Mira Cole
Episode nine. Two official objectives, one episode. Implement A.I. solutions for text and speech, and implement computer vision and image-generation capabilities, both by using Foundry.
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Episode 09 · Implement with Foundry
Text, speech, and vision apps
The skills list splits these across two objectives. Mira and Owen put them on one workbench so you can match a scenario to the right Foundry path.
- Text analysis application
- Spoken prompts with multimodal models
- Azure Speech in Foundry Tools
- Visual input and image generation
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Transcript
On the mics
Owen Park
Five verbs on the skills list. Build a lightweight app that includes text analysis. Respond to spoken prompts using a deployed multimodal model. Build a lightweight app with Azure Speech in Foundry Tools. Interpret visual input in prompts with a multimodal model. Create new visual outputs with generative models. Plus a lightweight vision app.
Mira Cole
Text analysis app is episode five's techniques, now as a call. You send text, you get sentiment, entities, key phrases, or a summary back. In Foundry that might be a language model with a strict system prompt, or a Foundry Tool purpose-built for language. The exam cares that you know the call returns analysis, not a poem, unless you asked for a poem.
Owen Park
Spoken prompts into a multimodal model. This is the new-feeling one. You do not always have to transcribe first. You deploy a model that accepts audio, the user talks, the model answers. Hands-free, kiosk, shop floor. The prompt can mix speech and text: 'Looking at this, what should I tighten?' while holding a part.
Mira Cole
Azure Speech in Foundry Tools is the dedicated path when you need a transcript, a custom voice, real-time recognition, or synthesis as its own pipeline. Multimodal chat is convenient. Speech is the specialist. If the scenario is 'we need a highly accurate transcript of a legal deposition,' pick Speech, not a casual voice prompt into a general model.
Owen Park
Visual input in a prompt is the photo plus question pattern. Deploy a multimodal model, attach the image, ask. A lightweight vision app is that loop in code: load image, send with the user message, print the answer. Same project client family you already know.
Mira Cole
Creating new visual outputs is image generation. Different model, different arrow. Prompt in, image file out. Do not send a photo of a cracked pipe to an image generator and expect a diagnosis. Send it to a multimodal understander. Use the generator when marketing wants five variations of a bike.
Owen Park
A mapping you can screenshot in your head. Analyze existing text: text analysis app. Talk to the model: multimodal speech, or Speech service. Hear the model: Speech synthesis. See an image: multimodal vision. Paint an image: generative image model.
Mira Cole
Question one. A factory wants workers to ask questions out loud without typing, using a model already deployed in Foundry. Which path matches the skills list most directly?
Owen Park
Respond to spoken prompts using a deployed multimodal model. That is almost a copy-paste of the objective.
Mira Cole
Question two. Legal needs a timestamped transcript of a six-hour hearing. Best Foundry-aligned choice?
Owen Park
Azure Speech in Foundry Tools, recognition, not a casual multimodal chat with no transcript requirements.
Mira Cole
Question three. An app should return sentiment and entities for each support email. What are you building?
Owen Park
A lightweight text analysis application. Not an agent unless they also asked it to file a ticket.
Mira Cole
Question four. A technician photographs a serial plate and asks what model it is. Which capability?
Owen Park
Interpret visual input in a prompt with a multimodal model. O.C.R. is happening inside that, but the Foundry implementation bullet is visual input in the prompt.
Mira Cole
Question five. Marketing wants four original hero images from a style description, no source photo. Which capability?
Owen Park
Create new visual outputs using a generative image model. If they had a source photo to edit, you might still generate, but you would not pick a speech service.
Mira Cole
Last teaching episode is Content Understanding, then a mixed exam-day set. If you remember one service name from this season, make it Azure Content Understanding in Foundry Tools.
Owen Park
Documents, images, audio, video. Schema in, fields out. See you in ten.