Dell Moving AI solutions from concept into production User Manual
Moving AI solutions from concept into production
Follow a proven path to the development and deployment of artificial intelligence applications that solve business problems.
ABSTRACT
In this white paper, we walk through five steps on the path to a successful artificial intelligence project, from developing a clearly defined use case to scaling a solution in a production environment. In addition, we explore a case study highlighting the experiences of a global enterprise that worked with the Dell Technologies HPC & AI Innovation Lab to develop a proof of concept for an AI solution that stops credit card fraud in real time.
Armando Acosta, Product Management
September 2020
DELL TECHNOLOGIES WHITE PAPER
TABLE OF CONTENTS
A STRATEGIC APPROACH TO AI PROJECTS |
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FIVE STEPS TO A SUCCESSFUL AI PROJECT |
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Step 1. Clearly define the use case |
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Step 2. Clean, tag, transform and catalog data |
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Step 3. Understand your data science skillset and needs |
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plus train your model |
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Step 4. Use DevOps for the last mile |
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Step 5. Scale to production |
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A CUSTOMER CASE STUDY |
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Use case . . . . . . . . . . . . . . . . . |
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Data |
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Data science . . . . . . . . . . . . . . . . |
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DevOps . . . . . . . . . . . . . . . . . . |
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Scale . . . . . . . . . . . . . . . . . . |
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KEY TAKEAWAYS |
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TO LEARN MORE |
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Published in the USA 9/20.
DELL TECHNOLOGIES WHITE PAPER
A STRATEGIC APPROACH TO AI PROJECTS
The process of developing and deploying artificial intelligence applications brings unique challenges. Complexity runs through the entire project, from defining use cases and preparing data to training algorithms, developing software code for last mile execution and scaling the solution in a production environment.
These are all familiar challenges to the data scientists, computer scientists and subject matter experts at Dell Technologies. The HPC & AI Innovation Lab team regularly works with enterprises, universities and public entities on the development of artificial intelligence algorithms and the high performance computing systems that run them.
As part of its mission, the team works proactively to share its learnings and best practices with the global user community. One of these overarching lessons is that organizations on the path to AI need to think strategically, across the entire lifecycle of the project, and then move down the development and deployment path in a stepwise manner.
With that thought in mind, we suggest these five steps toward a successful AI project:
1.Clearly define the use case
2.Clean, tag, transform and catalog data
3.Understand your data science skillset and needs plus train your model
4.Use DevOps for the last mile
5.Scale to production
In this white paper, we walk through each of these steps, and then explore the case study an AI project launched by a global enterprise. This organization worked with the HPC
& AI Innovation Lab to develop a proof of concept that proved the business value of an ambitious effort to use AI to fight credit card fraud in real time.
Figure 1. rDE deployed as data proxy for Cassandra databases – conceptual architecture
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