Knowledge Hub

Please note, AAaaS has nothing to do with Cloud, it can be on On-Premises or On-Cloud. AAaaS is an environment setup for Data Scientists to play around with Machine Learning and Deep Learning Modelling.

Advance Analytics as a Service (AAaaS) is a new concept and still maturing, due to which it is still very confusing. Different companies are offering different approaches, so there is no one best approach.

Two approaches been followed.

1. AAaaS as PaaS: Modelling tools like Databricks, Azure AML etc., are available as pre-installed services (refer to topic: PaaS) where customers can provision these tools in few clicks and simply start developing their Machine Learning or Deep Learning Models to build their own solutions. Here, customers must have their own MLOps along with CI/CD.
2. AAaaS as SaaS: In this approach, Solution Providers hosts pre-built or OOTB Machine Learning and Deep Learning Models. These are offered to customers to consume only. Customers inject their own dataset for relevant data analysis and analytics. Here, customers must follow Solution Providers MLOps along with CI/CD.

‘Data Segmentation is about dividing dataset into multiple sections based on some requirements.’

Segments are defined based on many parameters e.g., in a business, segments are defined by accessibility, actionable, profitable etc.

For example, when we go into a mall, there is a segment/section for Food Court where multiple categories of food are available with different classification like expensive or cheap food.

Steps to improve Data Segmentation

• Enrich your data to increase data quality.
• Plan and replan your data segmentation criteria(s)
• Improve real time segmentation.
• Design segment based on customer prioritization and feedback.
• Integrate with as many data elements.

In business, what you can do by having Data Segmentation

• You can create deals based on customer buying behavior.
• You can improve your products arrangement.
• You can plan your marketing strategy.
• You can analyze customer feedback.

These are one of the most confusing terms in the data domain. Both are mostly confused with each other.

The major difference between both is.

• Data Mining is done on ONLY structured data.
• Data Science is done on structured, semi and unstructured data.

Data Science is mostly tagged with Big Data and Data Mining is tagged with Data Warehousing. There are many definitions and comparisons for both in the market as both do machine learning, deep learning, data cleansing, statics, and math algorithms etc.

‘Data Science is the Superset of Data Mining.’

Data Mining is also known as KDD (Knowledge Discovery in Data). First it was performed in excel, then with the introduction of DWH, an automated process started discovering historical patterns and other valuable information from large data sets which are structured.

Now with the introduction of Big Data, Data Lake, NoSQL databases, semi and unstructured datasets, the magnitude of the data size has taken a huge step ahead and Data Science has come onto the surface.

We all know what Machine Learning (ML) models are right? ML is about using raw but structured or semi-structured dataset, then creating feature tables which are no more than traditional tables but with cleansed data. Later, ML models use this cleansed dataset to classify data and produce expected results.

Same way, Deep Learning models is about using raw but unstructured dataset where no one will know what will flow in. Deep Learning models dig out required data from that unstructured dataset to prepare cleansed dataset in shape of feature tables to classify for expected results.

For example, if we need to find out how many cars, bikes or buses there are in structured or semi-structured datasets then it’s relatively easy as in rows and columns format. We can have the width, length, and height so using ML models can easily ready this dataset and produce expected output. But what if we give pictures to ML models to extract the same result, will it be possible? The answer is No as ML models normally work on structured or semi-structured datasets. To find out cars, bikes, or buses from pictures, Deep Learning (DL) algorithm and models are used which can recognize the objects in the pictures and find out which presents car or bike or a bus. Please note, the next stage is supervised and unsupervised learning.

Supervised and Unsupervised Learning categorizes the dataset into two where there are situations where sometimes we know or sometimes, we don’t know what’s in there.

We have discussed this on another topic i.e., Data Classification, Categorization and Data Clustering but as Supervised and Unsupervised Learning are very important to understand Machine Learning, let’s understand it again here.

In Machine Learning, there are Supervised and Unsupervised Learning methods.

Both terms are used for pattern identification before complex algorithms come into action.

Supervised Learning is done based on the uniqueness of the data where e.g., all animals are tagged separately. Whereas Unsupervised Learning is done based on the characteristics we can create clusters e.g., 1) Animals with 2 legs 2) Animals with 4 legs.

• Supervised Learning uses labeled data whereas Unsupervised Learning uses unlabeled data.
• The most popular Supervised Learning algorithms in data mining are the K-Nearest Neighbor and decision tree algorithms.
• The two common Unsupervised Learning algorithms in data mining are K-means clustering and hierarchical clustering.
• Supervised Learning output is known.
• Unsupervised Learning out is unknown.
• Training data is provided for Supervised Learning.
• No training data is provided for Unsupervised Learning.

In the current era, people are confused between AI vs Data Science.

‘This is a very controversial topic.’

If you are clear about Data Science, then understanding AI will not be a problem. In the current era, with huge datasets either its structure, semi, or unstructured data, we create Data Science Models to analyze and predict. So, when it’s done by Humans, it’s called Data Science but when models are matured and are passed on to the systems or machines to run itself, train itself, fix itself, analyze itself and predict itself then it becomes Artificial Intelligence (AI).

AI is much more than that but here aim is not to explain or elaborate in detail like what AI is, here aim is to explain the difference between AI and Data Science at a very high level.

