1. Overview of Artificial Intelligence & Machine Learning
Artificial intelligence (AI)/Machine Learning (ML) business potential has been in the future for a long time. However, many organizations have found AI challenging to integrate. Scientists soon realized that teaching computers to learn was more efficient than coding them with every skill required to complete a task. Furthermore, the human race is considered superior due to its decision-making capabilities which could also be mimicked by deep learning, a subset of Ml.
Machine learning applications such as voice recognition improved medical diagnostics, and even self-driving automobiles are all the rage these days. However, machine learning and deep learning have immense economic promise. So, why hasn’t AI been implemented in every business? First, it can be challenging to implement independently for a single day. It can be challenging to envision use cases, hire a team to execute them, and then figure out how to integrate AI into a business operation. Second, it requires a lot of computing power. And it’s pricey. Purchasing and maintaining the compute resources needed to implement deep learning. AWS has launched many pre-trained services that can leverage to achieve the business objective.
2. Advantages & Use Cases of AI/ML
Although AI/ML has many applications, only a few are included in the below diagram:
Customers may create AI/ML systems that process and respond to data intelligently, frequently in near real-time. It allows enterprises to do more by increasing their speed and efficiency.
Predictive Maintenance is one of the most used use cases, and the entire 4.0 industrial revolution would be impossible to achieve without AI/ML. Artificial Intelligence may be viewed as a miracle that could save countless lives in the healthcare industry. The most valuable health domains where AI may help are disease diagnosis and drug research.
3. Benefits of AWS AI and ML
Amazon makes extensive use of AI services. Amazon’s AI skills are aimed to deliver personalized recommendations to its consumers, from utilizing AI to forecast the number of customers eager to buy a new product to running a cashier-less grocery store. According to research, Amazon’s recommendation engine is responsible for 35% of the company’s overall sales. Without question, Amazon is at the forefront of all the industries it has worked in due to its adaptable approach to learning. All the services that were previously only offered to well-established businesses are now open to small businesses.
4. Amazon Artificial Intelligence Service Overview
The above image, “AWS AI SERVICES,” depicts the central division of AWS AI services, which can be used to produce AI solutions in the shortest possible time without being an expert in the following domain. We can split AWS AI services into two categories: Core and Specialized. Core Services can be thought of as all-purpose services that any industry can use for any application. Specialized services have already been established in their purpose or use case, which was created for a specific mission.
5. Core Services:
This can be further classified into four categories: Vision, Text, chatbot & Speech. These Core categories are further explained below –
Amazon Lex – Conversational AI can be used to create chatbots.
Amazon Lex uses the same technology as used by Alexa for speech recognition and language understanding. Lex interfaces with Lambda, allowing you to trigger functions in the back-end. The bot can easily deploy on hardware devices, mobile clients, and platforms for chatting once it’s been constructed. Reports provided by Lex could be used to track bot metrics. Amazon Lex is a fully managed scalable service for creating, deploying & monitoring bots.
Amazon Rekognition – Amazon Rekognition extracts information and insights from your images and videos using pre-trained and configurable computer vision (CV) capabilities. Visual analysis service based on deep learning Search, verify and analyze millions of images and videos
3. AWS Panorama
Without uploading photos to the AWS Cloud, the AWS Panorama Appliance allows you to operate self-contained computer vision applications at the edge. You may interact with other AWS services and utilize them to track data from the application over time by utilizing the AWS SDK.
Amazon Comprehend – Natural Language Processing and Text Analytics. Easily extend and understand useful information from documents’ text.
Extract printed text, handwriting, and data from any document automatically using machine learning.
Amazon Translate is a neural machine translation service that provides language translation that is quick, high-quality, inexpensive, and configurable.
Amazon Polly – Amazon Polly is a service that converts text into lifelike voice, allowing you to create talking apps and new categories of speech-enabled devices.
Amazon Transcribe – Convert speech to text automatically. Audio input into Amazon Transcribe would be used to receive transcripts. It is possible to increase accuracy by filtering the content.
Note: Stay tuned for my follow up blog on AWS Artificial Intelligence Specialized Service
6. AWS Machine Learning Services Overview
Amazon SageMaker is a machine learning service that Amazon wholly manages. Data scientists and developers can use SageMaker to construct and train machine learning models fast and easily, then deploy them directly into a production-ready hosted environment. You don’t have to manage servers because it has an integrated Jupyter publishing notebook instance for easy access to your data sources for exploration and analysis.
Some of the main Amazon SageMaker features are depicted in the following diagram
The need for AI-integrated businesses is growing by the day, but the degree of skills required to construct services from the ground up is in short supply. AWS offers comprehensive AI/ML capabilities that may be readily integrated into any application, even if you don’t have a skilled team. Instead of developing an AI service to meet business needs, using AWS’s pre-trained services would be more time-efficient. Because of cloud AI services, AI-powered applications are becoming increasingly widespread.
8. About CloudThat
CloudThat is the official AWS Advanced Consulting Partner, Microsoft Gold Partner, and Training partner helping people develop knowledge on cloud and help their businesses aim for higher goals using best in industry cloud computing practices and expertise. We are on a mission to build a robust cloud computing ecosystem by disseminating knowledge on technological intricacies within the cloud space. Our blogs, webinars, case studies, and white papers enable all the stakeholders in the cloud computing sphere.
If you have any queries about Amazon Machine Learning Services, Artificial Intelligence on AWS, or anything related to AWS services, feel free to drop in a comment. We will get back to you quickly. Visit our Consulting Page for more updates on our customer offerings, expertise, and cloud services.
Q1. What is the difference between Amazon Lex & Polly?
Answer – Amazon Lex is using both voice & text to build conversational interfaces for chatbots, while AWS Polly is used to converting text inputs into speech.
Q2. How to use Amazon Rekognition in advertisements?
Answer – Amazon Rekognition could be integrated with recommendation systems to display person-oriented advertisements on waiting for queue or lounge. Amazon Rekognition could extract key features related to individuals using cameras that could be fed to the recommendation system later the same be displayed on the screen.
Q3. What is the best Amazon Machine Learning Course?
This course comprises presentations, group exercises, demonstrations, and hands-on labs. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.
Learn more about AWS Certification here.