Amazon and a lot of cloud vendors such as Microsoft and Google have services around machine learning (ML), artificial intelligence (AI), and virtual assistants. A popular one that might come to mind is Amazon Alexa, something I’ve written quite a few tutorials around over the years.
The concept around Alexa is simple. Provide the Alexa service some audio, have that audio converted into text or some other format that can be evaluated, execute some code, and respond with something to be spoken to the user. However, what if you didn’t necessarily want to use a virtual assistant with audio, but integrate as part of a chat application in the form of a chatbot?
In this tutorial we’re going to look at using Amazon Web Services (AWS) Lex, which is a service for adding conversational interfaces to your applications. If you’re coming from an Amazon Alexa background, the concepts will be similar as AWS Lex shares the same deep learning technologies.Read More
While I haven’t done too much with Serverless Framework and Functions as a Service (Faas) recently, I did in the past and it isn’t something that I’ve forgotten. In the past I demonstrated how to deploy Node.js functions to Amazon Web Services (AWS) Lambda that contain native dependencies. While not a necessity for all Lambda functions, it is for functions that use libraries for specific operating systems and architectures. For example, my previous article titled, Use AWS Lambda and API Gateway with Node.js and Couchbase NoSQL, fell into this situation. Making use of an EC2 instance or a Docker container with Amazon Linux will help most of the time, but there are scenarios where a little bit extra must be done to accomplish the task.
In certain circumstances everything may package and deploy correctly, but still throw errors. For example, a common error is around libstdc++ and a version of GLIBCXX not being found.
In this tutorial we’re going to see how to resolve library errors that might not be caught in a typical packaging and deployment scenario with Serverless Framework and AWS Lambda.Read More
It has been a few years since I last worked on and published an application, otherwise known as a Skill, for Alexa powered voice assistants. My last Skill titled, BART Control, was built out of necessity because of my commuting on the Bay Area Rapid Transit system. While I didn’t open source it, I had created the Skill with Node.js and a publicly available BART web service. Since then I had written a tutorial titled, Create an Amazon Alexa Skill Using Node.js and AWS Lambda, which also focused on Node.js.
I’m a huge fan of Golang and was pleased to see that AWS Lambda recently started to officially support it. AWS Lambda isn’t a requirement to creating Alexa Skills, but it is a huge convenience. To make things even better, Amazon recently sent me an invitation to take part in their developer offer to receive an Amazon Echo Show for publishing another Skill. The offer and Golang inspired me to develop another Skill and this time I wanted to share my process.Read More
I was recently working on a Functions as a Service (FaaS) project using AWS Lambda and Node.js. However, I was running into an issue where my package dependencies found in my node_modules directory were for the wrong platform once deployed to Lambda. This is not the first time I experienced a problem like this. I knew the issue straight away because I encountered the same thing when trying to use a node_modules directory generated on Mac from a Windows computer.
When uploading a package developed with Node.js to AWS Lambda, the package.json file is not considered. Instead you are uploading a package that contains the node_modules directory and all dependencies. So how do you develop for AWS Lambda from Mac and Windows, but have it work once deployed?
We’re going to see how to use Docker to get our Node.js FaaS project dependencies designed for Amazon’s flavor of Linux.Read More
Not too long ago I had written about creating an API with Node.js and Express that accepted image uploads and manipulated the images to be Android compliant before returning them in a ZIP archive. This article was titled, Create an Android Launcher Icon Generator RESTful API with Node.js, and Jimp, and it was a great example of creating APIs that that did most of their work in memory. I even demonstrated how to containerize the application with Docker.
Applications that manipulate media will need to be able to scale, otherwise there is a risk of the application crashing from not enough resources, or too many resources can get expensive. For this reason, it makes perfect sense to take the previous example serverless with Amazon’s Lambda and API Gateway offerings.
We’re going to see how to use API Gateway to accept HTTP requests with binary image data and process that data with Lambda to return various sized Android launcher images packaged in a ZIP archive.Read More
By now you’re probably aware that I’m all about Amazon Alexa skills since I’m a proud owner of an Amazon Echo. I had released a Alexa skill called BART Control and published a guide on creating a simple skill with Node.js and Lambda. If you went through my Node.js and Lambda guide you probably found it pretty painful to test the skill you were working on. The constant building and uploading to Lambda could easily get out of control. What if I told you there was a much simpler way that could save you a ton of time?
We’re going to take a look at adding test cases for testing an Alexa skill offline without ever having to upload the skill to Lambda.Read More