Interview Questions and Answers

Tutorial Rays Interview Questions and Answers
Model Deployment
  • December 30, 2025
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Model Deployment Chapter 5 – Monitoring and Maintenance of ML Models in Production

Monitoring and Maintenance of Machine Learning Models Deploying a machine learning model is not the final step in the ML lifecycle. Once a model is in production, it must be.

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Model Deployment
  • December 30, 2025
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Model Deployment Chapter 4 – Cloud Platforms for ML | AWS, GCP and Azure Overview

<div class=”bloc-syllabus”> <h2>Cloud Platforms Overview for Model Deployment (AWS, GCP, Azure)</h2> <p class=”blog_p”> Modern machine learning systems rarely run on local machines in production. Instead, they are deployed on cloud.

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Model Deployment
  • December 30, 2025
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Model Deployment Chapter 3 – Introduction to Docker for ML Deployment

Introduction to Docker for Model Deployment When machine learning models move from development to production, differences in environments often cause deployment failures. Docker solves this problem by packaging applications, dependencies,.

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Model Deployment
  • December 30, 2025
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Model Deployment Chapter 2 – Building REST APIs for ML | Flask and FastAPI

Building REST APIs for Machine Learning Using Flask and FastAPI Once a machine learning model is trained and serialized, it must be exposed to users or applications for predictions. The.

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Model Deployment
  • December 30, 2025
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Model Deployment Chapter 1 – Model Serialization for ML | Pickle and Joblib

Model Serialization in Machine Learning (Pickle and Joblib) After training a machine learning or deep learning model, the next crucial step is saving the model so it can be reused.

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Big Data
  • December 30, 2025
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Big Data Chapter 6 – NoSQL Databases | MongoDB and Cassandra Explained

NoSQL Databases in Big Data (MongoDB and Cassandra) Traditional relational databases struggle with scalability, flexibility, and performance when handling massive volumes of unstructured and semi-structured data. NoSQL databases were designed.

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Big Data
  • December 30, 2025
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Big Data Chapter 5 – Working with Streaming Data | Spark Streaming Basics

Working with Streaming Data in Big Data In many real-world applications, data is not generated in batches but arrives continuously in real time. Examples include sensor data, financial transactions, social.

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Big Data
  • December 30, 2025
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Big Data Chapter 4 – Spark SQL and DataFrames | Structured Data Processing

Spark SQL and DataFrames While RDDs give low-level control, most real-world Big Data applications work with structured or semi-structured data. Spark SQL and DataFrames provide a high-level, optimized, and user-friendly.

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Big Data
  • December 30, 2025
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Big Data Chapter 3 – Apache Spark Basics | Fast Distributed Data Processing

Apache Spark Basics Apache Spark is a powerful, open-source Big Data processing framework designed for fast, in-memory computation. Unlike MapReduce, Spark processes data in memory, making it significantly faster for.

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Big Data
  • December 30, 2025
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Big Data Chapter 2 – HDFS and MapReduce | Distributed Storage and Processing

HDFS and MapReduce in Big Data HDFS and MapReduce are the two core pillars of the Hadoop framework. HDFS handles the storage of massive datasets, while MapReduce processes those datasets.

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