplicate characteristic engineering code as a result of they haven’t any information of prior work.

There’s one other laborious drawback that ML groups have to unravel. As fashions are educated on engineered datasets, it’s crucial to use the identical transformations to knowledge despatched for prediction. This typically means rewriting characteristic engineering code, typically in a unique language, integrating it in your prediction workflow, and working it at prediction time. This complete course of shouldn’t be solely time-consuming, it may well additionally introduce inconsistencies, as even the tiniest variation in an information remodel can have a big impression on predictions.

With a purpose to clear up these issues, ML groups typically construct a characteristic retailer, a central repository the place they’ll preserve and retrieve engineered knowledge used of their coaching and predictions jobs. As helpful as characteristic shops are, constructing and managing your personal includes loads of engineering, infrastructure, and operational effort that takes worthwhile time away from precise ML work. Prospects requested us for a greater resolution, and we set to work.

Introducing Amazon SageMaker Characteristic Retailer
Amazon SageMaker Feature Store is a totally managed centralized repository on your ML options, making it simple to securely retailer and retrieve options with out having to handle any infrastructure. It’s a part of Amazon SageMaker, our totally managed service for ML, and helps all algorithms. It’s additionally built-in with Amazon SageMaker Studio, our web-based improvement setting for ML.

Options saved in SageMaker Feature Store are organized in teams, and tagged with metadata. Due to this, you possibly can rapidly uncover which options can be found, and whether or not they’re appropriate on your fashions. A number of groups also can simply share and re-use options, decreasing the price of improvement and accelerating innovation.

As soon as saved, options might be retrieved and utilized in your SageMaker workflows: mannequin coaching, batch remodel, and real-time prediction with low latency. Not solely do you keep away from duplicating work, you additionally construct constant workflows that use the identical constant options saved within the offline and on-line shops.

The Climate Corporation (Local weather) is a subsidiary of Bayer, and the business chief in bringing digital innovation to farmers. Says Daniel McCaffrey, Vice President, Knowledge and Analytics, Local weather: “At Climate, we believe in providing the world’s farmers with accurate information to make data driven decisions and maximize their return on every acre. To achieve this, we have invested in technologies such as machine learning tools to build models using measurable entities known as features, such as yield for a grower’s field. With Amazon SageMaker Feature Store, we can accelerate the development of ML models with a central feature store to access and reuse features across multiple teams easily. SageMaker Feature Store makes it easy to access features in real-time using the online store, or run features on a schedule using the offline store for different use cases, and we can develop ML models faster.”

Care.com, the world’s main platform for locating and managing high-quality household care, can be utilizing Amazon SageMaker Feature Store. That is what Clemens Tummeltshammer, Knowledge Science Supervisor, Care.com, informed us: “A strong care industry where supply matches demand is essential for economic growth from the individual family up to the nation’s GDP. We’re excited about Amazon SageMaker Feature Store and Amazon SageMaker Pipelines , as we believe they will help us scale better across our data science and development teams, by using a consistent set of curated data that we can use to build scalable end-to-end machine learning model pipelines from data preparation to deployment. With the newly announced capabilities of Amazon SageMaker, we can accelerate development and deployment of our ML models for different applications, helping our customers make better informed decisions through faster real-time recommendations.

Now, let’s see how one can get began.

Storing and Retrieving Options with Amazon SageMaker Characteristic Retailer
When you’ve run your characteristic engineering code in your knowledge, you possibly can set up and retailer your engineered options in SageMaker Feature Store, by grouping them in characteristic teams. A characteristic group is a set of information, much like rows in a desk. Every report has a singular identifier, and holds the engineered characteristic values for one of many knowledge situations in your authentic knowledge supply. Optionally, you possibly can select to encrypt the information at relaxation utilizing your personal AWS Key Management Service (KMS) key that’s distinctive for every characteristic group.

The way you outline characteristic teams is as much as you. For instance, you would create one per knowledge supply (CSV information, database tables, and so forth), and use a handy distinctive column because the report identifier (main key, buyer id, transaction id, and so forth).

When you’ve received your teams discovered, you must repeat the next steps for every group:

  1. Create characteristic definitions, with the title and the kind of every characteristic in a report (Fractional, Integral, or String).
  2. Create every characteristic group with the create_feature_group() API:
    sm_feature_store.create_feature_group(
         # The title of the characteristic group
         FeatureGroupName=my_feature_group_name,
         # The title of the column appearing because the report identifier
         RecordIdentifierName=record_identifier_name,
         # The title of the column motion because the characteristic timestamp
         EventTimeFeatureName = event_time_feature_name,
         # A listing of characteristic names and kinds
         FeatureDefinitions=my_feature_definitions,
         # The S3 location for the offline characteristic retailer
         OnlineStoreConfig=online_store_config,
         # Optionally, allow the web characteristic retailer
         OfflineStoreConfig=offline_store_config,
         # An IAM function
         RoleArn=function
    )
  3. In every characteristic group, retailer information containing a set of characteristic title/characteristic worth pairs, utilizing the put_record() API:
    sm_feature_store.put_record(
       FeatureGroupName=feature_group_name,
       Document=report,
       EventTime=event_time
    )

    For sooner ingestion, you would create a number of threads and parallelize this operation.

At this level, options shall be obtainable in Amazon SageMaker Feature Store. Due to the offline retailer, you need to use companies akin to Amazon Athena, AWS Glue, or Amazon EMR to construct datasets for coaching: fetch the corresponding JSON objects in S3, choose the options that you simply want, and save them in S3 within the format anticipated by your ML algorithm. From then on, it’s SageMaker enterprise as regular!

As well as, you need to use the get_record() API to entry particular person information saved within the on-line retailer, passing the group title and the distinctive identifier of the report you need to entry, like so:

report = sm_feature_store.get_record(
    FeatureGroupName=my_feature_group_name,
    RecordIdentifierValue="IntegralValue": 5962
)

Amazon SageMaker Feature Store is designed for quick and environment friendly entry for actual time inference, with a P95 latency decrease than 10ms for a 15-kilobyte payload. This makes it doable to question for engineered options at prediction time, and to switch uncooked options despatched by the upstream software with the very same options used to coach the mannequin. Characteristic inconsistencies are eradicated by design, letting you deal with constructing the most effective fashions as a substitute of chasing bugs.

Lastly, as SageMaker Feature Store consists of characteristic creation timestamps, you possibly can retrieve the state of your options at a selected cut-off date.

As Amazon SageMaker Feature Store is built-in with SageMaker Studio, I can see my two options teams there.

SageMaker screenshot

Proper-clicking on “Open feature group detail”, I open the identification characteristic group.

SageMaker screenshot

I can see characteristic definitions.

SageMaker screenshot

Lastly, I can generate queries for the offline retailer, which I may add to a Amazon SageMaker Data Wrangler workflow to load options previous to coaching.

SageMaker screenshot

Tips on how to Get Began with Amazon SageMaker Characteristic Retailer
As you possibly can see, SageMaker Feature Store makes it simple to retailer, retrieve, and share options required by your coaching and prediction workflows.

SageMaker Characteristic Retailer is accessible in all areas the place SageMaker is accessible. Pricing is predicated on characteristic reads and writes, and on the full quantity of knowledge saved.

Listed below are sample notebooks that can aid you get began straight away. Give them a try, and tell us what you assume. We’re at all times trying ahead to your suggestions, both by way of your regular AWS help contacts, or on the AWS Forum for SageMaker.

– Julien



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