Kubernetes (often abbreviated as "K8s") is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. It was originally developed by Google and is now maintained by the Cloud Native Computing Foundation (CNCF). Kubernetes is designed to manage containerized applications across multiple hosts in a cluster, abstracting away many of the underlying infrastructure details and providing a consistent interface for managing and deploying applications. With Kubernetes, developers can focus on building their applications and let the platform handle the deployment and scaling of the underlying infrastructure. Some of the key features of Kubernetes include automatic scaling, load balancing, self-healing, and rolling updates. Kubernetes also provides a rich set of APIs and tools for managing containers, including the kubectl command-line tool and a web-based dashboard. Additionally, Kubernetes is highly extensible, with a large ecosystem of plugins and third-party tools that can be used to extend its functionality.
A recommendation engine is a software system designed to provide personalized recommendations to users based on their past behaviors, preferences, and interests. The primary goal of a recommendation engine is to enhance user experience by helping users discover relevant and interesting items, such as products, services, content, or other users, that they may not have discovered on their own. Recommendation engines use various techniques, such as collaborative filtering, content-based filtering, and hybrid filtering, to analyze user data and make recommendations. Collaborative filtering involves analyzing user behavior data, such as purchases or ratings, to find similar users and recommend items that other users with similar interests have enjoyed. Content-based filtering, on the other hand, analyzes the properties of items, such as their descriptions or attributes, to recommend items that are similar to those that a user has previously expressed interest in. Overall, recommendation engines are widely used in various industries, such as e-commerce, media, social networking, and online advertising, to provide personalized recommendations that enhance user engagement and drive business revenue
A prediction engine is a software or machine learning model that is designed to make predictions about future events or outcomes based on historical data and statistical analysis. It is a type of artificial intelligence (AI) that can be used to forecast future trends, identify potential risks, and guide decisionmaking in a variety of industries, such as finance, healthcare, marketing, and manufacturing. The prediction engine works by analyzing patterns and relationships in large datasets, such as customer behavior, financial transactions, or sensor data, and then using these insights to make accurate predictions about future events or outcomes. It can also learn from new data over time to improve its accuracy and refine its predictions. The applications of prediction engines are vast and varied, ranging from predicting consumer behavior to forecasting weather patterns to identifying potential fraud in financial transactions. Ultimately, prediction engines can help businesses and organizations make better-informed decisions and improve their performance by anticipating future events and trends
Data analytics refers to the process of collecting, cleaning, processing, analyzing, and interpreting large sets of data to extract insights and make informed decisions. The field of data analytics utilizes various tools and techniques such as statistical analysis, machine learning, data visualization, and data mining to extract meaning from data. Data analytics can be used in a wide range of applications, from business and marketing to healthcare and scientific research. It can help organizations identify trends, patterns, and anomalies in their data, which can be used to optimize processes, improve decision-making, and gain a competitive advantage. In summary, data analytics is the process of examining and interpreting large data sets to discover meaningful insights and inform decision-making. Customers can use data analytics in several ways to improve their business outcomes. Here are some examples: Customer Segmentation: By analyzing customer data, businesses can identify patterns and characteristics that distinguish different customer groups. This allows businesses to create targeted marketing campaigns, tailor product offerings to specific groups, and optimize pricing and promotions. Product Recommendations: By tracking customer behavior and preferences, businesses can use data analytics to provide personalized product recommendations to customers. This can help increase customer engagement and sales. Customer Churn Prediction: By analyzing customer behavior and usage data, businesses can predict which customers are at risk of leaving and take proactive measures to retain them. Sales Forecasting: By analyzing historical sales data, businesses can forecast future sales and adjust their strategies accordingly. This can help businesses optimize inventory management, staffing, and other operational activities. Website Optimization: By analyzing website traffic data, businesses can identify which pages and content are most engaging to customers, and optimize the user experience to increase conversions and sales. Overall, data analytics can provide valuable insights into customer behavior, preferences, and needs, which businesses can use to improve their operations, increase customer satisfaction, and drive revenue growth.
Cloud computing is a technology that allows users to access computing resources, such as processing power, storage, and software applications, over the internet, rather than using their own physical hardware and infrastructure. Cloud computing providers offer a range of services, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), which can be used by individuals or businesses to run their applications and store their data. IaaS allows users to rent computing resources, such as virtual machines, storage, and networking, on a pay-per-use basis. PaaS provides a platform for developing, testing, and deploying software applications, while SaaS offers pre-built software applications that can be accessed and used through a web browser or a mobile app. Cloud computing offers several benefits, including cost savings, scalability, reliability, and flexibility. By leveraging cloud services, users can reduce their capital and operational expenses, while benefiting from the ability to quickly scale up or down their computing resources as needed. Additionally, cloud computing providers typically offer high levels of availability, security, and disaster recovery, which can help ensure that applications and data are always accessible and protected.
