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【2021 Application Example】 Hamastar Technology Builds an AI Model Management Platform to Accelerate the Application of AI

Riding the AI hype train, financial service providers are using their solid foundation in the industry to not only transform themselves, but also assist their customers with transformation!

Hamastar Technology, which has been established for over two decades, has been developing AI technology and assisting industry customers with the implementation of AI in recent years. Hamastar Technology believes that to implement a complete AI project, in addition to AI theoretical knowledge, data analysis, and model training capabilities, it is also necessary to develop APIs for data, establish databases, develop front-end RWD web pages, and even consider layout design and user experience based on customer needs. These tasks create technical barriers for AI startups. Even from the perspective of companies that have reached a certain scale, it is hard to accumulate technical experience and accelerate business growth due repeatedly investing manpower developing similar functions in each project.

Institutional customers still require high level of customization for AI

Using the requirements of government Agency A implemented by Hamastar Technology as an example, users must control false information from specific channels. The platform needs to provide data ingestion functions for training models and predictions, and can complete natural language processing (NLP) text classification model training and use. When the model discovers false information, it needs to immediately notify responsible personnel through messaging software. The need of Agency B is to use an AI model to automatically classify petitions and immediately provide information on past cases as reference for the petitioner or officer. Although the project models are similar (data ingestion, model prediction, warning / notification), the required functions still need to be separately developed for individual projects, and existing programs and models cannot be reused to speed up the implementation of subsequent projects.

After in-depth discussion, Hamastar Technology found that pain points of enterprises implementing AI projects include high implementation costs and lengthy project schedules. It is difficult for a single enterprise to simultaneously have data scientists, analysts, engineers, and designers. Current projects are all focused on solving the needs of specific fields, and it is difficult to reuse the AI models in other fields of application. At the same time, the tools are concentrated in AI projects and cannot provide customers with total solutions.

In other words, due to the "limited manpower," "restricted fields," and "insufficient tools" of AI service providers, the implementation of AI technology projects requires high costs or lengthy timelines. These are common problems that companies urgently need to solve. Therefore, if there is an AI model application service management platform, it will be able to solve the above difficulties and not only reduce costs, but also accelerate project implementation and provide customers with one-stop solutions.

AI model application service management platform assists in quickly completing projects

Therefore, with the support of the AI project of the Industrial Development Bureau, Ministry of Economic Affairs, Hamastar Technology carried out the "AI Model Application Service Management Platform (AISP) R&D Project" and engaged in the R&D of AISP products. The purpose is for AI service providers to complete the AI projects with twice the result using only half the effort.

The AI Model Application Service Management Platform offers AI-specific problem-solving solutions.

▲The AISP provides one-stop AI solutions

AI service providers can quickly assemble required functions, such as data API, model management, and model prediction result monitoring / subscription through existing module functions of the AISP. It also provides commonly used graphical tools to help companies quickly design interactive charts or dashboards required by users, effectively reducing the labor costs required to execute projects, shortening the solution POC or implementation time, and accelerating the implementation and diffusion of industry AI.

In terms of product business model, in the short term, the company will extensively invite IT service providers with expertise in the field of AI to work together, and use platform services to solve the AI implementation problems faced by requesting units in various field, gradually building trust in the platform brand. In the mid-term, the company hopes to gradually expand the market based on its past success, and form strategic alliances with multiple IT service providers to solve more and wider problems in specialized fields and provide more solutions for units to choose from.

The platform combines field experts to jointly expand overseas markets

In the long term, after establishing AI strategic alliances in various specialized fields, the platform will have a large number of AI solution experts for specialized fields. After accumulating a large amount of successful project experience, Hamastar Technology hopes that the AISP will be able to work with experts / companies to expand into the international market.

Harmastar Technology Co., Ltd. was formed in 2000 by recruiting numerous senior professional managers and technical experts in related fields. It is committed to software technology R&D and services, and aims to develop into an international software company, actively creating opportunities for international cooperation in the industry. Under the excellent leadership of its first president, the company has rapidly grown into a major software company in Taiwan.

