IBM Watson

AI is widely discussed and increasingly feasible because abundant computing power and cloud infrastructure make it easier to run machine learning at scale, but infrastructure access alone does not remove key barriers. High upfront costs, a shortage of qualified programmers, data scientists, and ML experts, and the likelihood that early AI proofs-of-concept (PoCs) will fail keep many enterprises from building in-house solutions. Competition for scarce talent further slows internal capability building, leaving many organizations without the expertise needed to customize, implement, and operate AI effectively.

AI-on-demand platforms address these constraints by providing accessible, high-level services that support multiple stages of the AI development and implementation pipeline. They offer user interfaces that reduce the need for deep ML and coding expertise, plus resources and training to help programmers integrate AI into existing systems. Delivered largely via cloud and designed to run across on-premises and public clouds, these platforms also help manage large datasets through secure storage, labeling, governance, and location-independent access. They commonly bundle “finished” capabilities—natural language, computer vision, decision support, predictive analytics, and often robotic process automation—and more mature offerings allow combination and customization.

IBM Watson is positioned as a comprehensive portfolio for building, deploying, and managing AI end-to-end, aligned to IBM’s “AI Ladder”: collect and access data; organize it through integration, cleansing, classification, and governance; perform advanced analysis by building and deploying models; then responsibly infuse AI into operations with ongoing accuracy adjustments and explainability for trust. Watson runs in multicloud environments via IBM Cloud Pak for Data on Red Hat OpenShift, with components including Watson Knowledge Catalog, Watson Studio, Watson Machine Learning, and Watson OpenScale for monitoring, bias and drift detection, explainability, and outcome alignment. Strengths include breadth, governance, and deployment flexibility; challenges include product interdependencies, manual external database integration, and potentially steep pricing for smaller organizations.

• Cloud computing enables scalable enterprise AI proofs-of-concept through access to CPU, GPU, and FPGA computation options.
• AI implementations typically require large datasets for training and for ongoing inputs during operational use.
• IBM’s AI Ladder progresses from collecting and accessing data, to organizing it, to advanced analysis, to responsibly infusing AI into operations.
• IBM Watson offerings run in multicloud environments and are compatible with AWS, Microsoft Azure, Google Cloud, and others.
• IBM Cloud Pak for Data is microservices-enabled and containerized, running on Red Hat OpenShift.
• Watson Knowledge Catalog supports data and metadata upload and storage policy needs before AI model design.
• Watson Studio supports tools such as Jupyter notebooks and Cognos Dashboards for data work and visualization.
• Watson Studio supports popular ML frameworks including TensorFlow and PyTorch.
• Watson Machine Learning packages training run results into Kubernetes containers with defined GPUs for projects.
• Watson OpenScale includes bias detection, model drift detection, and explainability capabilities.

• “Accessibility to infrastructure is not the only prerequisite to implementing AI solutions.”
• “Professionals cannot be trained fast enough, and the competition is fierce between competitors to attract the limited number of those already trained.”
• “AI-on-demand platforms provide a workaround for enterprises by increasing accessibility to high-level AI services.”
• “Cloud computing allows for the scalability of enterprise AI proofs-of-concept.”
• “The final stage on the AI Ladder is to infuse AI into the organization’s operations responsibly.”
• “This includes continually adjusting the model so that it continues to be accurate in a changing context, or to meaningfully pursue explainability of ML models to maintain trust and transparency.”
• “Scoring is the terminology used for passing data through a model to generate a prediction.”
• “Once the model is deployed, it can be run in other applications and results retrieved with API calls.”
• “There are three major oversight goals that can be accomplished with Watson OpenScale: monitoring for compliance, ensuring that models are resilient to changing situations, and aligning model performance with business outcomes.”
• “The pricing scheme and necessity to purchase multiple products are steep and may be prohibitive for smaller organizations.”