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Articles by Neetika Khandelwal
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Advantages and Disadvantages of Three-tier Architecture
A 3−tier application architecture is a modular client−server architecture that consists of a presentation tier, an application tier, and a data tier. The presentation tier is a graphical user interface (GUI) that interacts with the other two tiers; the data tier stores information; the application tier manages logic. A 3−tier architecture has pros in terms of better horizontal scalability, performance, and availability. When there are three layers, each component can be produced concurrently by a separate team of programmers using a different programming language than the developers of the other levels. The 3−tier paradigm makes it simpler for an organization ...
Read MoreBuild a Calculate Expression Game in Java
The task is to build an application which displays an expression and prompts for the answer. Once you enter the solution a new expression is displayed and these expressions keep on changing. After a certain amout of time the solution given for the respective application were evaluated and the total number of accurate answers were displayed. Methods (User defined) in the Game The section will walk you through the methods (and constructor) that are part of the implementation and their roles in the “Calculate Expression Game”. CalculateExpressionGame() This is the constructor of the calculateExpressionGame class. It sets up the ...
Read MoreBinary Search vs Contains Performance in Java List
When it comes to searching for elements in a collection, Java provides different options depending on the data structure you're using. Two popular methods for searching in a list are binary search and the contains() method. In this blog post, we'll compare the performance of binary search and contains in a Java list, highlighting their differences, strengths, and best use cases. Binary Search Binary search is an efficient way for locating a specific member in a sorted list. The search space is divided in half on a regular basis until the target element is found or the search space is ...
Read More12 Tips to Optimize Java Code Performance
Java is a well−known programming language that is used to create a variety of applications. The user experience could be harmed by performance issues that can be caused by poorly optimized Java code. In this blog post, you will come across 12 techniques for improving the performance of Java programming. Techniques to Optimize Java Code Use Efficient Data Structures The choice of a suitable data structure has a significant impact on the effectiveness and speed of Java programming. For instance, choosing a LinkedList over an ArrayList can be advantageous if you frequently add or delete entries from a list because ...
Read MoreBreak Any Outer Nested Loop by Referencing its Name in Java
Programming is all about coming up with the best and most efficient ways to solve the real−world problems. There are situations when you want to exit multiple loops simultaneously. This can be accomplished in Java by simply referencing the name of the loop you want to exit. In this tutorial, we'll look at how to break any outer nested loop in Java by referencing its name. Referencing Loop Names in Java You can break out of the Java nested loop by labelling the outer loop. This can be accomplished by using a label before the outer loop followed by a ...
Read MoreWorkflow of MLOps
The purpose of MLOps, is to standardize and streamline the continuous delivery of high performing models in production by combining ML systems development (dev) with ML systems deployment (ops). It aims to accelerate the process of putting machine learning models into operation, followed by their upkeep and monitoring. An ML Model must go through a number of phases before it is ready for production. These procedures guarantee that your model can appropriately scale for a wide user base. You'll run into that MLOps workflow. Why MLOps? Data ingestion, data preparation, model training, model tuning, model deployment, model monitoring, explainability, and ...
Read MoreEvaluating MLOps Platform
An MLOps platform's goal is to automate tasks associated with developing ML-enabled systems and to make it simpler to benefit from ML. Building ML models and gaining value from them requires several stages, such as investigating and cleaning the data, carrying out a protracted training process, and deploying and monitoring a model. An MLOps platform can be considered a group of tools for carrying out the duties necessary to reap the benefits of ML. Not all businesses that benefit from machine learning use an MLOps platform. Without a platform, it is absolutely possible to put models into production. Choosing and ...
Read MoreMLOps Tools, Best Practices and Case Studies
A collection of procedures and methods known as MLOps are meant to guarantee the scalable and reliable deployment of machine learning systems. To reduce technological debt, MLOps uses software engineering best practices such as automated testing, version control, the application of agile concepts, and data management. Using MLOps, the implementation of Machine Learning and Deep Learning models in expansive production environments can be automated while also improving quality and streamlining the management process. In this article, you will come across some of the tools and best practices that would help you do this job. MLOps Best Practices Following ...
Read MoreWho is MLOps Engineer?
What is MLOps? Machine Learning Operations (MLOps) is an acronym for Machine Learning Operations. MLOps is a basic component of Machine Learning engineering that focuses on optimizing the process of deploying machine learning models and maintaining and monitoring them. MLOps is a team effort that frequently includes data scientists, DevOps engineers, and IT. MLOps aims to boost automation and improve the quality of production models while simultaneously concentrating on business and regulatory needs, similar to DevOps and DataOps methodologies. MLOps began as a set of best practices, but it is gradually becoming a stand-alone solution to managing the ML lifecycle. ...
Read MoreMLOps vs DevOps
It would have often occurred that the development team has moved on to a new project while the operations team provides feedback on the previous one. This caused the deadline to be pushed back, for the entire software development cycle or machine learning model development cycle. For this reason, IT has adopted the new ways of working for preparing software and machine learning models, they are MLOps and DevOps. In this blog, you will get to know about these terms and how they differ. What is DevOps? The term DevOps stands for Development + OperationS. It is a method in ...
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