IBM Watsonx.ai : The AI powerhouse with untapped potential
September 27, 2024
IBM Watsonx.ai : The AI powerhouse with untapped potential
Score 8 out of 10
Vetted Review
Overall Satisfaction with IBM watsonx.ai
IBM Maximo Asset management platform is going through a radical change in terms of technical as well as functional architecture. Due to a technological platform change, the upgrade timeline for application has changed significantly. While doing the upgrade, we are also trying to ensure that our legacy codes built over last 15-20 years are also removed or retrofitted so that operational expenditure to manage the app reduces.
In order to address these challenges, we are building technical accelerator on WatsonX platform that contextualizes the code conversion from Java to Python in IBM Maximo specific libraries, reviews the code and recommends the improvements, reviews the code and identifies if the similar set of functionalities is delivered by Maximo in the latest version.
In our recent POC, we found out that the model requires a little training and it started giving outputs with 80-82% accuracy. We only spent 10-12 hours in training and we are absolutely happy with the accuracy. The target audience (developers) can significantly benefit from this tool as it is going to reduce at least 40% of cycle time in reducing the system complexities and address any issues that comes up due to legacy code in older version of Maximo.
In order to address these challenges, we are building technical accelerator on WatsonX platform that contextualizes the code conversion from Java to Python in IBM Maximo specific libraries, reviews the code and recommends the improvements, reviews the code and identifies if the similar set of functionalities is delivered by Maximo in the latest version.
In our recent POC, we found out that the model requires a little training and it started giving outputs with 80-82% accuracy. We only spent 10-12 hours in training and we are absolutely happy with the accuracy. The target audience (developers) can significantly benefit from this tool as it is going to reduce at least 40% of cycle time in reducing the system complexities and address any issues that comes up due to legacy code in older version of Maximo.
Pros
- Code Conversion
- Code Review
- Code Generation
- L1 queries on the product features
Cons
- Should allow flexibility to handle unstructured data in a more robust data labeling and annotation tools
- real-time collaboration features are minimal which causes development less efficient.
- There is no clarity around automated model monitoring and retraining features
- Lack of pre-built industry specific models requires more customization effort for certain use cases
- Seamless ingestion into IBM suite of products improved the end user productivity and helped users in insights-backed decision making
- The technical interfacing with commercially off the shelf products helps in reducing the overall cycle time for implementation and upgrades.
- The cost of the licensing could be more competitive.
The use cases of code explanation, code suggestion, code review, and code conversions from one language to another were relatively easy to build in Watson.ai than using CoPilot. I found that the contextualization of code for a packaged solution is easier to do in Watsonx.ai platform during my initial research.
Do you think IBM watsonx.ai delivers good value for the price?
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Are you happy with IBM watsonx.ai's feature set?
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Did IBM watsonx.ai live up to sales and marketing promises?
Yes
Did implementation of IBM watsonx.ai go as expected?
Yes
Would you buy IBM watsonx.ai again?
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