Machine Learning in AWS

What is Cloud Computing?

Cloud computing is the on-demand delivery of IT resources over the Internet with pay-as-you-go pricing. Instead of buying, owning, and maintaining physical data centers and servers, you can access technology services, such as computing power, storage, and databases, on an as-needed basis from a cloud provider. One of the cloud provider is Amazon Web Services (AWS).

Cloud computing is an important term for all Data Science and Machine Learning Enthusiasts. It is unlikely that you may not have come across it, even as a beginner. But why is it important, one might ask. A simple explanation for this is that as the dataset expands, i.e., more samples and features are added, the machine learning model becomes more complex. Such models then demand more computational power, which is why most of us have encountered the famous ran-out-of-memory error for some of our notebooks.

Then, how do we deal with this limitation? A high-end piece of hardware like the expensive machines specifically for Machine Learning and Deep Learning might sound worth the investment, though always not possible. Well, Cloud Services is the answer.

What are the Benefits of Machine Learning in the Cloud?

1. No machine learning expertise required
   AWS offer many machine learning services that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.
2. Easy to scale up
   AWS allows you to scale your machine learning projects up and down as needed. You can start with a small set of data points and add more as you get more confident in your predictions.
3. Cost efficiency
   The cloud has a pay-per-use model. This eliminates the need for companies to invest in heavy working and expensive machine learning systems that they won’t be using always and every day. It saves us from buying expensive GPU cards to train and deploy large machine learning models, such as deep learning.
4. Easy Integration
   Most popular cloud services also provide SDKs (software developer kits) and APIs. This allows you to embed machine learning functionality directly into applications. They also support most programming languages.

Real Life Use cases-

Increase customer satisfaction with conversational AI interfaces

Conversational AI interfaces add human-like conversation capabilities to your business applications by combining different natural language technologies like natural language processing (NLP), natural language understanding (NLU) and natural language generation (NLG). Conversational interfaces continue to grow as one of the preferred ways for users to interact with businesses.
With Amazon’s Conversational AI (CAI) solutions, enterprises can use AWS AI Services or leverage AWS Partners’ expertise to build highly effective chatbot and voice experiences, increase user satisfaction, reduce operational costs, and streamline business processes all while speeding up time-to-market.

Maximize the value of media

The demand for media content in the form of audio, video and images is growing at an unprecedented rate. Organizations across entertainment, education, and advertising industries are using media to deeply engage with their audiences like never before.

However, managing, analyzing, and monitoring media content is both complex and expensive. Thousands of personnel hours are often spent tagging, labeling, captioning, and reviewing media assets. Adding machine learning to content workflows solves these problems and increases the lifetime value of your media.

AWS Media Intelligence solutions (AWS MI) solutions are a combination of services that empower you to easily integrate AI into your media content workflows. AWS MI allows you to analyze your media, improve content engagement rates, reduce production costs, and increase the lifetime value of media content. With AWS MI, you can choose turnkey solutions from participating AWS Partners or use AWS solutions to avoid starting from scratch.

Improve business metrics analysis

Tracking, monitoring, and analyzing the right business metrics are integral to the success of any business. Effective business data analysis lets you to learn from the past, monitor the present, and better plan for the future. But analyzing large amounts of business data to forecast their future values, or to detect outliers and understand the root cause, is complex, time-consuming, and not always accurate. Amazon’s Business Metrics Analysis ML solution leverages Amazon Lookout for Metrics and Amazon Forecast to solve these problems by using machine learning to analyze large volumes of data while dynamically adapting to changing business requirements.

Modernize your machine learning development process

Machine learning (ML) has become a core technology ingredient in a wide range of use cases from natural language processing and computer vision to fraud detection, demand forecasting, product recommendations, preventive maintenance, and document processing. Harnessing the benefits of machine learning at scale requires standardizing on a modern ML development process across your business. Modernizing your ML development process can accelerate your pace of innovation by providing scalable infrastructure, integrated tooling, healthy practices for responsible use of ML, a choice of tools accessible to developers and data scientists of all ML skill levels, and efficient resource management to keep costs low.

Importance of Machine Learning Tools of AWS

Amazon Web Services is the leading public cloud service provider and has a wide array of cloud services and technologies on offer. Therefore, you could also find AWS machine learning tools suited to your various enterprise requirements. AWS provides a wider and deeper variety of machine learning and AI services for different businesses.

The machine learning tools on AWS primarily aimed at helping customers in addressing critical challenges that restrict developers from leveraging the optimal power of machine learning. Users could select pre-trained AI services to address applications of forecasting, computer vision, recommendations, and language processing.

Which Machine Learning Tools Should I Use?

If you are implementing AI for the first time, then you should start with one of the specialized services. Designed as standalone applications or APIs on top of pre-trained models, each platform offers a range of specialty services that allow developers to add intelligent capabilities without training or deploying their own machine learning models. The main offerings in this category are primarily focused on some aspect of either image or language processing.