Database migration refers to the process of moving data from one database to another. This is typically done when an organization needs to upgrade their database software or move to a new system altogether. The migration process can be complex and time-consuming, as it involves moving large amounts of data and ensuring that it is correctly formatted and indexed in the new database. There are several steps involved in a database migration, including: Planning: This involves defining the scope of the migration, identifying the source and target databases, and outlining the migration strategy. Extracting data from the source database: The data is extracted from the source database, typically using an ETL (Extract, Transform, Load) tool. Transforming the data: The data is then transformed into a format that is compatible with the target database. This may involve restructuring the data or converting it to a different data type. Loading the data into the target database: The transformed data is loaded into the target database, often using an ETL tool or a bulk-loading utility. Verifying data integrity: After the data has been loaded into the target database, it is important to verify that it has been correctly migrated and that there are no data integrity issues. Testing: Once the data migration is complete, it is important to thoroughly test the new database to ensure that it is functioning correctly. It is important to note that database migration can be a complex process that requires careful planning and execution. Organizations should ensure that they have the necessary expertise and resources to carry out the migration effectively.
Database performance and fine tuning
Database performance and fine tuning refer to the process of optimizing a database system to maximize its efficiency and speed. This involves a range of techniques and strategies that can be used to improve database performance and minimize the impact of performance issues. Some of the key techniques used for database performance and fine tuning include: Index optimization: Indexes are used to speed up database queries by allowing the database system to find data more quickly. Optimizing indexes involves choosing the right type of index for each table and optimizing the configuration of existing indexes. Query optimization: Query optimization involves analyzing queries and identifying ways to optimize their performance. This might involve changing the structure of the query, modifying the indexing strategy, or changing the way data is stored. Server configuration: Server configuration involves optimizing the hardware and software settings of the server hosting the database. This might include adjusting memory settings, disk I/O settings, or CPU usage settings to maximize performance. Database schema design: The design of the database schema can have a significant impact on performance. A well-designed schema can reduce the number of joins required to retrieve data, which can improve query performance. Data partitioning: Data partitioning involves breaking up large tables into smaller pieces, which can improve performance by reducing the amount of data that needs to be accessed for each query. Data caching: Data caching involves storing frequently accessed data in memory so that it can be accessed more quickly. This can be done at the database level, or at the application level. Overall, database performance and fine tuning is a complex and ongoing process that requires ongoing monitoring and optimization. By using a range of techniques and strategies, it is possible to create a high-performance database system that meets the needs of even the most demanding applications.
Application performance tuning
Application performance tuning involves improving the efficiency and responsiveness of software applications to meet the desired performance objectives. Here are some steps that can help improve application performance: Identify performance bottlenecks: The first step in application performance tuning is to identify the areas of the application that are causing performance issues. This can be done by using profiling tools, logging, and monitoring tools. Optimize code: Once the performance bottlenecks have been identified, the next step is to optimize the code to remove the bottlenecks. This can involve optimizing algorithms, improving data structures, and reducing unnecessary computations.
Aluminium 6061-T6 is a widely used alloy in various industries, including aerospace, automotive and marine due to its excellent combination of strength, corrosion resistance and formability. Its mechanical properties, particularly fatigue performance can be further improved through suitable processing techniques
Despite the benefits of FSP on improving the fatigue properties of aluminum alloys, there are still some challenges that need to be addressed to optimize the process and achieve the desired results. Some of these challenges include - Process parameters, tool material, material characteristics, heat treatment and cost and time
Optimizing the FSP process parameters for improving the fatigue properties of Al 6061-T6 plates requires a comprehensive approach that considers the effect of process parameters on the microstructure and mechanical properties of the material. By using a combination of experimental, computational, and analytical methods, it is possible to identify the optimal process parameters that lead to the desired properties. Machine learning can used to optimize Friction Stir Processing (FSP) parameters for improving the fatigue properties of Al 6061-T6 plates. The use of machine learning algorithms can significantly reduce the time and cost of optimization by automating the process of parameter selection Data Collection: The first step is to collect data on the FSP process and the resulting fatigue properties of the material. This data can include process parameters such as tool rotation speed, traverse speed, and axial force, as well as the resulting fatigue strength of the processed material. Data Preparation: The data must be cleaned and pre-processed before it can be used for machine learning. This involves removing any outliers, filling in missing values, and transforming the data into a suitable format for machine learning algorithms. Feature Engineering: Feature engineering involves selecting the most relevant features or process parameters that are likely to have the most significant impact on the fatigue strength of the material. Model Training: Machine learning algorithms can be used to build models that predict the fatigue strength of the material based on the selected process parameters. This involves selecting an appropriate algorithm (linear regression), splitting the data into training and validation sets, and tuning the hyperparameters of the model. Model Evaluation: The trained model can be evaluated using various performance metrics such as root mean squared error (RMSE) and R-squared (R2) to determine its accuracy and reliability. Parameter Optimization: The trained model can be used to predict the fatigue strength of the material for a wide range of parameter combinations. The model can then be used to identify the optimal combination of parameters that maximize the fatigue strength of the material. .
