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I applied via Recruitment Consulltant and was interviewed before Apr 2023. There were 3 interview rounds.
Success metrics are defined based on project goals and are tracked using key performance indicators (KPIs).
Define success metrics based on project goals and objectives
Identify key performance indicators (KPIs) to track progress
Regularly monitor and analyze KPIs to measure success
Adjust metrics and KPIs as needed to align with project outcomes
Use tools like dashboards and reports to visualize and communicate progress
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I have experience developing apps on Appsmith and believe improvements in user interface customization and performance are necessary.
Enhance user interface customization options to allow for more flexibility in design
Improve performance optimization to ensure smooth and efficient app functionality
Implement more integrations with third-party tools and services for enhanced functionality
Enhance documentation and support ...
The priorities for solving suggested improvements should be based on impact, feasibility, and urgency.
Prioritize improvements based on their impact on the overall performance or efficiency of the system.
Consider the feasibility of implementing each improvement, taking into account resources, time, and expertise required.
Evaluate the urgency of each improvement based on potential risks or consequences of not addressing
Top trending discussions
Practise data structures based problems, should be able to explain the code and optimizations etc
Design system for e-commerce. How to scale the system with the demand
Building a ML ops platform involves designing infrastructure, tools, and processes to streamline machine learning workflows.
Design scalable infrastructure to support ML model training and deployment
Implement version control for ML models and data
Automate testing and monitoring of ML models
Integrate with CI/CD pipelines for continuous deployment
Provide collaboration tools for data scientists and engineers
Practise data structures based problems, should be able to explain the code and optimizations etc
Design system for e-commerce. How to scale the system with the demand
Building a ML ops platform involves designing infrastructure, tools, and processes to streamline machine learning workflows.
Design scalable infrastructure to support ML model training and deployment
Implement version control for ML models and data
Automate testing and monitoring of ML models
Integrate with CI/CD pipelines for continuous deployment
Provide collaboration tools for data scientists and engineers
Front end Engineer
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Senior Software Engineer
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Front end Developer
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