Hi,
I work as Training Manager, hence, I would like to use AI and ML in my vertical to predict the probable attritions during a Training Class for a specific Client. There are regular attritions during a class and hence using AI & ML we can find out what would be our Training Throughput basis the predictions to find out probable attrition numbers.
Thanks,
Sunny Suri
Machine Learning and Artificial Intelligence will be extensively used in various areas within the company. I will share a few examples:
Identifying sales leads that have potential to generate high volume sales in future is very important for business, Machine Learning and AI will be leveraged to solve this problem. Marching Learning techniques like Classification Approaches, Recommendation Engine, Natural Language Processing techniques will be used to achieve this objective.
There are many use cases and applications of AI one can think of applying while starting a business- automating tasks according to skills and proficiency- with ratings of the employees to the given task- variable in bonus on this structure is a simple one.
For myself, I would like to make a classification model that can detect an image and identify it’s copy and origin on the internet- which is then minted as an NFT with the datestamp and ownership.
Hi,
I am confident AI can be used to enhance the performance of telecom business supporting applications by monitoring and reporting any breach in defined SLAs and suggesting preventive measures.
regards
Nikhil Ingole
I am from the networking industry. Some of the ways AI/ML can be used here are:
To keep track of the running systems on the network and its applications. After collecting the data for a few months on the status of the systems and the applications, this data can be used to predict the behaviour of the systems. It can be used to identify anomalies in advance and one or more actions can be configured to be executed, in case the anomaly occurs.
AI/ML can be used to observe user behaviour on the use of the applications(for example observe how the user uses or browses the GUI), and a customized experience can be provided to each user. This will help users in Enterprise class applications where there are a large number of features available on the GUI and not all users may need all features. So, by observing each users’ browsing behaviour, the most commonly used features may be provided on the first page and other features can be relegated to other pages.
In wireless network security, the behaviour of the network can be observed and data collected. If one or more users/devices are trying to access the network without authorization, that user or device can be classified as a rogue-device or a rogue-user. More insights can be identified as to the location of the rogue-devices, the timing of unauthorized access, the category of the users, the type of probes sent…etc
Thanks
SG
In my view AI and ML can be used in this vertical in many ways like:
Navigation
Train the drones for autonomous flights
Track objects with precision
Mapping areas
I work as Technical Associate Architect in Fintech company where we sell customized portfolios to our HNI Clients depending upon their risk appetite.
Below are the use cases that can be used
Calculating the risk profiles
Asset Allocation depending upon customer’s profile
Market View or Direction
Optimal / Automated Portfolio Generation
Rebalancing the Portfolio
Hi All,
AI and ML can be used for creating BOT application
Hi,
I am a Trader, Investor and Analyst of Equity Markets and financial instruments.
In regards to project, I will like to use AI/ML to analyze data and determine certain pattern or behavior to predict the future in equity investments and financial management and forecasting of economic circumstances across the world.
Thanks
Kunal
AI and ML can be used to understand the patterns that gets triggered on the daily basis for cloud operations and start leveraging this to solve those problems without the human interventions with this we can completely cut down the Mean Time To Resolve the issue and availability of the applications will be very high for the customers.
Also, we can use this methodolody to analyse the patterns and come up with a permanent fix for the problem that we face.
I work in a think-tank focusing on various aspects of civilization studies, including geopolitics, impact of technology on society, mind sciences, history of Indian science and technology etc.
Some use cases on AI/ ML that come to my mind:
Predictive Defect Assignment using AI in a Software Development Environment
Introduction
Software development is a complex and challenging process, involving many teams and components. One of the key challenges faced by organizations is accurately assigning defects to the correct component. This process can be time-consuming and prone to human error, leading to longer resolution times and increased costs. In this case study, we will examine how the use of Artificial Intelligence (AI) can improve the accuracy and efficiency of defect assignment in a software development environment.
