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📝 Design doc template & examples for machine learning systems (requirements, methodology, implementation, etc.)
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# ml-design-doc A template for design docs for machine learning systems based on this [post](https://eugeneyan.com/writing/ml-design-docs/). Note: This template is a guideline / checklist and is **not meant to be exhaustive**. The intent of the design doc is to help you think better (about the problem and design) and get feedback. Adopt whichever sections—and add new sections—to meet this goal. View other templates, examples [here](#other-templates-examples-etc). --- ## 1. Overview A summary of the doc's purpose, problem, solution, and desired outcome, usually in 3-5 sentences. ## 2. Motivation Why the problem is important to solve, and why now. ## 3. Success metrics Usually framed as business goals, such as increased customer engagement (e.g., CTR, DAU), revenue, or reduced cost. ## 4. Requirements & Constraints Functional requirements are those that should be met to ship the project. They should be described in terms of the customer perspective and benefit. (See [this](https://eugeneyan.com/writing/ml-design-docs/#the-why-and-what-of-design-docs) for more details.) Non-functional/technical requirements are those that define system quality and how the system should be implemented. These include performance (throughput, latency, error rates), cost (infra cost, ops effort), security, data privacy, etc. Constraints can come in the form of non-functional requirements (e.g., cost below $`x` a month, p99 latency < `y`ms) ### 4.1 What's in-scope & out-of-scope? Some problems are too big to solve all at once. Be clear about what's out of scope. ## 5. Methodology ### 5.1. Problem statement How will you frame the problem? For example, fraud detection can be framed as an unsupervised (outlier detection, graph cluster) or supervised problem (e.g., classification). ### 5.2. Data What data will you use to train your model? What input data is needed during serving? ### 5.3. Techniques What machine learning techniques will you use? How will you clean and prepare the data (e.g., excluding outliers) and create features? ### 5.4. Experimentation & Validation How will you validate your approach offline? What offline evaluation metrics will you use? If you're A/B testing, how will you assign treatment and control (e.g., customer vs. session-based) and what metrics will you measure? What are the success and [guardrail](https://medium.com/airbnb-engineering/designing-experimentation-guardrails-ed6a976ec669) metrics? ### 5.5. Human-in-the-loop How will you incorporate human intervention into your ML system (e.g., product/customer exclusion lists)? ## 6. Implementation ### 6.1. High-level design  Start by providing a big-picture view. [System-context diagrams](https://en.wikipedia.org/wiki/System_context_diagram) and [data-flow diagrams](https://en.wikipedia.org/wiki/Data-flow_diagram) work well. ### 6.2. Infra How will you host your system? On-premise, cloud, or hybrid? This will define the rest of this section ### 6.3. Performance (Throughput, Latency) How will your system meet the throughput and latency requirements? Will it scale vertically or horizontally? ### 6.4. Security How will your system/application authenticate users and incoming requests? If it's publicly accessible, will it be behind a firewall? ### 6.5. Data privacy How will you ensure the privacy of customer data? Will your system be compliant with data retention and deletion policies (e.g., [GDPR](https://gdpr.eu/what-is-gdpr/))? ### 6.6. Monitoring & Alarms How will you log events in your system? What metrics will you monitor and how? Will you have alarms if a metric breaches a threshold or something else goes wrong? ### 6.7. Cost How much will it cost to build and operate your system? Share estimated monthly costs (e.g., EC2 instances, Lambda, etc.) ### 6.8. Integration points How will your system integrate with
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Eugene Yan · @anthropics · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:367ccc4182466449, llm:Repository topics: design, design-docs, machine-learning; description: 'Design doc template & examples for machine learning systems'; README provides template and guidance for ML system design docs.