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We all know the latest trend in today’s technology and how Machine Learning is changing the way business decisions are made. Machine learning replaces old manual repeatable processes and provides the systems the ability to get into a mode of self-learning without being explicitly programmed.
There are many online resources that provides enough information on machine learning, developing ML models, algorithm selection, validation and evaluation.
While most of the time and resources are spent on developing models to achieve desired results, allocating additional computational resources to setup an infrastructure to replicate these models on a production environment can be a difficult task.
Machine learning models can be deployed into production in a wide variety of ways. However, AWS Lambda proves to be a suitable candidate when it comes to providing a scalable infrastructure to replicate the models.
AWS Lambda is a serverless computing service that executes your code based on the events from a user application and manages the compute resources for that application automatically. Here is a basic AWS lambda architecture.
The core components of AWS Lambda are Lambda functions and event sources. An event source can be any AWS service, or a user created application that publishes events and a lambda function is the custom code that processes the events. A lambda function is invoked automatically when the event sources trigger the function through Invoke operation.
The first step is to train your model based on the use case. You can also import open source models from Keras or TensorFlow. The most easy and efficient way is to use an existing model and fine tune them for your use case. TensorFlow is an open source library for high performance numerical computation that helps training models faster and easier whereas Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow.
Model Source: https://github.com/tensorflow/models
The next step is to upload your models to Amazon S3. Machine Learning models can also be created using Amazon ML tools without having to learn complex ML algorithms and technology.
Now since we’ve imported our ML models it’s now time to create a lambda function which can be invoked when an object is created in Amazon S3.
When a user uploads an object to the source bucket Amazon S3 detects an object-created event. Then it publishes this event to AWS Lambda by invoking the lambda function and passing event data as a function parameter.
There are other platforms and systems that provide a structured way to deploy your ML models. We’ve chosen to deploy our TensorFlow models with AWS Lambda because:
Stay tuned for our next article in which we’ll be exploring the lambda deployment limits and how to overcome these limitations.
Dashbird provides failure detection and alert for enterprise level applications and thus help in reducing the cost of failure of these applications to a very large extent.
In this guide, we’ll talk about common problems developers face with serverless applications on AWS and share some practical strategies to help you monitor and manage your applications more effectively.
Today we are announcing a new, updated pricing model and the end of free tier for Dashbird.
In this article, we’re covering 4 tips for AWS Lambda optimization for production. Covering error handling, memory provisioning, monitoring, performance, and more.
Dashbird was born out of our own need for an enhanced serverless debugging and monitoring tool, and we take pride in being developers.
Dashbird gives us a simple and easy to use tool to have peace of mind and know that all of our Serverless functions are running correctly. We are instantly aware now if there’s a problem. We love the fact that we have enough information in the Slack notification itself to take appropriate action immediately and know exactly where the issue occurred.
Thanks to Dashbird the time to discover the occurrence of an issue reduced from 2-4 hours to a matter of seconds or minutes. It also means that hundreds of dollars are saved every month.
Great onboarding: it takes just a couple of minutes to connect an AWS account to an organization in Dashbird. The UI is clean and gives a good overview of what is happening with the Lambdas and API Gateways in the account.
I mean, it is just extremely time-saving. It’s so efficient! I don’t think it’s an exaggeration or dramatic to say that Dashbird has been a lifesaver for us.
Dashbird provides an easier interface to monitor and debug problems with our Lambdas. Relevant logs are simple to find and view. Dashbird’s support has been good, and they take product suggestions with grace.
Great UI. Easy to navigate through CloudWatch logs. Simple setup.
Dashbird helped us refine the size of our Lambdas, resulting in significantly reduced costs. We have Dashbird alert us in seconds via email when any of our functions behaves abnormally. Their app immediately makes the cause and severity of errors obvious.