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LLM Engineering That Works: Prompting, Tuning, and Retrieval 专项课程

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Coursera

LLM Engineering That Works: Prompting, Tuning, and Retrieval 专项课程

Engineer Production-Ready LLM Systems.

Learn prompting, tuning, retrieval, and scalable architectures for reliable AI applications.

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2 月 完成
在 10 小时 一周
灵活的计划
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推荐体验

2 月 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Design and deploy production-grade LLM systems combining prompting, tuning, and retrieval

  • Build reliable, scalable AI pipelines with evaluation, monitoring, and governance

  • Apply responsible AI practices, ethics, and safety throughout the lifecycle of LLMs

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授课语言:英语(English)
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专业化 - 6门课程系列

Production AI Model Development and Ethics

Production AI Model Development and Ethics

第 1 门课程 10小时

您将学到什么

  • Apply custom training loops with callbacks (early-stopping, checkpointing) and diagnose gradient issues using norm and activation analysis.

  • Implement feature engineering pipelines for structured and text data, then evaluate ML experiments to select production-ready models.

  • Create comprehensive model cards for LLM features that detail intended use, technical limitations, and specific fairness metrics.

  • Evaluate AI systems against established ethical guidelines to identify biases and propose actionable mitigation strategies.

您将获得的技能

类别:PyTorch (Machine Learning Library)
类别:Feature Engineering
类别:Model Evaluation
类别:Model Deployment
类别:Responsible AI
类别:Technical Documentation
类别:Deep Learning
类别:Data Ethics
类别:MLOps (Machine Learning Operations)
类别:Scikit Learn (Machine Learning Library)
类别:Artificial Intelligence and Machine Learning (AI/ML)
类别:Software Documentation
类别:Data Preprocessing
类别:Data Pipelines
类别:Scalability
类别:Machine Learning
Building Reliable LLM Systems

Building Reliable LLM Systems

第 2 门课程 18小时

您将学到什么

  • Build scripts with lexical/semantic metrics to evaluate LLMs, diagnose hallucinations, and balance vector-search recall against latency.

  • Apply hypothesis testing, confidence intervals, and significance metrics to evaluate model accuracy and validate results from A/B experiments.

  • Utilize parameterized SQL and data manipulation to segment user logs, calculate retention, and securely retrieve large-scale datasets.

  • Analyze LLM performance gaps to prioritize technical fixes and implement remediation measures for production-level reliability.

您将获得的技能

类别:Performance Testing
类别:Performance Tuning
类别:SQL
类别:Model Evaluation
类别:Statistical Analysis
类别:Data-Driven Decision-Making
类别:Vector Databases
类别:Artificial Intelligence and Machine Learning (AI/ML)
类别:Debugging
类别:Query Languages
类别:Python Programming
类别:Retrieval-Augmented Generation
类别:Statistical Hypothesis Testing
类别:Pandas (Python Package)
类别:Large Language Modeling
类别:LLM Application
类别:MLOps (Machine Learning Operations)
Testing and Refining LLM Applications

Testing and Refining LLM Applications

第 3 门课程 13小时

您将学到什么

  • Apply TDD to microservice endpoints and refactor modules based on code reviews to improve readability and reduce complexity.

  • Develop behavior and safety tests to ensure LLM outputs comply with policies and block unsafe changes to the model.

  • Apply data versioning to track artifacts and evaluate ML experiment runs to select production-ready models.

  • Create scripts using Python's argparse to automate multi-step computational workflows in cloud environments.

您将获得的技能

类别:Continuous Integration
类别:Scripting
类别:Maintainability
类别:Test Driven Development (TDD)
类别:Unit Testing
类别:AI Security
类别:MLOps (Machine Learning Operations)
类别:CI/CD
类别:LLM Application
类别:Python Programming
类别:Software Engineering
类别:Test Automation
类别:Model Deployment
类别:Code Coverage
类别:AI Workflows
类别:Large Language Modeling
类别:SQL
类别:Statistical Analysis
类别:Responsible AI
类别:Testability
Designing Production LLM Architectures

Designing Production LLM Architectures

第 4 门课程 11小时

您将学到什么

  • Compare synchronous and asynchronous architectures and apply 12-factor principles and container orchestration to deploy scalable microservices.

  • Analyze multi-region deployments, pinpoint latency bottlenecks, and design resilient architecture improvements via fault analysis.

  • Create Airflow DAGs to automate data workflows and analyze the impact of schema evolution on downstream processes and tests.

  • Analyze trade-offs between self-hosting models vs. managed APIs and evaluate proposed infrastructure for fault tolerance and cost.

您将获得的技能

类别:Large Language Modeling
类别:Data Pipelines
类别:Infrastructure Architecture
类别:Open Source Technology
类别:Systems Architecture
类别:Kubernetes
类别:Apache Airflow
类别:Managed Services
类别:Containerization
类别:Software Architecture
类别:Scalability
类别:Application Deployment
类别:Microservices
类别:Application Performance Management
类别:Azure DevOps
类别:LLM Application
类别:AWS CloudFormation

您将学到什么

  • Create PRDs with requirements and success metrics, and evaluate features against user-story acceptance criteria to identify gaps.

  • Evaluate prompt patterns and compute-spend reports to implement model-optimization techniques that reduce operational costs.

  • Analyze pipelines using value-stream mapping to eliminate inefficiencies and prioritize chatbot KPI optimizations.

  • Create technical documentation for vector index updates and evaluate system effectiveness against business requirements.

您将获得的技能

类别:Product Requirements
类别:Cost Reduction
类别:User Acceptance Testing (UAT)
类别:Prompt Engineering
类别:LLM Application
类别:Process Mapping
类别:Model Evaluation
类别:Standard Operating Procedure
类别:Workflow Management
类别:Artificial Intelligence and Machine Learning (AI/ML)
类别:Cost Management
类别:Operational Efficiency
类别:Key Performance Indicators (KPIs)
类别:Prompt Patterns
类别:Vector Databases
类别:Large Language Modeling
类别:MLOps (Machine Learning Operations)
类别:Process Optimization
Advancing Your Career in Production AI

Advancing Your Career in Production AI

第 6 门课程 1小时

您将学到什么

  • Position yourself for senior AI roles by creating a strategic portfolio and mastering advanced system design and ethics-focused technical interviews.

您将获得的技能

类别:Data Ethics
类别:Responsible AI
类别:Model Evaluation
类别:SQL
类别:AI Workflows
类别:AI Product Strategy
类别:Prompt Engineering
类别:Communication
类别:AWS CloudFormation
类别:Artificial Intelligence and Machine Learning (AI/ML)
类别:MLOps (Machine Learning Operations)
类别:LLM Application
类别:System Design and Implementation
类别:Technical Design
类别:Python Programming
类别:AI Security
类别:Model Deployment
类别:CI/CD
类别:Apache Airflow
类别:Technical Communication

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