Table of Content
Mass Emailing using AWS Lambda
As the name suggests, this project aims at sending bulk emails to the existing and potential customers of a business firm. One of the advantages of using AWS Lambda is that it can easily be combined with other email or SMS services, developing a cost-effective mass-mailing solution. AWS Lambda is S3 that helps in the sharing of mass emails with a higher number of recipients. As soon as a CSV file is uploaded, an AWS S3 event is triggered; after which, the Lambda function imports the file into the database. Once this is set up, you can begin the process of sending emails in bulk to the provided email addresses.
The most popular example of mass emailing bulk service is MoonMail, which is designed using AWS Lambda.
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Using Amazon Recognition to Identify People
This project works using Amazon Rekognition on the concepts of computer vision, machine learning, and AI. To get started with the project, you need to have a good understanding of the basics of computer vision and its related algorithms.
As part of the project, you will have to create a face recognition model that can identify specific people in a photograph or image. In general, training face recognition is a tiring process and takes some time, but AWS Lambda makes the task easier.
To successfully implement this project, you need to use Amazon Recognition to perform face recognition. It makes the job easier by automatically extracting metadata from image and video files and capturing objects, faces, text and more by leveraging deep learning. To further progress in this project, you will have to train your model in identifying a famous personality. After training it for some time and feeding enough data, you can test your project to see how well it performs. In order to level up the task, you can train your model by adding more people.
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Train a Machine Learning Model with SageMaker
Amazon SageMaker is one of the best machine learning services offered by Amazon.
The aim of this project is to train a machine learning model using SageMaker. It provides an integrated development environment (IDE) for machine learning, in which you can train your machine learning model with the help of insightful data. The IDE allows you to create notebooks, switch between steps, check results, and more. The best part of working with SageMaker is that it enables you to get the compute instances faster and in a highly efficient way. To further decrease your amount of effort invested, use the Autopilot feature of SageMaker to complete the process with much less effort.
Website Development using AWS
The goal of this project is to develop a highly secure and reliable website using AWS Lightsail. It is a virtual private server that is used to create numerous websites. You can experiment with working on AWS by creating a website. You can create a website connected to the database. To make the website building task easier, you can use AWS EC2 or AWS Lambda services with AWS Lightsail as the virtual private server. It provides SSD-based storage and comes pre-configured with various web development options.
Building Custom Alexa Skills
The goal of this project is to develop a virtual assistant like Alexa and replicate its skills. To implement this, you can use AWS Lambda with a custom Alexa skill set. It is an object that is embedded within AWS Console to invoke the handler function. Along with this, you can use the Alexa handler function, which is a function of AWS Lambda. You will obtain custom logic to invoke the handler function.
Not only this, but as part of this project, you can also use third-party functions hosted outside of Alexa. To start with, you can begin tasks such as playing music, creating reminders, or asking to perform a function.
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Creating a Text-to-speech Converter
Text-to-speech is an AI-based functionality that is popularly used in many websites and web applications. The key focus area of this project is to create a text-to-speech converter. AWS Lambda and Amazon Polly are best to convert textual information to speech. This combination can help you develop real-life speech synthesis applications. With Amazon Polly, you can use advanced deep learning technologies to carry out accurate conversions; while AWS Lambda provides the ability to improve the response rate as it is critical in real-time applications.
Content Recommendation System
The goal is to use AI and machine learning with AWS to recommend content to end users based on history. Almost all streaming platforms, such as Netflix, Amazon Prime Video, and others, have content recommendations systems. You can use AWS cloud with nearest neighbor algorithms to work on this project. For this project, use Amazon SageMaker; it will help in carrying out machine learning implementations with ease. It includes built-in algorithms that do not need label data. It also uses semantic search in place of string matching to simplify tasks. AWS combined with nearest neighbor algorithms will provide accurate results and recommendations.
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Real-time Data Processing Application
In this project, you will work on processing high volumes of data quantities in real time with high accuracy in results.
Bustle is a real-world example that processes large volumes of site metric data in real time using AWS. You can use Amazon Kinesis Stream and AWS Lambda to work on this project. You will be required to create a Kinesis Stream in the initial step. It will be essential for you to configure it to capture data from a web source. Several Lambda function instances will be scaled up or down automatically with the scaling of the stream. You can use social media timelines or location-based data as your data sources.
You can integrate Kinesis and AWS Lambda in either of the three formats, stream-based model, synchronous invocation model, or event structure model.
Use Lex to Create Chatbots
In the present age where customer centricity matters the most, chatbots have been a great help to organizations. Chatbots not only provide quick answers to customer queries but also help in enhancing customer experience and reduce costs.
Chatbots have played an integral role in providing quick business solutions. As a result, around 58 percent of B2B companies and 42 percent of B2C companies use chatbots on their websites. Learn more about it from our Aws chatbot tutorial.
Businesses use chatbots to provide quick answers to questions and sometimes to resolve complaints. In this project, you will use Amazon Lex to build a chatbot. Amazon Lex is a service that simplifies chatbot building for developers. It offers one-click deployment, so when you’ve created a bot, you can add it to multiple platforms. It eases the process of building a chatbot that communicates naturally as you will only have to add a few phrases and samples to train the model. Moreover, you can easily integrate Amazon Lex with other AWS services such as AWS Lambda.
Creating a Personalized News Feed
The goal is to create a personalized news feed based on the preferences and previous search and browsing history. Google uses this functionality to show the suggested articles in the mobile browser based on the search and browsing history. You can use AWS DynamoDB and AWS Lambda to come with a personalized content delivery platform. You will be required to extract information from user touchpoints. DynamoDB stores information for the application. Data stored and Lambda functions are the platforms to develop user profiles. Associated parameters enable the creation of customer feed.
These are some AWS Lambda projects and ideas for beginners. We hope this blog will help you build your own project.
In case you have any questions on AWS Lambda, do reach out to us on our AWS community.