AI systems are helping to perform crop yield estimation, resulting in overall improvement in field resource utilization. Accurate and error free yield estimations are now ...
Artificial Intelligence Use Cases.
Initially started as an offshoot project to mimic and simulate the human brain, Artificial Intelligence emerged as the most dominant technology enabler for the 21st century enterprises. Through several trials and tribulations spanning decades of AI winter, the technology has now matured enough to empower a plethora of use cases
The Hottest Tech Domains in Artificial Intelligence
AI/ML infrastructure comprises of the computing and storage systems that provide a reliable and scalable environment for data scientists and engineers to build and deploy AI/ML models efficiently in production. It also includes ancillary tools, libraries, and cloud services that aid in overall deployment and management of an AI/ML system.
AI/ML middleware plays an important role in achieving efficiency in the overall deployment of a AI/ML system. It acts as an intermediary layer between the AI/ML models, the AI/ML infrastructure and user to provide enhanced features to optimize the typical workflows such as model management, deployment, and monitoring.
AutoML (Automatic Machine Learning) refers to the process of automating the end-to-end process of applying machine learning to real-world problems, using pre-built models and tools for tasks such as feature engineering, algorithm selection, and hyperparameter tuning. The goal of AutoML is to make machine learning accessible and effective for a wider range of users, including domain experts with limited data science and machine learning expertise.
Computer vision is a specialized field under artificial intelligence focused on enabling computers to interpret and understand visual information from the world in the same way that humans do. This involves developing algorithms and models that can analyze images and videos to identify objects, recognize patterns, and extract meaningful information.
Data analytics is one of the fundalemtal areas in artificial intelligene which deals with examining, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. The process involves a range of techniques and tools, including statistical analysis, probability, data visualization, and database management, to help organizations turn raw data into actionable insights.
Deep learning is a field of artificial intelligence that focuses on building artificial neural networks that minic the human brain, having multiple layers. It is based on the idea that a complex problem can be divided into smaller and simpler parts, and these parts can be learned and combined to produce a solution to the original problem. It is based on deep neural networks that are designed to recognize patterns to make predictions and generate similar patterns based on input patterns.
ETL stands for Extract, Transform, Load, and refers to the process of collecting data from various sources, transforming it into a format suitable for analysis, and loading it into a target database or data warehouse for further analysis and reporting. It is one of the fundamental processes in artificial intelligence which is responsible for data management process, as it allows organizations to integrate data from different sources, identify trends, and make informed decisions. The goal of ETL is to ensure that data is consistent, accurate, and available for analysis, regardless of the original source.
ML Oerations (ML Ops) is a set of practices and processes that enable organizations to manage and operationalize their models at scale. ML Ops is concerned with the entire lifecycle of ML models, from development to deployment and management, and covers a range of tasks including model training, validation, deployment, monitoring, and maintenance. The goal of ML Ops is to ensure that ML models are consistently delivering accurate results in production, while also providing the agility and speed required to rapidly develop, deploy, and update models in response to changing business needs.
Natural Language Processing (NLP) is a field of artificial intelligence and computer science that focuses on the interaction between computers and humans in natural language. NLP aims to enable computers to understand, interpret, and generate human language in a way that is meaningful and useful. NLP techniques are used in a variety of applications, including sentiment analysis, text classification, machine translation, text generation, and question answering.
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions and observing the consequences in an environment. The goal of the agent is to maximize a reward signal over time by selecting actions that lead to the highest expected reward. RL algorithms are used to solve a wide range of problems, including robotics, game playing, recommendation systems, and autonomous systems. The key components of an RL system are the environment, the agent, the state space, the action space, the reward function, and the policy.
Use Cases by Industry Verticals
June 8, 2022
Maintenance planning is a critical step to contain downtime, planned or unplanned, which can cost hundreds of thousands of dollars in lost production per hour. ...
May 25, 2022
Anomalies in systems occur when observations deviate significantly from the standard known “pattern”. Since anomalies occur very rarely, about 0.001 to 1% of the time, ...
