The Real Pay As You Go - AWS Lambda

My Serverless Computing post received queries about AWS Lambda and microservices.  AWS Lambda  is not microservice but you can create Microservices using AWS Lambda function and API Gateway that would trigger the function (if you are familiar with AWS services). Anyway for now lets understand AWS Lambda

What is AWS Lambda?

AWS Lambda is  famous in serverless world. Amazon Web Services did not invent serverless but their Lambda service has popularized concept of Serverless Computing. So whats AWS Lambda?

AWS Lambda is an event-driven, serverless computing platform provided by Amazon as a part of Amazon Web Services. It is a computing service that runs code in response to events and automatically manages the computing resources required by that code. It was introduced in November 2014.

A simple case for AWS Lambda

• Take example of a website hosted on AWS EC2  with auto scaling enabled
  
• The website is accessed by 100 concurrent users
• At 12pm PST the administrator starts to uploads 100 videos to the server or starts a batch job for processing. 
• This increases the load on the EC2 server, and triggers the auto-scaling feature, EC2 provisions more number of instances to meet this requirement. 
• From triggering auto-scaling to provision more instances takes X milliseconds duration which eventually results in slow actions on the website when the initial spike in the task is received. 
• This problem of slow response can be solved using distributed computing and allocating dedicated instance for website and 2nd dedicated EC2 for running the back-end code. 
• When the users are reading blog on the website & the back-end job is running in parallel the performance is not affected. However, video processing still takes a lot of time, as the load increases, because auto-scaling takes comparatively more time on EC2.
• A better solution is a stateless system called AWS Lambda

AWS Lambda Features

AWS Lambda computing services is event-driven & can be triggered by an message or email 

AWS Lambda computing services is serverless. 

AWS Lambda is stateless & runs process as background tasks in most efficient manner.

AWS Lambda Components

AWS Lambda is one of the computing services provided by AWS, which is event-driven and serverless. It is a stateless serverless system that helps us run our background tasks in the most

• Lambda function: Whatever custom codes and libraries that we create are called a function.
• Event source: Any AWS or custom service that triggers our function and helps in executing its logic
• Log streams: Lambda monitors each function and its metric can be viewed on CloudWatch, Developer can code our function in a way that it provides us custom logging statements to let us analyze the flow of execution and performance of each function.

 

AWS Lambda Working

 AWS Lambda

 

AWS Lambda Key Benefits

• No need for provisioning server - Due to its serverless architecture
• No need to set up any virtual machine (VM)
• Distributed Event Driven computing and highly scalable service allows developers to run and execute codes in response to events
• Cost Benefits of - just pay for the compute time taken and only when the code runs. Also, pay only for the used memory and the number of processed code requests, and the code execution time is rounded up by 100 milliseconds.
• Allows code performance monitoring in real time through Cloud Watch
  

AWS Lambda Limitations

By design AWS Lambda is not for long running tasks and it has few limitations & drawback

• The maximum execution duration per request is set to 300 seconds
• The maximum disk space provided is 512 MB for the runtime environment
• Memory volume varies from 128 MB to 1,536 MB
• Event request body cannot exceed more than 128 KB
• Code execution timeout is only 5 minutes
• Lambda functions write their logs only to CloudWatch, which is the only tool available  to monitor or troubleshoot our functions

 

Why is Natural Language Processing making such advances?

According to Wikipedia the field of study that focuses on the interactions between human language and computers is called natural language processing, or NLP for short. It sits at the intersection of computer science, artificial intelligence, and computational linguistics. There are other definitions that you can Google and find so lets jump to what is NLP and why is it making rapid advances in field of Artificial Intelligence.

Natural Language Processing

Natural language processing (NLP) is a field of artificial intelligence in which computers analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic stigmatization, translation, named entity recognition, relationship extraction, speech recognition, and topic segmentation.

“Apart from common word processor operations that treat text like a mere sequence of symbols, NLP considers the hierarchical structure of language: several words make a phrase, several phrases make a sentence and, ultimately, sentences convey ideas,” John Rehling, an NLP expert at Meltwater Group, says in How Natural Language Processing Helps Uncover Social Media Sentiment. “By analyzing language for its meaning, NLP systems have long filled useful roles, such as correcting grammar, converting speech to text and automatically translating between languages.”

NLP is used to analyze text, allowing machines to understand how humans speak. This human-computer interaction enables real-world applications like automatic text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, relationship extraction, stemming, and more. NLP is commonly used for text mining, machine translation, and automated question answering.

NLP is characterized as a difficult problem in computer science. Human language is rarely precise, or plainly spoken. To understand human language is to understand not only the words, but the concepts and how they’re linked together to create meaning. Despite language being one of the easiest things for the human mind to learn, the ambiguity of language is what makes natural language processing a difficult problem for computers to master.

Where can Natural Language Processing be used?

NLP algorithms allow developers and businesses to create software that understands human language. Due to the complicated nature of human language, NLP can be difficult to learn and implement correctly. However, with the knowledge gained from this article, you will be better equipped to use NLP successfully, no matter your use case.

Examples of natural language processing

NLP algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences), and making a statistical inference. In general, the more data analyzed, the more accurate the model will be.

Natural Language Processing application in business use cases

Natural language processing has a wide range of applications in business.

As just one example, brand sentiment analysis is one of the top use cases for NLP in business. Many brands track sentiment on social media and perform social media sentiment analysis. In social media sentiment analysis, brands track conversations online to understand what customers are saying, and glean insight into user behavior.

