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Overall Approach to Designing ML Systems

Aminian introduces a clear framework to guide you through the process of creating a machine learning system in an interview setting. This framework is inspired by creating machine learning systems in real-world contexts and breaks down the design process into several steps, encouraging a systematic approach instead of just diving into model selection.

Comprehend the Issue and Establish the Machine Learning Goal

Before jumping into technical details, it's crucial to first grasp the problem and set a clear machine learning goal. The problem provided in an interview is often intentionally vague, requiring you to probe for further information and constraints. Once a clear objective is established, you can then formulate it to align with suitable ML techniques.

Clarify Requirements and Constraints to Articulate the Problem

Aminian stresses the need to start by making the requirements clear through targeted questions. This involves understanding:

  • Business objective: What is the ultimate goal the design aims to achieve? For instance, is it boosting revenue, customer involvement, or platform safety?

  • Features: What features should the system accommodate? For example, will users be able to give thumbs up or down to recommendations, flag unsafe material, or apply filters to their search?

  • Data: What resources are accessible? What size are the datasets? Are they labeled? What are the characteristics (structured or unstructured, numerical or categorical)?

  • Limitations: What are the constraints related to compute resources, infrastructure, latency requirements, cost, privacy, etc.?

  • Scale: How many users, items, or data points will the system handle? How quickly are these metrics expected to grow?

  • Speed: How quickly must predictions be made? Is real-time functionality needed, or can batch forecasts work?

Practical Tips

  • Develop a habit of reflective journaling to clarify your own requirements before starting new projects. At the end of each day, write down the goals and requirements of your current projects, then formulate questions that address any ambiguities or uncertainties. This practice will help you refine your ability to ask targeted questions in professional settings by first applying it to your personal tasks.
  • Conduct informal interviews with friends or acquaintances who resemble your target audience to understand how design can meet business objectives. Ask them about their experiences with similar products or services and what design aspects made a difference in their satisfaction or purchase decisions. This can provide insights into how design can directly contribute to achieving your business goals.
  • Conduct a "feature audit" of your current systems by listing all the features they offer and marking how often you use each one over a month. This can reveal which features are essential to you and which are superfluous, guiding you in choosing future systems that better align with your actual usage patterns. For instance, if you never use the complex chart-making feature of your office suite, you might prefer a simpler, more streamlined alternative.
  • Explore free online courses or tutorials on data literacy to understand how to evaluate the quality and relevance of datasets. This self-education will empower you to make informed decisions about which datasets are worth your time and which aren't.
  • You can evaluate your tech subscriptions by creating a simple spreadsheet to track usage versus cost. Start by listing all the services you're subscribed to, along with their costs and how often you use them. This will help you identify which subscriptions are not cost-effective, allowing you to cancel or downgrade them to save money.
  • Conduct a "stress test" on your daily schedule by adding hypothetical commitments or responsibilities. For a week, pretend you have additional tasks and see how your current time management holds up. This experiment will reveal the capacity of your current system (your daily routine) to handle growth and help you identify where you might need to make adjustments or improvements.
  • Experiment with a 'decision speed dating' approach for minor daily choices to practice making quicker predictions. Set a timer for 30 seconds and during that time, make a decision about something small, like what to eat for breakfast or which exercise to do. Do this for a week and note if the pressure of the timer helps you become more decisive and if the outcomes of these quick decisions are satisfactory. This can help you get comfortable with making faster predictions in low-stakes situations, which might translate to improved decision-making speed in more significant areas of your life.
Identify Inputs and Outputs for the System

Once you understand the problem and its constraints, define what the system's inputs and outputs will be clearly. This may seem straightforward, but it helps to solidify your understanding of what you're doing. For example, is the input an individual image, a text search, a user profile, or a combination of these? Will the system's result be a single prediction, an ordered list of items, or a clustered set of data points?

Practical Tips

  • Customize your news feed by tweaking your user profile on news aggregation platforms. Regularly update your interests and disinterests to refine the algorithm's suggestions, ensuring that the content you receive is more aligned with your current preferences and less cluttered with irrelevant information.
  • Develop a habit of organizing your weekly tasks into ordered lists based on priority. Use a simple app or a physical notebook to list your tasks for the week, then rank them from most to least critical. At the end of the week, evaluate the list to see how well your prioritization aligned with actual outcomes and adjust your strategy for the following week.
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Machine Learning System Design Interview Summary ML Systems for Visual/Multimedia, Recommendation, Predicting/Classifying, and Moderating Content

Aminian showcases the application of the overarching framework to specific ML systems. He provides detailed solutions for frequently asked questions about designing machine learning systems for interviews, walking you through the thought process and highlighting best practices for designing practical and scalable systems.

Visual Search Program

Aminian tackles the design of a system for searching images, where users can find images visually resembling a given query image.

Learn Image Representations Using Contrastive Training

Aminian suggests using representation learning through neural networks, specifically CNN-based architectures or Transformers, to learn image embeddings. He advocates for contrastive training, where the model is taught to distinguish between similar and dissimilar images.

Aminian discusses various strategies for constructing training data: using human judgment, inferring similarity from user clicks, or creating altered copies of the image being queried to represent visually comparable images. He analyzes the compromises of each approach.

Practical Tips

  • You can explore image recognition by using online platforms that allow you to train...

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Machine Learning System Design Interview Summary Infrastructure and Monitoring for ML Systems

Aminian dedicates a portion of the book to discussing critical aspects of infrastructure and system observation for maintaining and scaling a machine learning setup.

Monitor Model Performance and System Health

Aminian reminds you to consider monitoring both metrics related to operations and ML-specific metrics:

  • Operation-related metrics: Confirm smooth system operation. Track metrics like latency, throughput, request volume, and how resources are utilized.

  • Machine learning-specific metrics: Ensure reliable predictions from the model. Track metrics like data and concept drifts, model accuracy, and distributions of outputs.

Practical Tips

  • Use a fitness tracker or app to measure and analyze your personal health metrics, such as heart rate (latency), steps taken (throughput), and calories burned (resource utilization). By observing these metrics over time, you can adjust your exercise and diet plans to optimize your health and fitness routine. For instance, if you notice your heart rate is higher than usual during workouts, you might decide to increase your cardiovascular training to improve your...

Machine Learning System Design Interview

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