How To Answer Any Machine Learning System Design Interview Question by Stefan Hosein
Table Of Content
If there is any significant model performance drift is observed, re-fresh/re-train the model. The system should be able to perform the correct function at the desired level of performance under a specified environment. In all the above scenarios, the expected ML response is accurate, fast, and reliable.
AWS CloudFormation support
A lot of its architectural decisions are inspired by the GFS design. HDFS is built around the idea that the most efficient data processing pattern is a “write once, read many times” pattern. At the end, the booklet contains 27 open-ended machine learning systems design questions that might come up in machine learning interviews. The answers for these questions will be published in the book Machine Learning Interviews. You can look at and contribute to community answers to these questions on GitHub here.
Stateless and stateful systems
Containerization is the packaging of software code with its dependencies to create a “container” that can run on any infrastructure. We can think of containers as more lightweight versions of virtual machines (VMs) that don’t need their own operating system. All containers on a host share that host’s operating system, which frees up a lot of system resources.
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Folders and files
Scalability, reliability, security, and privacy are paramount during deployment. The infrastructure should be scalable to handle varying workloads, ensuring the model can handle increasing demand. Reliability measures, such as redundancy, fault tolerance, and load balancing, should be in place to ensure the availability of the deployed model. Robust security practices, including access controls, encryption, and data anonymization, should be implemented to protect sensitive data and prevent unauthorized access. Privacy concerns should be addressed by complying with relevant regulations and implementing privacy-enhancing techniques, such as differential privacy. Model serving mechanisms need to be established to handle prediction requests efficiently.
Machine Learning Systems Design: A Free Stanford Course
In each later stage, you continue to increase the complexity (i.e. more optimized model in prediction) and execution time. The model needs to run on a reduced number of documents as the stages progress (e.g. your first stage could use a linear model and the final stage can use a deep neural network). Our goal is to improve our metrics when working on an ML-based system. We also want to ensure that we meet the capacity and performance Service Level Agreement (SLA). Performance-based SLA ensures that we return results within a given time frame (e.g. 500ms) for 99% of queries.
Analyzing feature correlations, performing statistical tests, or visualizations can provide insights into their impact on the target variable. To ensure the effectiveness of the training process, it is crucial to balance the positive and negative training samples. This balance is essential to avoid biased models and ensure accurate predictions across different classes or categories.
Online monitoring, on the other hand, tracks the model’s performance in real-time using live data. Both approaches help identify deviations or anomalies in the model’s behavior and trigger alerts or interventions when necessary. While offline model evaluation provides valuable insights, it is often necessary to complement it with online experimentation, such as A/B testing. Online experimentation allows for real-time evaluation and comparison of models or interventions in a live environment, gathering user feedback and assessing the model’s performance in a production setting. The Feature Engineering phase is a crucial step in machine learning system design as it transforms raw data into meaningful and representative features. Through careful selection, transformation, handling of missing values or outliers, and addressing bias, this phase plays a vital role in improving the quality and performance of the machine learning model.
It creates and refines its rules on a given task based on that data, which is called training data. To effectively develop such models, it’s essential to learn machine learning principles and techniques. This makes it crucial to avoid inadequate, irrelevant, or biased data. For instance, a machine learning model based on racially biased data will simply learn to automate racial bias. Even the most performant algorithms are useless if they are not based on quality dataset.
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Block storage is a data storage technique where data is broken down into blocks of equal sizes, and each individual block is given a unique identifier for easy accessibility. As opposed to adhering to a fixed path, blocks can be stored anywhere in the system, making more efficient use of the resources. Before you even begin working on the problem, you have to make sure you have enough information. The key to designing an efficient model is gathering as much information as possible.
These metrics provide valuable insights into the system’s accuracy, efficiency, and ability to meet business objectives. A. System design for machine learning involves designing the overall architecture, components, and processes necessary to develop and deploy machine learning models effectively. Each step based on criticality and business requirements is important for the success of the ML-based systems. The Training Data Collection phase is a critical step in machine learning system design as it lays the foundation for model training and performance.
Common metrics include accuracy, precision, recall, F1 score, and area under the ROC curve (AUC-ROC). The selection depends on the problem domain, class imbalances, and business objectives. It is important to choose metrics that provide meaningful insights into model performance and impact on the problem at hand. If the data is imbalanced, techniques like oversampling or undersampling can be used to address the class distribution and improve model performance. Stream processing refers to a computer programming architecture that focuses on the real-time processing of continuous streams of data.
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Object storage is the preferred data storage method for data archiving and data backups because it offers dynamic scalability. Communication happens through RESTful APIs at the application level. This type of storage provides immense flexibility and value to systems, because backups, unstructured data, and log files are important to any system. If you’re designing a system with large datasets, object storage would work well for your organization.
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