Problem Statement
A large software development organization face several challenges in the defect assignment process. The manual process is time-consuming, with team members having to spend significant amounts of time reviewing each defect to determine the correct component. This leads to longer resolution times and increased costs. Additionally, the manual process is prone to human error, with defects sometimes being assigned to the wrong component, leading to further delays. Defect hoping leads to additional costs.
Solution
To address these challenges, we implemented an AI-based solution for predictive defect assignment. The solution utilized natural language processing and machine learning algorithms to analyze the description and details of each defect and predict the most likely component to which it should be assigned. The solution is integrated into the organization’s defect tracking system, JIRA tool, allowing team members to quickly and easily assign defects to the correct component.
Implementation
The implementation of the AI-based solution involved several key steps:
Results
The implementation of the AI-based solution for predictive defect assignment has a significant impact on the software development process. The solution significantly reduces the time required to assign defects to the correct component, reducing resolution times and costs. Additionally, the solution greatly reduces the risk of human error, leading to fewer defects being assigned to the wrong component and fewer delays.
Conclusion
In conclusion, the use of AI in predictive defect assignment can provide significant benefits for software development organizations. By reducing the time required to assign defects and reducing the risk of human error, AI-based solutions can help organizations improve their overall efficiency and reduce costs. Additionally, by integrating AI algorithms into existing systems, organizations can easily and quickly benefit from the advantages of AI without having to make major changes to their processes or infrastructure.
Hello Everybody,
I am looking forward to use Artificial Intelligence and Machine Learning to create Generative AI for 3D generation of simple, daily-life objects inspired from real-life object data and recreate everything in a Virtual 3D form and make it accessible to public with imaginitive prompts and a wide-application in Generative AI.
Regards,
Pranay Kumar Panda
Case Study: How Conversational AI Resolved Shared Services Queries for a Global Consulting Firm
Introduction:
In today’s digital age, businesses are turning to Conversational AI to solve their operational problems. One such example is a global consulting firm that had a high number of shared services queries from its workforce, which led to an overhead for IT, HR, and Finance reps. The firm decided to use NLP and develop an intelligent chatbot solution to address these concerns.
Challenge:
The consulting firm had consultants working remotely, making it difficult to reach the IT, HR, and Finance reps to address their queries. Additionally, the high volume of queries meant that reps were spending significant amounts of time resolving issues, leading to low efficiency and productivity.
Solution:
The head of Conversational AI, recognizing the need to address the challenge, leveraged the shared services queries to train an intelligent chatbot. Using natural language processing, the chatbot was designed to resolve common queries and guide consultants to self-service their requests. The chatbot was made available on the company’s communication channel, MS Teams, as well as mobile devices.
Impact:
Five months post-implementation, the chatbot has helped reduce the workload on the IT, HR, and Finance teams, saving valuable time for these reps. The chatbot has also enabled consultants to self-serve their requests, leading to increased productivity and efficiency. This has resulted in higher employee satisfaction and engagement, improving the overall performance of the organization.
Conclusion:
The implementation of Conversational AI has enabled the global consulting firm to reduce the workload on its shared services teams and improve overall efficiency. The chatbot has resolved common queries, enabling consultants to self-serve their requests and saving valuable time for IT, HR, and Finance reps. As the organization continues to explore further improvements, Conversational AI has proved to be a reliable and effective solution for resolving shared services queries.
As a Ph.D scholar in Finance, I was interested in learning python for statistical analysis, therefore I wanted to learn the basics of python and how it can be used in the field of Finance. Also, the course would help in analysing stock price movements and how AI ML can be applied in price prediction.
