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"I'm a 3rd-year Computer Engineering student at Marwadi University with skills in C++, web development (MERN stack), and DevOps tools like Kubernetes. I contribute to open-source projects and share tech knowledge on GitHub and LinkedIn. I'm learning cloud technologies and app deployment. As an Internshala Student Partner, I help others find jobs and courses." now currently focusing on #90DaysOfDevops
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In this series whatever i will learn through out my aws journey i will share with you with blogs
Management services and migration services are essential components in the realm of cloud computing, particularly when working with AWS (Amazon Web Services). These services help organizations manage, provision, optimize, and migrate their IT resourc...
One of the most common networking issues in AWS occurs when an EC2 instance is correctly configured but remains inaccessible from the internet. Even if the security group, network ACLs, and applicatio
Static websites are one of the simplest and most cost-effective ways to host web content in the cloud. Instead of managing virtual machines or web servers, Amazon S3 allows you to store HTML, CSS, Jav
Containerization has become the standard way to package and deploy modern applications. Instead of installing software directly on virtual machines, developers package everything—including dependencie
On Day 37 of the KodeKloud 100 Days of Cloud Challenge, I explored one of the AWS security best practices—granting an EC2 instance access to Amazon S3 using IAM Roles instead of storing AWS credential
On Day 36 of the KodeKloud 100 Days of Cloud Challenge, I built a complete Application Load Balancer (ALB) architecture on AWS. Instead of exposing an EC2 instance directly to the internet, an Applica
Anand Raval
136 posts
I'm Anand Raval, a Cloud & DevOps Engineer with AWS Solutions Architect Associate (SAA-C03), Certified Kubernetes Administrator (CKA), and Azure Fundamentals (AZ-900) certifications. This blog covers AWS, Kubernetes, Terraform, CI/CD, cloud architecture, automation, cost optimization, troubleshooting guides, and hands-on DevOps projects.
In the realm of artificial intelligence (AI) and machine learning (ML), Amazon Web Services (AWS) offers a suite of powerful services designed to cater to diverse business needs. These services enable organizations to harness the potential of AI/ML without requiring extensive expertise in the field. Here's an introduction to some of the key AI/ML services provided by AWS:
Amazon SageMaker: This is a fully managed service that simplifies the process of building, training, and deploying ML models at scale. SageMaker provides a comprehensive set of tools for data preparation, model training, and deployment, making it accessible for developers and data scientists to accelerate their ML workflows. It supports popular frameworks like TensorFlow and PyTorch and offers features like SageMaker Studio for an integrated development environment, and SageMaker Data Wrangler for data preparation.
Amazon Lex: Amazon Lex is a service for building conversational interfaces using voice and text. It powers chatbots and virtual assistants with natural language understanding and automatic speech recognition. Lex is used to create applications that can engage in lifelike conversations, making it ideal for customer service bots and interactive voice response systems.
Amazon Kendra: This is an intelligent search service powered by machine learning. Kendra enables organizations to index and search their data across various repositories, providing accurate and relevant search results. It is particularly useful for enterprises looking to enhance their internal search capabilities and improve information retrieval.
Amazon Comprehend: A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text. Comprehend can perform tasks such as sentiment analysis, entity recognition, and language detection, making it valuable for analyzing customer feedback, social media posts, and other text data.
Amazon Polly: This service turns text into lifelike speech, allowing developers to create applications that can talk. Polly supports multiple languages and voices, making it suitable for creating interactive voice applications, e-learning platforms, and accessibility tools.
These AWS services are designed to be scalable, cost-effective, and easy to integrate into existing workflows, enabling businesses to leverage AI/ML technologies to drive innovation and efficiency across various domains. Whether you're looking to build sophisticated ML models, create conversational interfaces, or analyze text data, AWS provides the tools and infrastructure to support your AI/ML initiatives.
Thankyou for reading !!!!!