One area of focus for future research is the development of deep learning models for predicting the fatigue strength of materials based on FSP parameters. Deep learning models are capable of processing large amounts of data and can identify complex patterns that may not be visible using other machine learning algorithms
machine learning is a powerful tool for optimizing FSP parameters for improving the fatigue properties of Al 6061-T6 plates. By automating the process of parameter selection, machine learning can significantly reduce the time and cost of optimization while improving the accuracy and reliability of the results The specific optimization method used and the target fatigue strength. However, in general, using machine learning can lead to significant improvements in the accuracy and efficiency of the optimization process. Applied linear regression/support vector regression (SVR) algorithm to build a model that predicted the fatigue strength of the material based on the FSP process parameters Feature variables are rotational speed, feed rate, tilt angle, no of pass and axial force Target variable is ultimate tensile strength (UTS) Model predict the UTS with an accuracy of 96.7%
Potato Leaf Disease Prediction refers to the use of machine learning algorithms to predict the occurrence and severity of leaf diseases in potato plants. These diseases can have a significant impact on potato crop yields and quality, so predicting and identifying them early can help farmers take appropriate actions to mitigate their effects There are various types of potato leaf diseases, including late blight, early blight, black dot, brown spot, and leaf roll virus. These diseases are caused by different pathogens and have distinct symptoms, such as yellowing or browning of leaves, lesions on leaves, and wilting of plants potato leaf disease prediction is a promising application of machine learning in agriculture that has the potential to help farmers optimize their yields and improve their bottom line.
potato leaf disease is the need to accurately predict and identify leaf diseases in potato plants early in their development to mitigate their impact on crop yield and quality The challenge lies in developing accurate and effective methods for predicting and identifying potato leaf diseases Traditional methods for disease identification rely on visual inspection by trained professionals, which can be time-consuming and subject to human error and the accuracy of visual inspection may be affected by factors such as lighting conditions and the experience of the inspector
Potato leaf disease prediction using Convolutional Neural Networks (CNN). The CNN approach to potato leaf disease prediction involves using a deep learning algorithm that is specifically designed to analyze image data and identify patterns and features that are indicative of different types of leaf diseases in potato plants Data collection: A large dataset of labeled images of potato leaves affected by different types of diseases (such as early blight, late blight, and leaf roll virus) and healthy leaves is collected. Data pre-processing: The images are pre-processed by resizing, cropping, and normalizing them to make them suitable for input into the CNN model. Model architecture: A CNN model is designed with multiple layers of convolutional, pooling, and fully connected layers to extract features from the input images and classify them into different types of diseases. Model training: The model is trained on the pre-processed dataset using backpropagation and stochastic gradient descent optimization to minimize the loss function. Model evaluation: The trained model is evaluated on a separate set of validation data to assess its accuracy in predicting the correct type of disease. Model deployment: The trained CNN model is deployed in the field by farmers who can capture images of potato leaves using a smartphone or other digital camera and feed them into the model for analysis. The model then predicts the type of disease affecting the leaves, along with a confidence score.
Different leaf disease prediction such as Grapes/Cashew involves selecting relevant features from the input images that can help distinguish healthy leaves from those affected by different types of diseases. Here are some common features used in leaf disease prediction includes-
The result of potato leaf disease prediction using CNN can vary depending on various factors such as the quality and quantity of the dataset, the architecture of the CNN model, the pre-processing of input images, and the evaluation metrics used to assess the performance of the model The performance of the CNN model is evaluated using metrics such as accuracy, precision, recall, and F1 score Model predict the potato leaf disease/normal with the accuracy of 95%. BUSINESS OUTCOME • Early detection and diagnosis • Improved crop yields • Reduced use of pesticides • Cost saving • Increase food security Framework Used: TensorFlow and Keras Language Used: Python Hardware Used: T4-GPU
The Client is attempting to build a social media platform tailored towards solving a very niche area of the digital market, for which a lot of specialized learning and implementation is necessary. As a halo project, a recommender system is being implemented to customize and tailor content to the users of the social media platform. A lot of advancements are being made with parallelization with GPUs, scaling across different nodes, as well as the highlight feature in the pipeline: localizing compute and making use of the compute power of the edge device to run code to remove the compute complexity scaling problem altogether.
