Top 22 Orange Interview Questions with Answers
Here are 22 commonly asked Orange interview questions along with concise answers: 1. What is Orange? Orange is an open-source data visualization and analysis tool that provides a visual programming…
Here are 22 commonly asked Orange interview questions along with concise answers: 1. What is Orange? Orange is an open-source data visualization and analysis tool that provides a visual programming…
Here are 30 commonly asked Weka interview questions along with concise answers: 1. What is Weka? Weka is a popular open-source machine learning toolkit that provides a collection of algorithms…
Here are 15 commonly asked RapidMiner interview questions along with concise answers: 1. What is RapidMiner, and what are its key features? RapidMiner is a data science platform that allows…
1. What is PyBrain? Ans: PyBrain is an open-source machine-learning library for Python that provides tools for neural network-based artificial intelligence and machine-learning algorithms. 2. What are the key features…
1. What is MXNet? Ans: MXNet is an open-source deep learning framework designed for efficient and flexible implementation of neural networks. 2. What are the key features of MXNet? Ans:…
1. What is Theano? Ans: Theano is an open-source numerical computation library for Python that allows efficient computation of mathematical expressions, especially those involving multi-dimensional arrays. It is commonly used…
1. What is Deeplearning4j (DL4J)? Deeplearning4j is an open-source, distributed deep learning library for Java and Scala, designed to be used in business environments and integrated with the Java Virtual…
1. What is a machine learning library? Ans: A machine learning library is a collection of tools, algorithms, and functions that provide pre-built functionality for developing and applying machine learning…
1. What is BigDL? BigDL is an open-source distributed deep learning library for Apache Spark that allows users to build and train deep learning models on big data using familiar…
1. What is IBM Watson Studio? Ans: IBM Watson Studio is an integrated development environment (IDE) that enables data scientists and developers to collaboratively build, train, and deploy machine learning…
1. What is Amazon SageMaker? Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS). It helps data scientists and developers to build, train, and…
1. What is Scikit-learn? Ans: Scikit-learn is a popular Python machine-learning library that provides efficient tools for data analysis and modeling. It includes a wide range of algorithms for classification,…
1. What is the Microsoft Cognitive Toolkit (CNTK)? The Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning library designed to help developers create machine learning models. It provides a…
1. What is Apache Mahout? Apache Mahout is an open-source machine-learning library that provides scalable implementations of common machine-learning algorithms. It is designed to run on distributed computing platforms such…
1. What is Keras? Answer: Keras is a high-level deep learning API written in Python that runs on top of lower-level deep learning libraries such as TensorFlow, Theano, or Microsoft…
1. What is PyTorch? Answer: PyTorch is an open-source machine learning library for Python that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based…
1. What is TensorFlow? TensorFlow is an open-source machine learning framework developed by Google. It allows developers to build and train machine learning models using data flow graphs, which represent…
1. What is Airflow? Airflow is an open-source platform used to programmatically create, schedule, and monitor workflows. It allows developers and data engineers to create complex data pipelines by defining…
The AI Ops workflow involves several steps to implement and operate AI-based automation in IT operations. Here is a high-level overview of the typical AiOps workflow:
AIOps implementation follows a set of principles to ensure successful deployment and operation. Here are some of the key principles for implementing AIOps:
There are different implementation models for AIOps, depending on the organization’s specific needs and goals. Here are some of the most common AiOps implementation models:
Here are some steps to get started with AIOps:
Here are some examples of successful proof of concepts (POCs) for AIOps that have demonstrated significant benefits:
Here are 20 AiOps companies, including