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Tool-using AI applications promise to connect natural-language requests with real business workflows. That promise is useful only when the boundaries are engineered carefully. A model […]
Read articleMany machine-learning projects spend more time correcting features than selecting models. A model may learn a powerful relationship from training data that will never be […]
Read articleMLOps is not a job title for the person who knows how to run a training pipeline. It is an operating model for repeatedly changing […]
Read articleTraining produces a model artifact; users experience an application. The engineering space between those facts is where deployment and monitoring failures happen. For readers working […]
Read articleTraining a model is more than starting a compute job and waiting for a success message. A production-grade training design controls data selection, evaluation, reproducibility, […]
Read articleGenerative AI strategy begins with a business process, not a model announcement. Leaders often see convincing demonstrations and assume productivity improvements will automatically appear when […]
Read articleThe practical challenge in Google’s AI portfolio is not memorizing every product name. It is recognizing whether a business needs employee productivity, source-grounded research, customized […]
Read articleGenerative AI can produce fluent language, code, images and other content without guaranteeing that the result is accurate, authorized, or fit for a business decision. […]
Read articleA machine-learning model can pass offline validation and still behave badly in production because the features used during training do not mean the same thing […]
Read articleA data warehouse query can be elegant and expensive at the same time. An analyst asks for orders placed in one week, but the job […]
Read articlePrivate connectivity is often introduced with a reassuring diagram: two services exchange data without sending traffic over the public internet. That statement is useful but […]
Read articleA company stores sensitive customer files in Amazon S3. It assumes AWS will prevent inappropriate disclosure because the service is managed. An employee then makes […]
Read articleA startup wants to launch an image-sharing application. One developer proposes a large virtual machine to run the web server, database, and image storage together. […]
Read articleA growing online retailer replaces an aging server fleet with services in AWS. Its leadership expects immediate savings because there is no longer a large […]
Read articleAn order service has to notify accounting, trigger shipment, update customer-facing status, and preserve an auditable record. Its first implementation calls all four destinations synchronously. […]
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