ML is currently being used on a couple of projects where we aim to build models that identify anomalies in billing and payments patterns and I would like to gain a better overview of what can be accomplished and how best to support my team
Using machine vision and AI capability for home care monitoring and managing the team effectively based on the a daily activity pattern based on their daily activity in the project to identify any abnormal deviations which might be an early challenges of the Project
Problem Statement: Optimizing PC Sales Forecasting in a Retail Business using AI & ML
Introduction:
A retail business specializing in PC sales is facing challenges in accurately forecasting demand for different PC models, resulting in inventory imbalances and potential lost sales opportunities. To address this problem, the business aims to leverage AI and machine learning techniques to improve the accuracy of their sales forecasting and optimize inventory management.
Case Study:
Problem Identification:
The retail business struggles to forecast PC sales accurately due to various factors such as evolving customer preferences, market trends, and the complexity of product features. This leads to overstocking or understocking of certain PC models, resulting in increased costs, storage issues, and missed sales opportunities.
Data Collection and Analysis:
To tackle this problem, the retail business collects historical sales data, including PC model, customer demographics, purchase patterns, marketing campaigns, and external factors like seasonality and economic indicators. This data is analyzed to identify patterns, trends, and correlations.
AI and ML Implementation:
The retail business leverages AI and ML techniques to build a forecasting model that takes into account various data factors to predict future PC sales. The following steps are involved:
a. Data Preprocessing: The collected data is cleaned, transformed, and prepared for analysis, ensuring data quality and consistency.
b. Feature Selection: Relevant features such as PC model, customer demographics, marketing campaigns, and external factors are identified based on their impact on sales.
c. Model Training: Various machine learning algorithms, such as regression, time series analysis, or ensemble methods, are applied to train a forecasting model using the historical sales data and selected features.
d. Model Evaluation: The trained model is evaluated using appropriate metrics to assess its accuracy and performance in forecasting PC sales.
e. Forecasting and Optimization: The forecasting model is deployed to predict future PC sales based on real-time or historical data. The predictions help optimize inventory management, ensuring the right stock levels for each PC model to meet customer demand while minimizing storage costs and potential stock-outs.
Continuous Improvement:
The AI and ML model are regularly monitored and retrained using updated data to ensure ongoing accuracy and adaptability to changing market dynamics. Feedback loops are established to gather insights from sales data, customer feedback, and other relevant sources, enabling continuous improvement of the forecasting model.
Benefits and Results:
By implementing AI and ML techniques in PC sales forecasting, the retail business achieves several benefits:
a. Improved Sales Forecasting Accuracy: The forecasting model enhances accuracy by incorporating various data factors and identifying patterns and trends that were previously difficult to capture manually.
b. Optimized Inventory Management: With more accurate demand forecasts, the retail business can optimize inventory levels, reducing overstocking and understocking issues, minimizing costs, and improving customer satisfaction.
c. Enhanced Sales and Profitability: By aligning inventory levels with customer demand, the business maximizes sales opportunities and minimizes missed revenue due to stockouts.
d. Better Resource Allocation: With improved forecasting, the business can allocate resources effectively, such as adjusting marketing campaigns and optimizing production and supply chain processes based on anticipated demand.
Conclusion:
By leveraging AI and ML techniques to address the challenge of PC sales forecasting, the retail business achieves more accurate predictions, optimizes inventory management, and ultimately enhances sales and profitability. The implementation of an advanced forecasting model allows the business to adapt to market dynamics, provide better customer experiences, and gain a competitive advantage in the PC sales industry.
Working with AI and ML
Uncover insights
Predict outcomes
Automate tasks
Personalize experiences
Solve complex problems
Gain data-driven insights
Drive innovation
Excited to work with the python ML libraries so that rule base learning will be replaced with AI
#AI #MachineLearning #Innovation
AI and ML can be useful in following ways in my project
Hi All,
I am currently working as a Data Analyst in IT industry. We work on devices data and their compliance status based on the version and patch updates. It is a recurring task of updating reports and would like to use AI/ML in drawing insights from data and notify infrastructure team on the impact of new upgrades on any device. Looking forward to gain better knowledge and leverage AI & ML to resolve the issues at a very stage.
Thank You.