March 25, 2022
This article covers a case study on tackling skin cancer with AI and Deep Learning in non-invasive ways. Imagine a pen that you put against ...
March 17, 2022
2021 has been a transformative year, especially in the aftermath of the COVID-19 pandemic. It also made us realize the importance of leveraging the power ...
December 28, 2021
In an industry as huge as healthcare, it’s no surprise that organizations rely heavily on their contact centers. And, even more than in other industries, ...
May 30, 2022
Artificial Intelligence Use Cases.Initially started as an offshoot project to mimic and simulate the human brain, Artificial Intelligence emerged as the most dominant technology enabler ...
June 5, 2023
The domain of machine learning (ML) is fascinating and frustrating at the same time. There are an abundance of tools and libraries available for ML. ...
January 25, 2022
Banking and financial services customers have high expectations for quality customer service – after all, their money and their time are both involved. In fact, ...
August 23, 2021
Artificial intelligence (AI) is the new frontier in financial services. The technology has the potential to improve customer engagement, streamline operations, and reduce costs. With ...
August 9, 2022
In recent times, the focus in the insurance industry has shifted towards delivering excellent customer service by adopting a consumer-centric model. As the customer volume ...
February 18, 2022
The insurance industry is one of the earlier pioneers of making data-driven decisions and adopting Machine Learning. Over the last few years, the amount of ...
January 12, 2022
Environmental, social, and governance (ESG) criteria are a set of standards for a company’s operations that socially conscious investors use to screen potential investments. Environmental ...
Use Cases by Horizontal Functions
January 27, 2022
Modern applications are mobile first and are built around cloud native distributed microservices architectures. These architectures have become the basic building blocks for complex and ...
January 26, 2022
With the continued proliferation of cybersecurity threats and attacks, one of the most important things you can invest in as a business is cybersecurity automation ...
December 30, 2021
Employee attrition used to be a mystery. Here’s how you can unlock insights with AI. ...
May 30, 2022
If automated operations is the endgame, then investing in an AIOps platform that enables automation will get you there. ScienceLogic has been focused on the ...
May 27, 2022
Instrumenting your code generates telemetry data that shows health and performance stats of a system. In this blog, we will dive deep into code instrumentation, ...
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March 14, 2022
Sales and support enablement has changed dramatically with the use of speech recognition. Sales and support teams can perform situational training and analysis before the ...
March 4, 2022
Audience Segmentation is a marketing strategy based on identifying subgroups within the target audience in order to deliver more tailored messaging for stronger connections. The ...
Use Cases by User Personas
April 4, 2022
Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by ...
March 22, 2022
The current state-of-the-art method for solving text-based problems includes separating sentences into sequences of tokens. Relying on tokens is, for the most part, a necessary ...
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March 23, 2022
This article throws light on running collaborative experiments in machine learning. Sharing experiments to compare machine learning models is important when you’re working with a ...
February 15, 2022
Extracting valuable insights out of data collected from machine sensors can be hard, often requiring analyzing data from many sensors in parallel. Due to the ...
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August 23, 2022
Any machine learning project involves continuously evolving code, datasets, and models. This process requires data versioning for tracking and managing the changes that are made ...
August 22, 2022
As data has grown exponentially, data quality monitoring has become crucial for building successful data and machine learning systems. 42% of data analysts that took ...
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May 10, 2022
The vast amount of data generated daily across society is widely considered as a game-changer for research, technological innovation, and even policy making. Big data has ...
April 13, 2022
Data maturity is the extent to which an organization is able to utilize its data to extract meaningful insights that drive decision-making. Event stream processing technologies ...
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February 20, 2023
Here is a demonstration of a video stream in real-time using WebAssembly to apply a pre-trained food classification model to each frame of the video ...
January 16, 2023
If you are facing challenges in anonymize data and finding it difficult to perform it at a large scale, you need a data tokenization platform. ...
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April 12, 2022
While the global spend on artificial intelligence (AI) and machine learning (ML) was $50 billion in 2020 and is expected to increase to $110 billion ...
