Operationalizing Data Science using Cloud Foundry
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This presentations walks through how the joint solution between Alpine’s Chorus Platform and Pivotal's Cloud Foundry closes the gap between data science insights and business value
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Next DSS NYC Event 👉 https://datascience.salon/newyork/
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Reducing the gap between R&D and production is still a challenge for data science/ machine learning engineering groups in many companies. Typically, data scientists develop the data-driven models in a research-oriented programming environment (such as R and python). Next, the data/machine learning engineers rewrite the code (typically in another programming language) in a way that is easy to integrate with production services.
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Find out how during our Automate Studio training on March 28 – led by Sigrid Kok, Principal Sales Engineer, and Isra Azam, Sales Engineer, at Precisely.
This session’s for you if you want to discover the best approaches for creating, extending or maintaining different types of materials, as well as automating the tricky parts of these processes that slow you down.
Greater control over your Automate Studio business processes means bigger, better results. We’ll show you how to enable your business users to interact with SAP from Microsoft Office and other familiar platforms – resulting in more efficient SAP data management, along with improved data integrity and accuracy.
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real world approaches for creating multiple types of materials, balancing flexibility and power with simplicity and ease of use
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downloading the generated material number
using formulas to format prior to upload, such as capitalization or zero padding to make it easy to get the data right the first time
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tips on modifying alternate units of measure, building from scratch using GUI scripting
modify multiple language descriptions, build from scratch using a standard BAPI
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Through these topics, you’ll gain plenty of actionable takeaways that you can start implementing right away – including how to:
improve your data integrity and accuracy
make scripts flexible and usable for automation users
seamlessly handle both simple and complex parts of material master
interact with SAP from both business user and script developers’ perspectives
easily upload and download data between SAP and Excel – and how to format the data before upload using simple formulas
You’ll leave this session feeling ready and empowered to save time, boost efficiency, and change the way you work.
Automate Studio reduces your dependency on technical resources to help you create automation scenarios – and our team of experts is here to make sure you get the most out of our solution throughout the journey.
Questions? Sigrid & Isra will be ready to answer them during a live Q&A at the end of the session.
Who should attend:
Attendees who will get the most out of this session are Automate Studio developers and runners familiar with SAP MM. Knowledge of Automate Studio script creation is nice to have, but not required.
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- 1. Operationalizing Data Science using Cloud Foundry Alpine Data Lawrence Spracklen VPE
- 3. 3 Operationalization • What happens after the models are created? • How does the business benefit from the insights? • Operationalization is frequently the weak link – Operationalizing PowerPoint? – Hand rolled scoring flows?
- 4. 4 Barriers to Model Ops • Scoring often performed on a different data source to training • Batch training versus RT/stream scoring • How frequently are models updated? • How is performance monitored?
- 5. 5 Define Act Transform Deploy Model Business Leader Employees and Customers Chorus 6 The Chorus Process
- 7. 7 Pivotal BDS • Provides support for high-performance SQL on both Hadoop and traditional data warehouses – HDB/HAWQ and GreenPlum • Alpine supports SQL & MADlib accelerated machine learning algorithms on both HAWQ and GPDB • Alpine models trained on HAWQ can be scored on GPDB and vice versa
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- 10. 10 Turn-key model updates Conditionally push model to Cloud Foundary Scoring engine
- 11. 11 Turnkey Model Ops 1) Launch CF scoring engine 2) Configure export 3) Score data Curl –X POST …
- 12. 12 PFA • Portable Format for analytics is the JSON- based successor to PMML – Version 0.8.1 available • Significant flexibility in encapsulating complex data pre- and post-processing
- 13. 13 Complex scoring flows Push entire flow to Alpine Scoring Engine Trained Model Pre-processing Scoring Flow
- 14. 14 PFA Support • Not only model operators need to export PFA • Process entire DAG from raw data input to final model output – Synthetize PFA doc to represent the flow • PFA is capable of representing many key operations – Much richer than PMML • Provides support for supplemental info to be leveraged by the scoring flows
- 15. 15 Conclusions • Operationalization of Data Science findings often overlooked • Need easy model deployment to ensure maximum impact • PFA makes it much simpler to deploy complex scoring flows • Pivotal + Alpine Chorus provide turn-key model operationalization support
- 18. 18 External interaction Streaming RESTfulBatch Hadoop DB Chorus Alpine IR JDBC PFA/SQL/PMML Internal External