【2021 Solutions】 Customized AI Models: Jia Heng Technology Helps Clients Accelerate AI Application
After the COVID-19 pandemic, the push for digital transformation using digital tools has accelerated across all industries. However, for business owners, the question arises: Is it worth implementing AI? What benefits does it bring to the company? In fact, there are many AutoML platforms currently available that help businesses speed up the introduction of AI and build AI models, simplifying the adoption of AI for companies.
Businesses face significant challenges in adopting AI, and automated machine learning platforms offer solutions
Jia Heng Technology's General Manager Liang Baifeng stated that businesses face challenges such as scarcity of talent, data handling, timing of modeling, integration with production, technology mastery, and cost efficiency when adopting AI. Nevertheless, not every process needs to incorporate AI technology. What businesses need are AI custom solutions that meet business requirements. Thus, AutoML is a core tool for businesses applying AI technology. Previously, to build 100 AI models, 100 modeling experts were needed. With AutoML, only a few data scientists are required to build 100 models. Once AI models are established, they can be integrated into business production processes. Thus, complex application scenarios can be addressed through highly customized modeling to meet client demands.
In the process of enterprise AI adoption, it used to rely heavily on AI experts, but in the future, it will be driven by industry experts, focusing on solving real business application scenarios as the key to success. Liang Baifeng thinks there are four key phases:
One, Scenario Selection: Deciding whether machine learning is the right approach for solving the problem.
Two, Data Preparation: Data is just material. Choosing the 'right' and 'effective' data is crucial.
Three, Model Building: Focus on the efficiency of model design, a combination of multiple models is necessary to solve problems.
Four, Production Integration: The model meets the restrictions of production while maintaining flexibility based on production conditions. To address the issues of diverse business scenarios, high implementation hurdles, long cycle times, and high costs faced by traditional AI model design, it is essential to utilize AutoML technology to create an automated platform, effectively resolving the developmental and implementation challenges of AI.
DarwinML's Four Core Technologies help enterprises start from scratch in model design
Developed by Jia Heng Technology, DarwinML is an AutoML platform for designing AI machine learning models based on genetic evolution theory. DarwinML uses an evolutionary approach to automatically design and optimize machine learning and deep learning models, featuring excellent capabilities in model generation and hyperparameter optimization, starting from 'zero' to design models automatically.
The four core technologies of DarwinML are described as follows:
One, Model Gene Bank: Collects a large number of algorithms and basic modules that can be applied to Deep Learning, Machine Learning, and Data Feature Extraction.
Two, Auto-evolution Algorithm: Utilizes genetic algorithms, model interpretative statistical methods, and reinforcement learning techniques. In the continuous model evolution, it enhances model quality.
Three, Complete Model Lifecycle Management: Uses DarwinML and Darwin Inference to build a closed ecosystem for model generation, use, and re-optimization.
DarwinML significantly shortens the modeling time, and efficiency is markedly improved
In the traditional model design process, originally from data feature extraction, model design, model training to parameter adjustment, it took AI engineers 3-6 months to manually model. However, using DarwinML for automatic modeling can shorten it to 3-7 days, significantly reducing time and markedly improving efficiency. DarwinML can automatically generate models and rule sets based on objectives, with modules possessing self-evolving capabilities. Its core technologies include machine/deep learning/model gene banks, model evolutionary design algorithms, and big data parallel computing technology, among others, yielding significant benefits such as:
One, Data organization, data labeling, and data cleaning are semi-automated, reducing dependency on the workload and volume of labels by 40%.
Two, Machine learning modeling time is reduced to minutes, with a modeling capacity 5%-10% higher than traditional modeling.
Three, Deep learning modeling time is reduced to hours, achieving a standard consistent with the industry's best models but more straightforward and faster.
(This article is organized from selected content of the 'AI Engineering Online Meetup')
「Translated content is generated by ChatGPT and is for reference only. Translation date：2024-05-19」
