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Axtria
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Model maintenance is done using MLflow, a platform for managing the end-to-end machine learning lifecycle.
MLflow provides tools for tracking experiments, packaging code, and deploying models.
It allows for easy comparison of different model versions and tracking of performance metrics.
MLflow can be integrated with popular machine learning libraries like scikit-learn, TensorFlow, and PyTorch.
Model maintenance involves re...
Precision recall is a measure used in classification tasks to evaluate the quality of the model's predictions.
Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.
Recall is the ratio of correctly predicted positive observations to the all observations in actual class.
Precision and recall are often used together to evaluate the performance of a classification m...
Behavioural question, business development experience
Discussion on resume? Why did you use deep learning model?
I applied via Recruitment Consulltant and was interviewed before May 2023. There were 2 interview rounds.
Top trending discussions
I applied via Company Website and was interviewed in Mar 2024. There was 1 interview round.
I have experience in system design for current product, focusing on scalability and efficiency.
Analyze current system architecture and identify areas for improvement
Consider scalability, performance, security, and user experience in design
Collaborate with cross-functional teams to gather requirements and ensure alignment
Implement best practices and technologies to optimize system design
Regularly review and iterate on s
NoSQL is a non-relational database management system that allows for flexible and scalable data storage, while RDBMS is a traditional relational database management system with structured data storage.
NoSQL databases are schema-less, allowing for easy scalability and flexibility in data storage.
RDBMS databases use structured query language (SQL) for data manipulation and retrieval.
NoSQL databases are better suited for ...
I applied via LinkedIn and was interviewed before Aug 2022. There were 5 interview rounds.
I applied via Recruitment Consulltant and was interviewed in Mar 2022. There was 1 interview round.
I applied via Walk-in and was interviewed in Nov 2024. There were 5 interview rounds.
Normal online code / frontend test
Designing and building a platform similar to BookMyShow involves creating a user-friendly interface for booking tickets for various events.
Develop a user-friendly website and mobile app for users to browse and book tickets for movies, concerts, plays, etc.
Implement a secure payment gateway for users to make online transactions.
Integrate a database to store information about events, venues, tickets, and user bookings.
In...
To build a tinyurl-like service, design a system with a URL shortening algorithm, a database to store mappings, and a redirect service.
Use a hashing algorithm to generate short URLs from long URLs (e.g. MD5, SHA-256).
Store the mappings of short URLs to long URLs in a database (e.g. MySQL, Redis).
Implement a redirect service that takes a short URL, looks up the corresponding long URL in the database, and redirects the u...
I applied via LinkedIn and was interviewed in Jun 2024. There were 4 interview rounds.
Design a system for booking airplane tickets efficiently and securely.
Create a user-friendly interface for customers to search and book flights.
Implement a secure payment system for processing transactions.
Include features for managing flight schedules, seat availability, and pricing.
Integrate with airlines' reservation systems for real-time updates.
Provide options for seat selection, meal preferences, and special requ...
Current system is a web-based application for managing customer data and orders.
Frontend built with React for user interface
Backend built with Node.js and Express for server-side logic
Database using MySQL for storing customer data and orders
Authentication using JWT tokens for secure access
Rate limiter is a mechanism to control the rate of requests sent to a server
Implement a sliding window algorithm to track the number of requests within a specific time frame
Set a limit on the number of requests allowed per unit of time
Return an error response when the limit is exceeded
Test cases: 1. Send requests below the limit - should be successful. 2. Send requests above the limit - should receive an error response
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