In this blog post we'll show you how you can use Amazon Translate, Amazon Comprehend, Amazon Kinesis, Amazon Athena, and Amazon QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. These conversations are a low-cost way to acquire leads, improve website traffic, develop customer relationships, and improve customer service. In this blog post, we'll build a serverless data processing and machine learning (ML) pipeline that provides a multi-lingual social media dashboard of tweets within Amazon QuickSight. We'll leverage API-driven ML services that allow developers to easily add intelligence to any application, such as computer vision, speech, language analysis, and chatbot functionality simply by calling a highly available, scalable, and secure endpoint. These building blocks will be put together with very little code, by leveraging serverless offerings within AWS.
In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve it as a web application using Google Kubernetes Engine. If you haven't heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use the same machine learning pipeline and Flask app that we built and deployed previously. This time we will demonstrate how to containerize and deploy a machine learning pipeline serverless using AWS Fargate. This tutorial will cover the entire workflow starting from building a docker image locally, uploading it onto Amazon Elastic Container Registry, creating a cluster and then defining and executing task using AWS-managed infrastructure i.e.
In the current age of cloud computing, there is now a multitude of mature services available -- offering security, scalability, and reliability for many business computing needs. What was once a colossal undertaking to build a data center, install server racks, and design storage arrays has given way to an entire marketplace of services that are always just a click away. One leader in that marketplace is Amazon Web Services, which consists of 175 products and services in a vast catalog that provides cloud storage, compute power, app deployment, user account management, data warehousing, tools for managing and controlling Internet of Things devices, and just about anything you can think of that a business needs. AWS really grew in popularity and capability over the last decade. One reason is that AWS is so reliable and secure.
Are there AWS cloud services you haven't heard of? By now, every IT professional has heard of popular Amazon Web Services (AWS) offerings like Elastic Cloud Compute (EC2) and Simple Storage Service (S3). However, with well over a hundred cloud services available, Amazon has plenty of other Coud offerings that sometimes fly under the radar. The latest data from Synergy Research Group shows that spending on infrastructure as a service (IaaS), platform as a service (PaaS) and hosted private cloud climbed by an impressive 46 percent in the last quarter of 2017, and Amazon brought in most of that money. According to the report, "AWS maintained its dominant position with revenues that exceeded the next four closest competitors combined."
Cloud computing in general, and Infrastructure-as-a-Service (IaaS) in particular, have become widely accepted and adopted paradigms for computing with the offerings of virtual machines (VM) on demand. By 2020, 67% of enterprise IT infrastructure and software spending will be for cloud-based offerings.16 A major factor in the increased adoption of the cloud by enterprise IT was its pay-as-you-go model where a customer pays only for resources leased from the cloud provider and have the ability to get as many resources as needed with no up-front cost (elasticity).2 Unfortunately, the burden of scaling was left for developers and system designers that typically used overprovisioning techniques to handle sudden surges in service requests. Studies of reported usage of cloud resources in datacenters19 show a substantial gap between the resources that cloud customers allocate and pay for (leasing VMs), and actual resource utilization (CPU, memory, and so on). Serverless computing is emerging as a new and compelling paradigm for the deployment of cloud applications, largely due to the recent shift of enterprise application architectures to containers and microservices.23 Using serverless gives pay-as-you-go without additional work to start and stop server and is closer to original expectations for cloud computing to be treated like as a utility.2 Developers using serverless computing can get cost savings and scalability without needing to havea high level of cloud computing expertise that is time-consuming to acquire. Due to its simplicity and economical advantages, serverless computing is gaining popularity as reported by the increasing rate of the "serverless" search term by Google Trends. Its market size is estimated to grow to 7.72 billion by 2021.10 Most prominent cloud providers including Amazon, IBM, Microsoft, Google, and others have already released serverless computing capabilities with several additional open source efforts driven by both industry and academic institutions (for example, see CNCF Serverless Cloud Native Landscapea). From the perspective of an IaaS customer, the serverless paradigm shift presents both an opportunity and a risk.