With less than a week to go, the excitement and anticipation are building up for industry's largest cloud computing conference - AWS re:Invent. As an analyst, I have been attempting to predict the announcements from re:Invent (2018, 2017) with decent accuracy. But with each passing year, it's becoming increasingly tough to predict the year-end news from Vegas. Amazon is venturing into new areas that are least expected by the analysts, customers, and its competitors. AWS Ground Station is an example of how creative the teams at Amazon can get in conceiving new products and services.
We learned that the emerging ecosystem of containers, microservices, cloud scalability, devops, application monitoring, and streaming analytics is not a fad. It's the future, already powering Silicon Valley's and Seattle's most advanced tech companies. Throw in machine learning and IoT and you have a comprehensive framework for the next phase of enterprise IT, with continuous improvement as its founding principle. At the same time, we became more aware of the widening gulf between this new world and most existing enterprise IT operations. That's why the hoary phrase "digital transformation" refuses to die -- the leap from legacy to modernity requires profound, multiphase, across-the-board change.
Gone are the days when application development was the daunting task of the highly skilled developers backed with strong IT skills, low code application development has democratized app development and empowered a new generation of citizen developers. There was a time when app development was in the domain of people with complex coding and technical skills. We called these people by various names like programmers, coders, techies, and they usually worked in a world oblivious of the everyday priorities of the business world. However, with the passage of time, this scenario is much more democratized now. Newer business models have given rise to new technologies that are replacing old legacy systems and processes.
Forrester released a report on Monday establishing five cloud computing predictions for 2020. The predictions reveal the growing battle for cloud computing dominance, with major cloud vendors evolving and shifting tactics. These predictions will help CIOs and their organizations prepare for the growing cloud landscape, which continues shifting as industry cloud leaders develop. With the public cloud market--cloud apps (SaaS), cloud development and data platforms (PaaS), and cloud infrastructure (IaaS)--expected to reach $411 billion by 2022, Forrester outlined the following five future enterprise cloud shifts in its Predictions 2020: Cloud Computing report. Depending on the organization's needs, a company can rent any of these services from major cloud providers.
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.