{
"title": "Production-Grade LLM Development in Python Learning Path",
"fullDescription": "You invoke off-the-shelf LLMs in Python, yet they hallucinate, bog down, or miss your domain’s nuances. You need a repeatable, code-first way to control, customize, and scale these models.\n\nThis path guides developers through structured prompting pipelines, advanced prompt engineering, and model fine-tuning on proprietary data. Learners master quantization to accelerate performance, integrate quantitative and qualitative metrics to evaluate accuracy and coherence, and build deployment workflows that automate retraining and monitoring. Hands-on modules also cover image-to-text and text-extraction tasks. By the end, your organization will deliver domain-aligned, high-performance LLM services ready for production.",
"outcomes": [
"Develop and catalog standardized prompts to produce consistent, context-aware responses",
"Customize LLMs via fine-tuning to reduce hallucinations and align outputs with business data",
"Accelerate inference speed by applying quantization and model-compression techniques",
"Evaluate model quality with appropriate metrics and methods for accuracy, coherence, and task performance",
"Deploy and monitor LLM services in automated pipelines that ensure reliability and scalability",
"Integrate LLMs for multimodal tasks such as image-to-text conversion and structured data extraction"
],
"audience": {
"description": "This path is designed for anyone building, customizing, or operating LLM solutions in Python and the teams that support those efforts.",
"recommendedFor": [
"Python developers and AI engineers implementing LLM integrations",
"Data scientists fine-tuning LLMs for domain-specific tasks",
"MLOps and DevOps professionals automating deployment and monitoring for AI applications"
],
"alsoStrongFitFor": [
"Software architects designing scalable AI-driven applications",
"Product managers defining LLM-powered features and requirements",
"Technical leads overseeing AI performance, cost, and reliability"
]
},
"summary": "By completing this path, learners will collaborate effectively across functions and deliver production-ready LLM services that are accurate, efficient, and resilient.",
"coursesIncluded": [
{
"courseTitle": "Getting Started with Multimodal LLMs in Python",
"courseDescription": "Core prompts, quick-start integrations, and environment setup for first deployments with different outputs."
},
{
"courseTitle": "Prompt Engineering for Developers in Python",
"courseDescription": "Develop structured prompting patterns, context management, and chaining techniques."
},
{
"courseTitle": "Advanced Prompt Engineering for Developers in Python",
"courseDescription": "Explore dynamic prompts, multi-step chains, and prompt template optimization."
},
{
"courseTitle": "Fine-Tuning Techniques",
"courseDescription": "Fine-tune models on custom datasets using Python frameworks to boost domain specificity."
},
{
"courseTitle": "Fine-Tuning Optimization",
"courseDescription": "Apply parameter-efficient methods and best practices to speed up fine-tuning."
},
{
"courseTitle": "LLM Evaluation Techniques",
"courseDescription": "Select and implement quantitative and qualitative benchmarks for accuracy, coherence, and task-specific performance."
},
{
"courseTitle": "Quantization and LLM Efficiency",
"courseDescription": "Compress and optimize model size for fast, cost-effective inference."
},
{
"courseTitle": "LLMs for Image-to-Text Processing",
"courseDescription": "Build pipelines that combine vision models and LLMs for descriptive text generation from images."
},
{
"courseTitle": "Text Extraction using Donut Models in Python",
"courseDescription": "Use LLMs to parse and extract text from structured document layouts, like forms, and images."
}
]
}