Tag: IBM watsonx
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IBM watsonx vs the Field, the Cost Recap and the Verdict (IBM Gen AI Series, Part 24)
The final part of the IBM watsonx series puts the platform against Bedrock, Azure, and Vertex on the axes that decide a regulated purchase, recaps what a governed deployment really costs, and gives a clear verdict on where watsonx wins and where to walk away.
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watsonx Reference Architectures for RAG, Agentic, and Regulated Workloads (IBM Gen AI Series, Part 23)
The three watsonx reference architectures that recur in real builds: enterprise RAG on watsonx.data, agentic systems on watsonx Orchestrate, and a watsonx.governance overlay for regulated work. Which to build first, what each costs, and where they break.
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watsonx LLMOps on OpenShift, from Notebook to Promotion Gate (IBM Gen AI Series, Part 22)
How to run generative AI features in production on the IBM stack: watsonx.ai for models and governance, Red Hat OpenShift AI for pipelines and GPUs, and a promotion gate that refuses to ship a regression.
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EU AI Act Readiness and Responsible AI on watsonx (IBM Gen AI Series, Part 21)
The 2026 Digital Omnibus pushed high-risk deadlines to December 2027, but GPAI and transparency duties still bite this year. Here is how I classify a watsonx system and build the evidence pack before an auditor asks.
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watsonx Cost Governance and FinOps, from Resource Units to a Real Budget (IBM Gen AI Series, Part 20)
watsonx charges in Resource Units, capacity unit hours, and a flat instance fee. Here is how those meters add up, and the plan choice that keeps your generative AI bill honest.
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watsonx.governance Risk Monitoring and AI Factsheets (IBM Gen AI Series, Part 19)
How watsonx.governance tracks models, logs AI factsheets, monitors drift and bias in production, and maps controls to the EU AI Act. Part 19 of the IBM watsonx series.
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Docling and Multimodal Document Understanding on watsonx (IBM Gen AI Series, Part 18)
How IBM Docling and the Granite vision models turn PDFs, scans, and DOCX files into clean structured chunks for RAG on watsonx, and where a vision model is worth the cost.
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watsonx Model Evaluation and Benchmarking (IBM Gen AI Series, Part 17)
How to evaluate and benchmark models and prompts on IBM watsonx using watsonx.governance, Evaluation Studio, and the ibm-watsonx-gov SDK. Score faithfulness, answer relevance, and context relevance on your own golden set, not a public leaderboard.
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watsonx Assistant, Conversational Applications End to End (IBM Gen AI Series, Part 16)
How to build a conversational application on IBM watsonx Assistant, from a first action and built-in conversational search to routing hard requests to the agent from Part 15, with the channel and cost trade-offs an architect actually weighs.
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Building Agents on watsonx Orchestrate with the ADK (IBM Gen AI Series, Part 15)
How to build a native agent on watsonx Orchestrate with the ADK, wire its tools, collaborators, and knowledge, pick the right agent style, and keep multi-agent routing from wrecking your bill.
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watsonx.ai Training Infrastructure and Scaling on OpenShift (IBM Gen AI Series, Part 14)
Every watsonx tune is a Kubernetes job under the hood. Here is how GPU nodes, the NVIDIA operators, autoscaling to zero, MIG limits, and Kueue quotas fit together on Red Hat OpenShift.
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Data Prep and Synthetic Data on watsonx.data (IBM Gen AI Series, Part 13)
How to land, clean, and curate real data in the watsonx.data lakehouse, then use the Synthetic Data Generator on watsonx.ai to fill the gaps before a tune. With a worked cost example and a runnable cleaning script.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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