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Last updated on Dec 25, 2024
  1. All
  2. Engineering
  3. Algorithms

Your algorithm faces unexpected demands from end-users. How do you adapt to meet their needs?

As end-user needs evolve, so must your algorithm. To keep pace with change:

  • Gather user feedback: Regularly collect and analyze end-user input to identify new requirements.

  • Iterate quickly: Implement agile development practices to rapidly test and refine updates.

  • Monitor performance: Continuously track how changes affect user experience and system efficiency.

How do you adjust when users throw curveballs at your algorithms? Looking forward to hearing your strategies.

Algorithms Algorithms

Algorithms

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Last updated on Dec 25, 2024
  1. All
  2. Engineering
  3. Algorithms

Your algorithm faces unexpected demands from end-users. How do you adapt to meet their needs?

As end-user needs evolve, so must your algorithm. To keep pace with change:

  • Gather user feedback: Regularly collect and analyze end-user input to identify new requirements.

  • Iterate quickly: Implement agile development practices to rapidly test and refine updates.

  • Monitor performance: Continuously track how changes affect user experience and system efficiency.

How do you adjust when users throw curveballs at your algorithms? Looking forward to hearing your strategies.

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Help others by sharing more (125 characters min.)
5 answers
  • Contributor profile photo
    Contributor profile photo
    Oleg Tagobitsky

    Cloud APIs | AI | Computer Vision | Image Analysis | CBDO

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    Adapting an algorithm to unexpected user demands requires a mix of agility, user feedback and technical precision. First, identify the root cause of the new requirements — are they based on real needs or just edge cases? Prioritize changes that align with your core objectives while maintaining performance and scalability. Use A/B testing and iterative updates to fine-tune results. Also, communicate openly with users — set realistic expectations and explain trade-offs. AI is about continuous learning and sometimes, the best adaptation isn’t adding complexity but refining what already works.

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    Eshita Sohani

    Chevening Scholar 2024 | MSc Computer Science @University of Birmingham I Cloud and Devops Engineer | AI Agent Developer | 2X AWS Certified and Terraform Associate Certified Developer | Researcher |

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    A while ago, I was managing a cloud-based analytics platform that suddenly faced unpredictable spikes in data uploads when a new feature was introduced. The existing architecture couldn’t handle the increased load, causing delays and failures. To address this, I gathered feedback, analysed logs, and implemented auto-scaling using AWS Lambda and Kinesis to handle the varying data volume. We also used SQS to queue data during peak times, ensuring nothing was lost. After testing and gradual rollout, the system stabilised, and performance improved. This taught me the importance of scalability in cloud systems. How do you handle sudden demands in your cloud solutions?

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    Alvin H.

    Compliance | Procurement | Data Analysis | Excel | SQL | R | Python

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    In my experience, algo changes based on quick response can cause chaos or vulnerabilities. I don't see where unit testing or testing is done to ensure known potential issues do not arise. When you create any algo, there are assumptions, risks willing to take, and potential failures. If you only look to customers to drive your product, you will find people that will exploit your product. Have a test plan in place based on your assumptions, risks, and potential failures.

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    Yash Gaglani

    Senior Software Development Engineer @ ThirdAi Platforms | Java | Microservices | Spring Boot | MySQL | MongoDB | AWS | Cyber Security.

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    Adapt your algorithm to meet unexpected user demands using a layered approach: 1. Logs & Observability: Use tools like Micrometer and CloudWatch to analyze usage spikes and performance bottlenecks. 2. Root Cause Analysis (RCA): Identify if the algorithm is correct but lacks scalability example it can have multiple sequential DB calls, inefficient indexing, or server-side file uploads which is correct by lacks scalability. 3 Evaluate Alternatives: Find efficient alternatives with careful consideration of trade-offs like cost, speed, and complexity e.g leverage batch processing, or switch to pre-signed URLs for better performance. 4. Controlled rollout: Deploy in closed beta, monitor log and validate before full deployment.

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    Ruchitha Pellakuru

    | Full Stack Software Engineer | AWS | Hodophile | GHC'23| Google TechStars'24| Top Web Development Voice|

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    When end-users introduce unexpected demands, I prioritize adaptability through continuous feedback loops. I collect real-time user data and analyze behavioral patterns to detect emerging needs. Agile iteration ensures rapid testing and deployment of updates without disrupting core functionality. Implementing modular and parameterized designs allows quick adjustments without major rewrites. Automated monitoring with anomaly detection helps identify performance shifts early. Lastly, I use A/B testing to validate changes, ensuring that optimizations genuinely enhance user experience while maintaining algorithm efficiency and scalability.

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