DATA AND CLOUD FREQUENTLY ASKED QUESTIONS
What is the difference between AI and ML?
Other people may theorize on the difference in meaning between AI (artificial intelligence) and ML (Machine Learning), but when working in the field, those terms are used interchangeably. Machine Learning is the more commonly used term used when describing the actual algorithms, data science and operational engineering (MLOPs) involved in building models, whereas AI is used when talking about the abilities of what a machine learning system can do. For example, Document AI is a solution that can parse and understand documents, using machine learning to train the model.
What is AI/ML?
Slang for artificial intelligence (AI) and machine learning (ML), this term refers to a significant advancement in computer science and data processing that is rapidly changing a wide range of sectors.
Artificial intelligence (AI) is the term for systems and algorithms that can replicate cognitive processes like perception, learning, and problem-solving in order to simulate human intellect. While machine learning (ML) is a branch of artificial intelligence (AI) that belongs to the "limited memory" category that allows for the AI (machine) to learn and grow over time.
What is generative AI?
Generative AI is a blanket term that covers things like chatbots, text-to-image AI, enterprise search of documents, and code assistance. Users may quickly create new content based on a range of inputs through generative AI. These models can take as inputs and outputs text, photos, sounds, animation, 3D models, or other sorts of data. For example, a user can give input like asking a natural language question like “where can I find a red dress with dinosaurs on it?”, and generative AI can be used to return an output of images of red dresses with dinosaurs on them, despite those images not being labeled as “red dresses with dinosaurs on them”.
What is a LLM (Large Language Model)?
A large language model (LLM) is a kind of machine learning model that can carry out a number of natural language processing (NLP) activities, including text generation and classification, conversational question and answer, and text translation.
What is advanced analytics?
Advanced analytics is an umbrella term for a variety of data analysis methods mostly used for forecasting, including machine learning, predictive modeling, neural networks, and artificial intelligence (AI). Advanced analytics employs more complex data analytics methods, such machine learning, to produce predictions and improve decision-making. Predicting future outcomes and recommending a plan of action are two different purposes for which advanced analytics are used.
What is the cloud and cloud data?
‘The Cloud’ is a bunch of computers that are connected to the internet, and those computers are owned by corporations like Google (Google Cloud) or Amazon (Amazon Web Services). These corporations are known as Cloud Providers. Companies or individual users can buy those computers and use them to do whatever they need to do. For cloud data use cases, they will buy those computers to store their data there, and compute things with that data. For example, I might need to calculate the average wait times for all airports in the world. My own computer does not have enough power to do this, so I would instead run that calculation on the cloud, using the Cloud Providers computers.
What is data analytics?
The science of using raw data analysis to draw inferences about information is known as data analytics. Numerous methods and procedures used in data analytics have been mechanized into mechanical procedures and algorithms that operate on raw data for human consumption.
What exactly is geospatial data?
Geospatial data, often known as location data or spatial data, is information about locations on the surface of the Earth. You can associate things, occasions, and other real-world occurrences with a particular geographic region that is denoted by latitude and longitude coordinates. That being said, geospatial data often goes beyond just latitude and longitude, and can be represented in either vector or raster format as a point on a giant grid that covers earth.
What is retail analytics and how it is used?
Retail analytics is the practice of employing software to gather and analyze data from offline, online, and catalog sources in order to give businesses insight into consumer behavior and market trends.
Retail analytics is used by businesses to forecast demand, explain past operational and financial performance, diagnose potential problems, propose more productive alternatives, diagnose what might have gone wrong, and offer recommendations, sometimes in real time, that store associates, customer service representatives, and others can use to cross-sell, upsell, or improve the customer experience.
What is technical writing?
Technical writing currently refers to any format that conveys technical information, as opposed to formerly mostly referring to user manuals. Instruction manuals or other documents that explain processes or applications are examples of technical writing.
What are the different types of Data Analytics?
There are four main types of data analytics:
Descriptive analytics: This explains what has occurred over a specific time period. Business intelligence (BI) tools and dashboards cannot exist without descriptive analytics, which is the foundation of reporting. It responds to fundamental inquiries like "how many, when, where, and what."
Diagnostic analytics: Analyzing data to determine causes and events or why something happened is known as diagnostic data analytics. Drill down, data discovery, data mining, and correlation techniques are frequently used.
Predictive analytics: This shifts to what is most likely going to occur soon. Institutions use predictive analytics to identify trends, correlations, and causation.
Prescriptive analytics: AI and big data are combined in prescriptive analytics to help forecast outcomes and determine the best course of action. The two subcategories of this analytics area are optimization and random testing.