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VENDORiQ: IBM Watsonx and Predictions for Open Source AI

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IBM Watson was the first functional AI to gain broad public awareness. Unfortunately, implementing Watson required significant effort to first clean and format organisational data, and then train the AI model. IBM has now launched IBM watsonx, which alleviates much of the training challenges. IBRS looks into how enterprises can benefit from the surge in AI development and integrations in enterprise platforms.

The Latest

6 June 2023: IBM announced IBM watsonx, a new AI and data platform designed to help enterprises scale and accelerate the impact of advanced AI using trusted data. IBM recognises that organisations adopting AI require a comprehensive technology stack that allows them to train, fine-tune, and deploy AI models across the entire workforce. Three products will be launched in the middle of 2023 namely, IBM watsonx.ai, which offers features such as an AI development studio with access to pre-trained foundation models and open source models; IBM watsonx.data, which is a fit-for-purpose data store built on open lakehouse architecture so users can access data in a single point of entry with built-in governance tools; and IBM watsonx.governance to offer an AI governance toolkit to ensure consumer privacy, identify model bias, and allow regulatory compliance.

With watsonx, organisations can leverage IBM’s curated and trained foundation models, which are developed with security considerations in mind. This can provide a level of assurance regarding the security and reliability of the AI models used within the organisation. The platform’s library of thousands of large-scale AI models, trained on various datasets such as language, geospatial data, IT events, and code, adds value by offering pre-trained models that companies can leverage for their specific use cases. This library provides a valuable resource for organisations, saving them time and effort in training models from scratch and allowing them to benefit from IBM’s expertise in AI model development.

Why It’s Important

IBM was a pioneer of AI with the introduction of Watson. However, Watson’s uptake was hindered by the need to meticulously prepare its training data – that is, the information assets provided to the AI so it could respond accurately and with the organistion’s priority knowledge. Preparing data proved difficult, requiring specialised skills that were difficult to find and consulting services. This made Watson projects both expensive and potentially risky, since organisations sometimes found their information assets were not as complete or of the quality needed to train Watson. Watsonx addresses this challenge. 

IBRS expects similar tools as watsonx to enter the market, many expanding on the notion of MLOps (see related IBRS advisory, below). Microsoft will build out its Azure environment, along with its Copilot efforts. Oracle will no doubt make its own announcements. 

However, the real action will be in the open source world.

IBRS expects that open source development tools, code libraries and frameworks will emerge and dominate the custom AI development landscape. These tools will wrap complex issues, such as converting unstructured data into open source vector databases, semantic search, and large language models, etc.

Such tools are emerging and evolving rapidly, most of which are offered as Python open source projects. 

Who’s Impacted

  • AI development teams
  • Software development teams

What’s Next?

  • Ensure that AI models and data usage can maintain regulatory compliance, data privacy, and ethical AI practices.
  • Conduct a maturity assessment of your IT strategy to see where AI will add value by referring to the Analytics Artificial Intelligence Maturity Model.

Related IBRS Advisory

  1. Machine Learning Operations (MLOps), The AI Productivity Fast Track
  2. How Can AI Reimagine Your Business Processes?
  3. DIY Or Ready-Made? Choose Your AI Adoption Path Carefully
  4. Options For Machine Learning-As-A-Service: The Big Four AIs Battle It Out

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