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AWS SageMaker – Machine Learning

AWS SageMaker – Machine Learning

Our introduction to Machine Learning (click here) Blog

Before discussing what actually an AWS SageMaker is and what it does, it is essential to understand the concept and working of AWS. The Amazon Web Services (AWS) is a subsidiary of Amazon.com which offers individuals, businesses and governments access to on-demand cloud computing platforms on a paid subscription basis. AWS allows its users to acquire a virtual collection of computers that can be accessed through the Internet, these virtual computers imitate most functions of a physical computer. AWS performs various functions such as database storage, compute power, content delivery, machine learning and other functions. Certainly, Amazon Web Services provide reliable, flexible, affordable and scalable cloud computing services allowing its users to create sophisticated applications. The users pay according to the usage and number of virtual computers, and not for all the functions. Indeed, AWS offers a multitude of products relevant to machine learning, which is an algorithm designed to measure quality, develop the model, tune and integrate the application.

After understanding AWS, it will now be easier to understand the concept of AWS SageMaker in machine learning.  An Amazon SageMaker is a platform tailor-made for workflows of Machine Learning. It is a fully-managed platform which permits the developers and data scientists to construct, manage, train and organize machine learning models of all scale quite speedily and easily. Amazon SageMaker is capable of eradicating barriers that slow down the usage of machine language for developers. Moreover, AWS SageMaker is designed with a mindset allowing the machine learning community to seamlessly adopt it as well as to stress-free deploy various trained models to production-ready hosted environments. It also offers Jupyter, which is an integrated authoring notebook instance designed to provide easy access to the data sources in order to perform proper exploration and analysis, without the management of several servers. Furthermore, it allows common and standard optimized machine learning algorithms to run effectively against large data within a distributed infrastructure. It definitely provides a flexible distributed training choices allowing its users to adjust their workflows through native support of algorithms and frameworks. Deployment of a machine learning model into an encrypted, scalable and secure environment through single click launch using the Amazon SageMaker.

Undeniably, most developers find machine learning harder than other languages due to the process of building and training models along with complex and slower deployment of it into the production function.

For using machine learning, it is essential to first collect and prepare the training data that highlight important data sets. Selecting the framework and algorithm to be used is the second phase. Once a decision has been made about the approach, the training of models to provide predictions begin, which is a daunting task as it involves a lot of compute. It is important to tune the model in order to gain the best possible forecast and predictions, which is a manual and tedious task. Once a fully trained model is developed, it is vital to integrate the model with the application and deploy it on the infrastructure which will conduct the scaling. Unquestionably, these steps are again quite tedious as it requires access to large compute & storage, time and specialized expertise in order to optimize every part of the model. Hence, Amazon SageMaker comes in handy as it eliminates the complexity for the developer. The Amazon SageMaker encompasses various modules which are compatible with other machine learning models along with having the capability to be used independently.

Amazon SageMaker works in a three step process of build, train and deploy. The first step is “build” where Amazon SageMaker makes easy to construct various machine language models and also gets them prepared for training by providing all the things needed. It also connects training data along with selecting and optimizing the framework and algorithm for the application. Furthermore, it helps its users to explore and visualize training data sets and connects them directly with the data in Amazon. Once you have completed the step build, the training begins, where the SageMaker allows single click training of the model through a management console. It definitely manages the entire infrastructure making the scaling and training an easy and fast task. Also, it automatically tunes the model in an attempt to achieve the highest possible accuracy. In the last step, Amazon SageMaker deploys the algorithm and model into production in order to generate real-time predictions. Amazon SageMaker takes away the heavy lifting of machine learning, so building, training, and deploying machine learning models become easy and faster.

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1 Comment

  1. AWSCC Staff

    Great write up

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