Following are some of the Machine Learning tools in AWS and its briefing-

• Amazon SageMaker
  Amazon SageMaker is always the obvious addition among machine learning solutions in the AWS marketplace. It is a fully-managed platform that helps data scientists and developers ensure the easier and faster building, training, and deployment of machine learning models at a different scale. Amazon SageMaker clips off all the barriers which generally slow down developers aspiring to use machine learning.
  Amazon SageMaker removes the complexity and helps developers understand and utilize the full potential of all steps in machine learning. The modular design of Amazon SageMaker makes it one of the most flexible machine learning tools on AWS. You can use the different modules together or independently for building, training, and deploying machine learning models.
• Amazon SageMaker Ground Truth
  Datasets are the lifeblood of machine learning, and Amazon SageMaker Ground Truth offers the platform for the development of training datasets for machine learning with higher accuracy and speed. SageMaker Ground Truth is one of the top AWS machine learning tools because it provides easy access to public and private human labelers. In addition, it also facilitates labelers with interfaces and in-built workflows for general labeling tasks.
  Most important of all, SageMaker Ground Truth can reduce labeling costs by almost 70% through automatic labeling. The effective use of machine learning for automatic data labeling offers better cost savings and productivity. The SageMaker Ground Truth model gradually becomes efficient over time through learning continuously from labels by human labelers. As a result, it can improve its capability for labeling more data automatically and contributing to faster training of datasets.
• Amazon Lex
  The next promising addition among Amazon machine learning tools is Amazon Lex. It is a service for developing conversational interfaces in any application through the use of voice and text. Lex offers the functionalities of advanced deep learning in the form of automatic speech recognition (ASR) for the conversion of speech to text. In addition, it also provides natural language understanding features for recognizing the intent in a text.
  As a result, it can enable the development of applications with highly interactive user experiences and almost real conversational interactions. Amazon Lex simplifies access to speech recognition and natural language understanding alongside presenting the power of Alexa to all developers. It is one of the leading technologies for the development of entirely new categories of products created only through conversational interfaces.
• AWS Inferentia
  One of the striking AWS machine learning tools is AWS Inferentia. It is a machine learning inference chip that aims at delivering higher performance at lower costs. AWS Inferentia offers support for Apache MXNet, PyTorch, and TensorFlow deep learning frameworks and models using the ONNX format. AWS Inferentia facilitates higher throughput, low latency inference performance at unbelievably low costs.
• Amazon Textract
  Amazon Textract is undoubtedly one of the productive Amazon machine learning tools. It is a service that extracts text and data automatically from scanned documents. Amazon Textract offers more than the capabilities of optical character recognition (OCR) and helps in the identification of content in the fields through forms and information stored in tables.
• Amazon Comprehend
  Amazon Comprehend is the foremost entry among AWS machine learning tools that comes to mind when you think of Natural Language Processing (NLP). It is an NLP service based on machine learning for finding insights and relationships between various attributes in text. Amazon Comprehend utilizes machine learning for discovering new insights and relationships in the available unstructured data.
  It can identify the language in the text and extract key phrases, events, places, brands, and people in a text. Amazon Comprehend utilizes tokenization and parts of speech for analysis of text and automatic organization of a set of text files according to the topic. The AutoML features in Amazon Comprehend can also help in creating a custom set of text classification models or entities built specifically according to an enterprise’s needs.
• Amazon Rekognition
  Amazon Rekognition is among the many common AWS machine learning tools that you can find at present. It is a service that helps in adding image analysis capabilities to different applications. Rekognition can help in the detection of objects, faces, and scenes in particular images. It can also help in searching and comparing faces.
  The Amazon Rekognition API provides the ease of adding advanced deep-learning-based visual search and image classification capabilities to applications. Amazon Rekognition leverages deep neural network models for the detection and labeling of multiple objects and scenes in images. As a result, you can find Amazon Rekognition as a vital tool for integrating powerful visual search and discovery functionalities into an application.
• Amazon Translate
  Amazon Translate is one of the productive AWS machine learning tools with the maximum potential of machine learning for users. It is a neural machine translation device for faster, affordable, and highly accurate language translation. Amazon Translate helps in localization of content such as applications and websites for international users. Its primary functionalities are evident in the easier translation of large volumes of text with the assurance of efficiency.

How is Hardware Impacted by Machine Learning Workloads?

• Machine learning workloads require greater processing power
• The amount of processing required could be expensive
• GPUs are the processor of choice for many ML workloads because they significantly reduce processing time
• Google and other companies are creating hardware that’s optimized for machine learning jobs
• To help people get started with AI, Amazon offers a camera that can run deep learning models

Hardware is an important consideration when it comes to machine learning workloads. Training a model to recognize a pattern or understand speech requires major parallel computing resources, which could take days on traditional CPU-based processors. In comparison, powerful graphics processing units (GPUs) are the processor of choice for many AI and machine learning workloads because they significantly reduce processing time.

Conclusion:

This article presents a comprehensive overview of what Cloud Services are, why these are essential for AI and Machine Learning requirements, and suggests an approach to selecting a service if you are a beginner. Although most of these vendors provide platforms for general-purpose AI and ML needs, a beginner still needs to choose a platform that is easy to use, requires no Cloud expertise to set up & run, offers better support and tools for Machine Learning including NLP, chatbots, or service bots as well as Neural Networks for Deep Learning.