Python has many fans in Artificial Intelligence and Data Science communities, but is it ready for enterprise deployment?
Python is de facto the language of choice when it comes to AI, ML, and data science, with a large volume of high-quality, professional open-source modules available covering a wide range of AI, ML, and data science use cases.
For example, there are great AI solutions for product tagging and automatic recommendations, such as "you may like these accessories to go with your purchase", that deliver highly relevant suggestions that achieve much better results than the simplistic "you purchased this fridge; you might also be interested in these fridges".
Another striking example is helping online customers to find products by simply uploading a picture, and taking them direct to a product showcase - with available accessories - in the store, to provide a great customer experience and drive higher sales.
As desirable as these potential benefits of AI are, when it comes to integrating AI in an enterprise environment there are major challenges:
Challenge #1: Long Development Timelines
Integration of complex Python-based AI applications into enterprise environments requires custom development taking months or longer. The āAPI-ificationā of your AI application is challenging to do in a scalable way in terms of reliability, transaction fault-tolerance, security and GDPR compliance.
Challenge #2: Data Connectivity
Data needs to be pulled from many different systems and technologies, a challenge that must be solved in order for the AI solution to be useful.
Challenge #3: Python Integration With Enterprise Technologies
Interacting with existing Java-based enterprise technologies and systems from the very different world of Python-based AI solutions is a significant technical hurdle.
Overcoming these challenges require solutions that provide simple ways to access enterprise data, wherever and however it is stored; connect Python-based AI and data science solutions with enterprise Java applications in a secure and scalable way; integrate with process orchestration and automation initiatives; and support modern DevOps with iterative, continuous delivery enabling rapid time to market.
How Qorus brings AI to the Enterprise
Qorus Integration EngineĀ® is a platform for connecting different systems and technologies into robust business processes with automatic error detection and recovery. Qorus enables you to:
Connect to any data source, silos in any format, and event streams from operations
Host in a microservices architecture that directly supports standard Python and enables it to seamlessly integrate with enterprise technologies.
And because the Python application is embedded in a microservice it scales easily, handling many simultaneous requests despite the single-threaded nature of CPython.
Integrate Java applications directly with Python data science solutions and AI pipelines using configuration-based enterprise integration and AI building blocks in a single solution.
Generate AI-derived insights from your connected data and technologies.
Comply with data protection and privacy regulations including GDPR and CCPA. Simply define which data is to be used by application, create data references without passing personal data, and delete data immediately after use.
Connect your business with AI
Connecting new AI and Data Science capabilities seamlessly with your enterprise IT and business applications unlocks new ways to deliver customer experience initiatives, process improvements and innovations for your business.
Contact us today to see how we can help you harness the power of AI and Data Science to transform your business.