In this era, social media has surpassed every medium to generate data. Social media is not only about writing messages, it also about sending audio or video messages and the moment audio and video (with voice) comes into picture, NLP comes into action.

‘NLP is about using techniques to understand the way humans communicate.’

Using NLP, code tries to convert audio into text and vice versa. Using NLP capabilities there are solutions which talk to humans, understand what they want and then reply to them. NLP also assists in performing actions e.g., a human can ask ‘I want a Chill Coke Zero’, the NLP will recognize this request and get a Cold Coke Drink for customer. At the same time, if Chill Coke Zero is out of stock, using Machine Learning and AI, it can reply to customer in Voice format that ‘At the moment, we are out of stock, would you like to go for Chill Pepsi Black’.

Steps of NLP

• Understand
• Analyze
• Manipulate
• General Human Language or Text

Generative Artificial Intelligence (AI) has become a prominent trend in recent times, particularly emphasized by our enthusiastic Sales folks 😊. But let's pause for a moment, are you familiar with the concept of Synthetic Data? Surely, it's not new to you 😊. And for those with a tech-savvy mindset, the term "Generative AI" likely triggers thoughts of Generative Models or Statistical Models, perhaps with a knowing smile.

Indeed, the application of generative and statistical models has been around for decades. In fact, the idea of Generative AI isn’t new at all, it dates back to as early as 1950, when Alan Turing proposed machines that could exhibit intelligent behavior.

Today, however, we're witnessing a resurgence of interest, largely due to the accessibility and power of tools like OpenAI’s ChatGPT, Google Gemini, Anthropic’s Claude, Meta's LLaMA, and Stability AI's Stable Diffusion. These tools represent a leap forward in putting generative capabilities into the hands of everyday users, generating text, images, audio, and even code with natural ease.

The term "Large Language Model" (LLM) generally refers to advanced artificial intelligence models designed to understand and generate human-like language. These models are typically trained on massive amounts of diverse text data and leverage deep learning techniques to capture the complexities of language. One of the notable examples of an LLM is GPT (Generative Pre-trained Transformer) like GPT-3 for ChatGPT in current era.

Here are key characteristics and aspects associated with Large Language Models:

• Training on Extensive Datasets: LLMs are trained on large and diverse datasets, which often include a wide range of text sources such as books, articles, websites, and other textual content from the internet.
• Pre-training and Fine-tuning: LLMs go through a pre-training phase where they learn to predict the next word in a sentence or fill in missing words. After pre-training, they can be fine-tuned on specific tasks or domains to enhance their performance in particular areas.
• Transformer Architecture: Many LLMs, including GPT models, use a transformer architecture. Transformers enable the models to capture long-range dependencies and contextual information in the data efficiently.
• Natural Language Understanding: LLMs excel in natural language understanding, allowing them to comprehend the meaning, context, and nuances of human language. This includes tasks such as language translation, summarization, question answering, and more.
• Generative Capabilities: One of the distinctive features of LLMs are their generative ability. They can generate coherent and contextually applicable textual content primarily based totally on a given activate or input.
• Applications: LLMs have found applications in various domains, including chatbots, language translation, content generation, summarization, sentiment analysis, and more.
• GPT Series: The GPT (Generative Pre-trained Transformer) series, developed by OpenAI, is a notable example of LLMs. GPT-3, the third iteration, is one of the largest and most powerful language models to date.

We have already gone through Analysis and Analytics. Analytics refers to the systematic analysis of data or statistics to uncover meaningful patterns, insights, and trends. There are several types of analytics, each serving specific purposes and addressing different aspects of data analysis.

The major types of analytics include:

• Descriptive Analytics: It describes what has happened in the past, providing a historical overview. For example, summarizing sales data to understand total revenue for a specific period.
• Diagnostic Analytics: It focuses on why a particular event or trend occurred by identifying its root causes. For example, investigating a drop in website traffic to determine the reasons behind the decline.
• Predictive Analytics: It predicts future outcomes or trends based on historical data and statistical algorithms. For example, using machine learning models to predict customer churn or stock prices.
• Prescriptive Analytics: It recommends actions to optimize outcomes based on predictive analytics results. For example, suggesting marketing strategies to increase customer retention based on predictive insights.
• Other types of analytics include:
• Text Analytics (Text Mining): It analyzes unstructured text data to extract insights, sentiment, or patterns. For example, analyzing customer reviews to understand sentiment and identify common themes.
• Spatial Analytics: It analyzes geographic or location-based data to uncover spatial patterns and relationships. For example, mapping crime rates in a city to identify high-crime areas.
• Social Media Analytics: It analyzes social media data to understand trends, sentiments, and user interactions. For example, monitoring brand mentions on social media to assess brand sentiment.
• Web Analytics: It analyzes website data to understand user behavior, track performance, and optimize user experience. For example, tracking page views, bounce rates, and click-through rates on a website.
• Business Intelligence (BI): It involves the use of tools and processes to analyze and visualize business data for decision-making. For example, creating dashboards to monitor key performance indicators (KPIs) in real-time.
• Big Data Analytics: It analyzes large and complex datasets to uncover hidden patterns and insights. For example, analyzing vast amounts of sensor data for predictive maintenance in manufacturing.
• Customer Analytics: It analyzes customer data to understand behavior, preferences, and trends. For example, creating customer segmentation based on purchasing patterns.