AWS Lambda, Machine Learning on AWS and their use-cases/project ideas!!
This article contains information about what is “Lambda” service in AWS, how accurate predictions can be achieved using AWS machine learning and their real-world use-cases. Also, which top companies use Lambda effectively.
Before jumping on main topics, let’s understand what is AWS and Serverless in short ::
Amazon Web Services (AWS) ::
AWS is a secure cloud service platform created by Amazon organization. AWS can securely store all your files on the cloud so you can access them from anywhere, send bulk email to your customers, run web and application servers in the cloud to host dynamic websites and many more. It has tons of services for different industry requirements like EC2, cloudwatch, S3, Lambda, etc. You can have all control over any service you want but, you need to pay for it either hourly or monthly basis (depending on service) and some services are made free for limited time span.
What is Serverless?
Serverless is a concept in which applications can be build and run on cloud without been managed by humans, just requirement for that application should be crystal clear. In other words, your application runs on servers, but all the server management is done by AWS. It provides a way to remove architecture responsibilities from your workload, including provisioning, scaling, and maintenance. Scaling can be automatic, and you only pay for what you use.
Tons of AWS services are available to implement serverless concept. One of the serverless service is “Lambda”.
Lambda Service ::
AWS Lambda is an event-driven serverless computing platform which means that it runs code in response to events, while automatically taking care of all the back-end infrastructure and admin that is needed to run your code. All the developer needs to focus on is their code. The rest is taken care of “under the hood” by AWS.
In other words, this service allows you to run functions in the cloud. It’s a huge cost saver as you pay only when your functions execute.
Lambda work flow ::
For instance, you provide your code to lambda, what actually happens is a container is created, deployed and managed by AWS. You don’t need to manage the container as lambda is a serverless service, even though, you don’t have the power/access to change the container’s behaviour. This container is simply a registry/location for your code.
Programming languages supported by AWS Lambda ::
- Java
- Go
- NodeJS
- Python
- Ruby
- C#
AWS Lambda Use Cases or project ideas ::
1} Massive real-time data processing
We all know data can stream in from communication devices or from user input devices. Sometimes, when your application may need to handle large volumes of streaming input data, and moving that data to temporary storage for later processing may not be an adequate solution.
Hence, if you send the data stream to an AWS Lambda application designed to quickly pull and process the required information, you can handle the necessary real-time tasks without slowing down your main application.
2} Log analysis while in motion
Simple lambda function can be created to check log files from Cloudwatch. Lambda can search in the logs looking for specific events or log entries as they occur and send out notifications via SNS.
3} Operate Serverless websites
As discussed earlier, lambda is a serverless service and completely managed by AWS so, web frontend can send to Lambda function using API gateway through endpoints like https or http. Lambda can handle application code and data in database service (DynamoDB) as well. Also, a fixed cost will be applicable for running database service and can pay if traffic incurred.
4} Backups
Lambda event can be used for housekeeping within AWS accounts. Creating backups, checking for idle resources, generating reports and other tasks which frequently occur can be implemented in no time under AWS lambda.
5} Processing uploaded S3 objects
Integration of S3 with lambda is one of the best application and has a lot of advantages. You can start processing your code/files with lambda as soon as something is landed onto S3 bucket. For example, Image thumbnail generation with AWS Lambda, it’s cost-effective and no scaling up would be necessary.
Which company/start-up use AWS Lambda Service?
I} Netflix
Netflix uses AWS services (especially Lambda) very frequently. We all know, Netflix handles petabytes of video and other data types that need to be streamed to its 50 million customers globally. 50,000 instances in 12 zones and 40–50% of those instances are turn on/off daily. Scaling is the big picture for it and hence, AWS provides it.
Netflix uses Lambda to execute rule-based checks and routing to handle data stored in S3. Whenever a piece of data is written to S3, a Lambda function decides which data needs to be backed up, which needs to be stored offsite.
For security, Netflix uses Lambda functions to check if new instances created by various services are secure, if wrong configurations are spotted then instance is shut down automatically.
II} Localytics
Localytics is a mobile app analytics startup and uses Lambda service very effectively. Localytics leverages serverless framework to manage flow of code across pipeline. Here, lambda is used to leverage other AWS services depending on need and accomplish complex cross-service tasks that would otherwise take a lot of effort.
Machine Learning on AWS ::
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. AWS helps you at every stage of your ML adoption journey with the most comprehensive set of artificial intelligence (AI) and ML services, infrastructure, and implementation resources. According to me, most crucial and time-consuming part of the job is defining the problem and building a meaningful dataset, i.e. ::
- Making sure you know what you are going to classify/predict
- Collecting as much data as you can about the context without making too many assumptions on what is and isn’t relevant
AWS Machine Learning is quite powerful in this regard because it automatically trains and tests a lot of complex models, tuned with different parameters so that the best one will be chosen for the final evaluation. In fact, this will generally be the one you would usually end up figuring out by hand through trial and error. Of course, AWS machine Learning will also handle all of your input normalization, dataset splitting, and model evaluation work. In fact, as long as you provide a valid data source, AWS Machine Learning can solve most of your low-level problems.
Machine Learning use-cases or project ideas ::
1} AI for DevOps
Today’s world is all about automation and DevOps has excellent tools for automation such as Docker, Kubernetes, Ansible, Jenkins, etc. AI for DevOps is the shift towards more automation and more proactive mechanisms that enable teams to innovate faster with confidence. Designed to augment developer’s expertise with ML capabilities, AI for DevOps is a journey from manual processes with infrequent deployments and slow innovation cycles to rapid iteration cycles with CI/CD, and automated alarming for monitoring production. This use case would be helpful to ::
1. Improve code quality with continuous monitoring
2. Optimize application performance and reduce cost
2} Fraud Detection
Now-a-days, everything is almost reached to virtualization (online mode). Lots of things like tons of money is lost to online fraud. With AWS Fraud Detection machine learning solutions, companies can proactively and more accurately detect and prevent online fraud. These solutions will help reduce revenue losses, avoid brand damage, and provide a frictionless customer online experience while adapting to changing threat patterns. This use case would be helpful to ::
1. Prevent and detect online fraud in real-time
2. Give fraud teams more control
Conclusion
AWS provides a massive global cloud infrastructure that allows you to quickly innovate, experiment and iterate with all necessary services.
Keep Learning
Keep Growing
Thank YOU…!!!