Crezam is the premium creative ecosystem that brings together artists of all kinds to create and pursue opportunities as both professionals and patrons. It brings together creators & design agencies on a single platform. The work involved building end-to-end product development with a recommendation engine, BOT & others. The services involved creating user profiles, social feeds, Tests & badges & job sections. The application is built on Java at the backend with microservices & flutter at the frontend.Technologies & framework used :
The Client aims to make a newsletter-style carousel of productive articles for their Employees, for which a solution was built that scours the internet, finds good blog articles over 16 different categories, and renders them into friendly email blasts that can be circulated company-wide.
Bluechink is associated with Sakku foundation (non-profitable organization) which has a vision to bring differently abled children to the mainstream society.
Goal and Summary: To meet the ever-increasing demand to store multimedia more efficiently with no perceivable loss in quality from the Client, a solution was developed to smartly manage, compress and store multimedia content being uploaded to their servers. This code was imperative to be realtime in nature, so as to facilitate minimal perceived lag on the Client’s end, as this use-case is very read-heavy. Acceleration with Nvidia GPUs was obtained and successfully implemented, with a final compression ratio (in terms of filesize) of about 22.4:1 with no perceivable lag and loss in color information. Data Collected and Processed: A projection chart of the estimated storage needs pre-optimisation, as well as a specification list with respect to the desired quality of the images and media post compression. Additionally, a thorough investigation was also conducted on the feasibility of the status-quo installation, with research about the media type breakup, typical sizes and read frequency of these files. Methodology: A number of Python-based libraries concerning media processing were investigated, including, but not limited to moviepy and ffmpeg. Once the prototype had been established, the engine was augmented with pandas capabilities to automate the reading of the data influx, as well as the management of the files. Once this was established in the first phase of the project, a further optimization requirement in terms of compute complexity and costs were identified, and to accelerate the code, moving it away from CPU compute towards GPGPU tasks, libraries such as OpenCL and CUDA were looked at. The final implementation uses pandas as the identification and management layer, the moviepy library as the processing backend, and CUDA as the GPU acceleration library, with some supplementary code employing regular expressions and system file handling to write the framebuffer contents to a file, as well as the json library to communicate with the rest of the server stack
Goal and Summary: This suite of applications encompassed a way to manage, track and optimize inventory of the said product depending upon geographical areas with higher demand. This suite of applications included a dashboard for easy view and accountability by the management, and a field application to log sales, request inventory, and verify a sale with digital signature, fingerprint, as well as GPS tagging. Data Collected and Processed: The implementation began with an identification of the requirements of the organization. Accordingly, a Business Requirements Document was prepared, and a suitable track of development and deployment of this project was ascertained. The BRD included the projected user base of the application, estimated traffic on the applications, as well as the takeaways from the application to be used in other departments to propel product sales. Methodology: Keeping cognizance of the exigent nature of the need for implementation of this project owing to excessive sales bleed happening in the company, coupled with a lack of accountability of stock owing to supply chain issues, a no-code platform was selected to be the best route, since functionality and quick deployability superseded aesthetics in this case. There were two versions of the application designed: The initial production-level suite of 3 applications—one each for the field sales representatives, the management, as well as the channel partners—was developed and deployed on Google AppSheet for the best possible integration with the rest of the organization, which uses Google Cloud for its productivity. The design commenced on August 5th 2022, with a production test on the 23rd of August 2022. The second version of the suite was made in Microsoft Power Apps, with screens designed in Figma and Adobe Illustrator, owing to the organizational demand for enhanced UI/UX and the presently found bandwidth to improve these things. This was delivered less than 20 days later. Impact: The Company in question was able to regain accounts of their inventory, as well as ensure minimal bleed in terms of mismanaged, damaged or otherwise compromised stock. The average turnaround time for an end seller to have inventory delivered to them was reduced to intra-day, a departure from the existing weekly refresh cycle that was causative of returns.
Bluechink is associated with Sakku foundation (non-profitable organization) which has a vision to bring differently abled children to the mainstream society.