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June 17, 2022
Nowadays, Machine Learning models are frequently used in different domains including Health. Here is a tutorial on how to build and train a model as ...
April 20, 2022
Lead scoring is a methodology used by sales and marketing departments to determine the worthiness of leads, or potential customers, by attaching values to them ...
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April 7, 2022
Production machine learning (ML) pipelines are built to serve ML models that enrich the product and/or user journey to a business’ end users. Machine learning ...
April 6, 2022
The Explainable AI (XAI) program aims to create a suite of machine learning techniques that produce more explainable models, while maintaining a high level of ...
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March 24, 2022
A dot density map is a type of visualization that uses a dot or another symbol to show the presence of a feature or phenomenon, ...
January 19, 2022
Time series modeling is needed in every business. You may want to forecast sales or estimate demand or gauge future inventory levels. Perhaps you want ...
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Artificial Intelligence Innovations
List of platforms powering the artificial intelligence use cases
List of components, libraries and frameworks powering artificial intelligence use cases
List of tools empowering artificial intelligence use cases
Artificial Intelligence Use Case Master Index
AI/ML Infrastructure > AI Cloud
AI/ML Infrastructure > AI Orchestration
AI/ML Infrastructure > Federated Learning
AI/ML Infrastructure > ML Node
AI/ML Middleware > Data Compliance
AI/ML Middleware > Explainable AI
AutoML > Classification Model
AutoML > Feature Discovery
AutoML > RPA
AutoML > Predictive Model
Computer Vision > Face Recognition
Computer Vision > Object Detection
Computer Vision > Object Recognition
Data Analytics > Descriptive Analytics
Data Analytics > Diagnostic Analytics
Data Analytics > Predictive Analytics
Data Analytics > Prescriptive Analytics
Data Analytics > Visual Analytics
Deep Learning > Neural Network
Deep Learning > Voice Recognition
Deep Learning > Text Recognition
Deep Learning > Natural Language Generation
Deep Learning > Voice Generation
ETL > Data Governance
ETL > Data Transformation
ETL > Data Extraction
ETL > Data Pipeline
ETL > Stream Processing
ETL > Data Orchestration
ML Operations > Algorithm Optimization
ML Operations > ML Workflows
ML Operations > Data Observability
ML Operations > Data Management
NLP > Virtual Assistant
NLP > Chatbot
NLP > Conversational AI
NLP > Natural Language Understanding
NLP > Language Modeling
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This page is updated every week to enlist emerging use cases of artificial intelligence realized with the help of innovating platforms, solution components and tools.
Artificial Intelligence encompasses multiple cutting edge technologies, from computer vision, to natural language processing, to deep learning and streaming data processing. The artificial intelligence use case index covers all the prevalent and emerging use cases that are setting the stage for newer innovations enabling a world where humans are assisted by augmented intelligence. You can check out the domains section to get a glimpse of the various technological domain within artificial intelligence.
You can search based on various artificial intelligence domains or the alphabetical list of artificial intelligence use cases. Additionally, you can also look out for artificial intelligence use cases based on industry verticals, horizontal functions, and user personas. Some of the popular industries adopting artificial intelligence are finance, insurance and agritech. In terms of horizontal functions, we find a lot of artificial intelligence use cases in HR and sales , marketing. Similarly, ML engineers and data scientists are the most prevalent personas working on the development of artificial intelligence based applications.
The top artificial intelligence use cases are the ones that augment our basic senses, such as vision and hearing. In this regard, computer vision and text to speech recognition capabilities have made ground breaking progress. Apart from that, natural language processing and deep learning have profoundly improved our cognitive abilities in deciphering massive amount of unstructured data. Additionally, explainable AI is now rising up as the new man machine interface to synergize AI assisted tasks for humans.
If your company is building innovative platforms or tools that have artificial intelligence at the core of innovation, you can reach out to us. We are happy to cover your story, as a blog post covering the technical nuances or business insights around adopting your technology for realizing the specific use case.
If you have any specific questions, please feel free to drop us a line and we shall initiate a conversation.