AI & machine learning · Skill
ML Pipeline Workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Open in PhởWhat the skill covers
The sections of its instructions, in order.
- 01 Overview
- 02 When to Use This Skill
- 03 What This Skill Provides
- Core Capabilities
- Reference Documentation
- Assets and Templates
- 04 Usage Patterns
- Basic Pipeline Setup
- Production Workflow
- 05 Best Practices
- Pipeline Design
- Data Management
- Model Operations
- Deployment Strategies
- 06 Integration Points
- Orchestration Tools
- Experiment Tracking
- Deployment Platforms
- 07 Progressive Disclosure
- 08 Common Patterns
More in LLM apps and fine tuning
- Embedding Strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding…
- Hybrid Search Implementation
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
- LangChain & LangGraph Architecture
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating…
- LLM Evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI…
- RAG Implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document…
- Similarity Search Patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
- Vector Index Tuning
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
- Checkpoint Promotion
Gate fine-tuned checkpoints with drift budgets, paired comparison, and forgetting checks before promotion. Use after a training run produces a checkpoint, when deciding whether a…
- Dataset Curation
Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring…
- Eval Harness First
Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a…
- Fine-Tuning Method Selection
Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort…
- GRPO & RLVR Training
Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code…
- LoRA & QLoRA Recipes
Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing…
- Preference Optimization
Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between…
- Quantized Export
Export a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes promotion, when…
- Trace To Training Data
Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when…
- Vision-Language SFT
Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or…
- Recsys Pipeline Architect
Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X…
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Frequently asked questions
What does ML Pipeline Workflow do?
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Is ML Pipeline Workflow free to use?
Yes. ML Pipeline Workflow is published by Seth Hobson under the MIT licence, and you can use it with a free Phở account.
How do I use ML Pipeline Workflow in Phở?
Press Open in Phở, then Try to apply it to your next message, or add the LLM apps and fine tuning set so Phở loads it whenever a request matches.
Does ML Pipeline Workflow need other tools?
No. It works in a normal Phở chat with what you type or upload.
Copied unchanged from wshobson/agents at commit 7e2fb55 and credited to Seth Hobson, who is not affiliated with Phở. The full instructions and licence text ship with the skill inside Phở.