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【導入案例】赫銳特科技VCSEL封裝元件瑕疵導入AOI檢測 提升產能效率20
HRT Technology Improves Production Efficiency by 20% Through AOI Detection of Defects in VCSEL Packaging

In 2017, the launch of the iPhone X made 3D sensor technology used in Face ID highly popular, which drove the development of VCSEL, a core component in the 3D sensor module In the detection of defects in incoming packaged VCSEL, the use of AI inference models can solve the industry's issue with low yield and improve reliability to 95 VCSEL technology currently can be used in many applications and various end consumer markets, including robots, mobile devices, surveillance, drones, and ARVR VCSELs are a good solution in applications that require high-speed modulation capabilities, such as cameras and biometrics VCSEL technology has a wide range ofnbsp applications, including in drones Pictured Zoyi Technology's Agricultural Drone VCSEL technology has a wide range of applications, AI technology assists in defect detection HRT Technology stated that the packaged VCSEL market is also facing strong price competition from competitors, and needs to further reduce costs and enhance product 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to solve the problems above, HRT Technology hopes to use AI to monitor the size and appearance defects of epoxy resin components in the VCSEL epoxy resin lens incoming stage, verifying whether their dimensions meet specifications, whether the cutting edges are smooth, and whether there are any defects in their appearance Since traditional incoming material inspection requires a rough visual inspection by humans to distinguish the quality The problem of image collection needs to be solved first to successfully collect image data Therefore, HRT Technology first developed an Automated Optical Inspection AOI device, which includes X, Y, Z three-axis motion, high-resolution cameras, and related control software to automatically record images After collecting the image data, opencv aligns the test image and a normal image to determine differences between the two images, and then pixel mapping is used to compare the pixel area to complete initial screening Manual labeling is carried out according to the image classification above, including samples that are normal, have defects in appearance, or have different shape characteristics, and then algorithm training and verification is carried out Residual neural network ResNet or other related algorithms are used for deep learning to identify the quality of lenses Implementation of AOI inspection improves production efficiency by 20 and above Comparing the differences before and after the implementation of AI image inspection, the incoming VCSEL lens inspection before implementation only involved manual inspection of the appearance The lens is packaged on the VCSEL package that has completed die bonding After passing the general light up test, the final reliability test high temperature reflow is performed Failed samples go into the rework process However, after the implementation of AOI inspection, it can screen defective lenses sooner and reduce the cost of subsequent materials input, it can also reduce the need for 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Testing Seat Contact Components AI Intelligent Flaw Detection

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Realizing the dream of unmanned stores, Magpie Life is building the future of the smartphone industry

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Modeling function 1 Provides model training functions for two types of prediction problems regression Regression and time series Time Series Forecast nbsp2 Supports Auto ML automatic modeling, and the best model is recommended by the system Integrated models can also be established to improve model accuracy nbsp3 Supports multiple algorithm types Random Forest, XGBoost, GBM and other algorithms nbsp4 Supports a variety of time series models exponential smoothing, ARIMA, ARIMAX, intermittent demand, dynamic multiple regression and other models nbsp5 Supports a variety of model evaluation indicators R, MAE, MSE, RMSE, Deviance, AUC, Lift top 1, Misclassification and other indicators nbsp6 Supports automatic cutting of training data sets and Holdout verification data sets, and can manually adjust the ratio nbsp7 Supports automatic model ensemble learning Stacked Ensemble, balancing function learning Balancing Classes, and Early Stopping nbsp8 Supports the creation of multiple models at the same time The system will allocate resources according to modeling needs, so that modeling, prediction and other tasks have independent computing resources and do not affect each other In the overall server space With an upper limit, computing resources can be used efficiently nbsp9 It has in-memory computing function, which can use large-capacity and high-speed memory to perform calculations to avoid reading and writing a large number of files from the hard disk and improve computing performance 3 Data concatenation function Using API grafting and complete data concatenation automation, there is no need to manually import data, improving user experience 4 Chart analysis function Provides visual charts and basic statistical values for product sales AI data analysis solutions have two major advantages 1 Entrepreneurship machines can be rented and sold at low cost to open unmanned physical stores and cooperate with the chain retail industry Through smart machines, entrepreneurs can rent and sell them at a lower cost than the store rent Cost of running a retail business Two cooperation models, machine sales and leasing, are provided, and the choice is based on the evaluation of the industry 2 Various types of products are put on the shelves Products are sold anytime and anywhere 24 hours a day Up to 60 kinds of diversified products can be put on the shelves Large transparent windows enhance the visibility of products Regular replenishment and tracking of product sales status are available, and product types can be adjusted according to needs Recently, the line between the Internet and the physical world has blurred, the way customers interact has changed significantly, and consumer demand is changing and personalized The retail industry is facing unprecedented challenges and uncertainties, and mastering data has become key AI data analysis solutions can help the retail industry quickly activate large amounts of data, create seamless personalized experiences, optimize the operational value chain and improve efficiency, and strengthen the core competitiveness of enterprises 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」