“One of the most compelling ways NLP offers valuable intelligence is by tracking sentiment — the tone of a written message (tweet, Facebook update, etc.) — and tag that text as positive, negative or neutral,” according to Rehling.

Similarly, Facebook uses NLP to track trending topics and popular hashtags.

“Hashtags and topics are two different ways of grouping and participating in conversations,” Chris Struhar, a software engineer on News Feed, says in How Facebook Built Trending Topics With Natural Language Processing. “So don’t think Facebook won’t recognize a string as a topic without a hashtag in front of it. Rather, it’s all about NLP: natural language processing. Ain’t nothing natural about a hashtag, so Facebook instead parses strings and figures out which strings are referring to nodes — objects in the network. We look at the text, and we try to understand what that was about.”

It’s not just social media that can use NLP to its benefit. There are a wide range of additional business use cases for NLP, from customer service applications (such as automated support and chatbots) to user experience improvements (for example, website search and content curation). One field where NLP presents an especially big opportunity is finance, where many businesses are using it to automate manual processes and generate additional business value.

Other practical uses of NLP include monitoring for malicious digital attacks, such as phishing, or detecting when somebody is lying. And NLP is also very helpful for web developers in any field, as it provides them with the turnkey tools needed to create advanced applications and prototypes.

Artificial Intelligence (AI) in healthcare industry - What to look for in 2021 and 2022 ?

Overview of AI in Healthcare 

Chart : https://www.statista.com

Artificial Intelligence and Machine Learning systems proved that machines are better and faster than humans analyzing big data. Today, organizations have large datasets of patient data and insights about diseases through techniques like Genome Wide Association Studies (GWAS). Using AI, healthcare providers can analyze and interpret the available patient data more precisely for early diagnosis and better treatment. Today, it is possible to say whether a person has the chance to get cancer from a selfie using computer vision and machine learning to detect increased bilirubin levels in a person’s sclera, the white part of the eye. 

Why AI in Healthcare?

1. Better patient care: AI can provide better patient care by detecting diseases earlier and offering more efficient treatment methods. According to Frost & Sullivan’s research, AI has the potential to improve healthcare outcomes by 30 – 40%.
2. Data-driven decision-making: With machine learning algorithms, AI can document and offer more insights about a patient’s status and help doctors make better data-driven decisions by providing a better picture.
3. Time & cost saving for administrative tasks: AI can handle administrative tasks like patient registration, patient data entry, and doctor scheduling for appointment requests.

AI Healthcare Market

In 2016 AI healthcare market was estimated to be around $0.66 B 

In 2021 AI market was expected to grow by 10 times to $6.7 billion. 

In 2019 estimate is 41.7% compound annual growth rate to $1.3 billion 

In 2025 the AI healthcare market is projected to be $13 billion

Over the last 4 years there has been a horizontal expansion in areas of implementation in healthcare. Companies like Cerner Corporation, IBM, McKesson Corporation ,GE Healthcare, Telstra Health, HotDocs, Health Engine are leading the healthcare revolution across the world.

So what are the areas where AI is  revolutionizing healthcare industry? Here is my list of top 18 areas of focus for AI Healthcare across the world in 2021-2022

1- Assisted or automated diagnosis & prescription: Chatbots can help patients self diagnose or assist doctors in diagnosis. 

2- Prescription auditing: AI audit systems can help minimize prescription errors.

3- Pregnancy Management: Monitor mother and fetus to reduce mother’s worries and enable early diagnosis

4- Real-time prioritization and triage: Prescriptive analytics on patient data to enable accurate real-time case prioritization and triage.  

5- Personalized medications and care: Analyze patient data to find the most effective and personalized treatment  and helps reducing cost and increasing effectiveness of care. 

6- Patient Data Analytics: Analyze patient and/or 3rd party data to discover insights and suggest actions.  AI allows the institution (hospital, etc…) analyze clinical data and generate deep insights into patient health. It provides an opportunity to reduce cost of care, use resources efficiently, and manage population health easily.

 7-Surgical robots: Robot-assisted surgeries combines AI and collaborative robots. These robots are well-suited for procedures that require the same, repetitive movements as they are able to work without fatigue. AI can identify patterns within surgical procedures to improve best practices and to improve a surgical robots’ control accuracy to sub-millimeter precision.

8- Early diagnosis: Analyze chronic conditions leveraging lab data and other medical data to enable early diagnosis 

9- Medical Imaging : Advanced medical imaging to analyze and transform images and model possible situations.  

10- Drug discovery: Find new drugs based on previous data and medical intelligence. 

11- Gene analysis and editing: Understand genes and their components. Predict the impact of gene edits.

12- Device and drug comparative effectiveness : Helps review if drug or a new medical device are effective

13- Brand management and marketing: Create an optimal marketing strategy for the brand based on market perception and target segment. 

14- Pricing and risk: Determine the optimal price for treatment and other service according to competition and other market conditions.

15- Market research: Prepare hospital competitive intelligence. 

16- Operations: Process automation technologies such as intelligent automation and RPA help hospitals automate routine front office and back office operations such as reporting. 

17- Customer service chat-bots: Customer service chat-bots allow patients to ask questions regarding bill payment, appointments, or medication refills. 

18- Fraud detection: Patients may make false claims. Leveraging AI powered Fraud Detection tools can help hospital managers to identify fraudsters.

 

Useful Links

Cobot - https://research.aimultiple.com/cobot/