See how multiple models work and companies have successfully implemented this approach to increasing performance and cost reduction.
Using the strengths of various AI models and joining them into one application can be a great strategy that will help you meet your performance goals. This approach uses the power of multiple AI systems to improve accidents and reliability in complex scenarios.
More than 1.800 AI models are available in the Microsoft Model catalog. Even more models and services are available through Azure Openai Service and Azure AI Foundry, so you will find the right models to create an optimal AI solution.
Let’s look at how multiple models are working, and explore scenarios in which companies have successfully implemented this approach to increasing performance and cost reduction.
How Multiple Models access work
The approach of multiple models includes a combination of different AI models to a more efficient solution of complex tasks. Models are trained for different tasks or aspects of a problem such as language understanding, image recognition or data analysis. Models can work in parallel and process different parts of input data simultaneously, direct to falling models or be used in different ways in the application.
Suppose you want to see the finely tuned vision model with a large language model, which is to perform several complex tasks classification in conjunction with questions in natural language. Or maybe you have a small model tuned to generate SQL queries on your SCCHCHEMA database and want to look at it with a large model for more general tasks such as getting information and helping research. Both of these boxes offer approaches to multiple models that offer you adaptability to create an understanding of AI solutions that meet the specific requirements of your organization.
Before immersing a more model strategy
First, identify and understand the result you want to achieve, because this is the key to selecting and deploying the right AI models. In addition, each model has its own set of merit and challenges that need to be considered to ensure that you choose the right one for your goals. Before introducing a more model strategy, several items should be considered: including:
- The intended purpose of the models.
- Application requirements around the model size.
- Training and management of specialized models.
- The necessary different degrees of accuracy.
- Manage application and models.
- Security and distortion of potential models.
- Costs of models and a year -old scale cost.
- The correct programming language (Check DevqualitySyeval for the current information about the best languages to be used with specific models).
The weight you give to each criterion will depend on factors such as your goals, technological magazines, resources and other variables specific to your organization.
Let’s take a look at some scenarios and several customers who have implemented more models into their work.
Scenario 1: Direction
Routing is when AI and machine learning technology optimizes the most effective ways for use, such as call centers, logistics and more. Here are examples:
Multimodal routing for different data processing
One of the innovative applications of processing multiple models is the road task at the same time through various multimodal models that specialize in processing specific data types such as text, images, sound and video. For example, you can use a combination of a smaller model such as the GPT-3.5 turbo, with a multimodal model of a large language such as GPT-4O, depending on modality. This routing allows the application to process multiple modalities by directing each type of data to the best model for it, increasing the overall performance and versatility of the system.
Routing experts for specialized domains
Another example is professional routing, where challenges are direct to specialized models or “experts” based on specific areas or field references in the role. By implementing professional routing, the company ensures that different types of user queries are solved by the most watched model or AI service. For example, technical support issues may be direct to a model trained in technical documentation and ticket support, while general information requirements could be solved by a more non-purpose language model.
Expert routing can be particularly useful in fields such as medicine, where different models can be tuned for processing specific topics or images. Instead of relying on one large model, several smaller models such as the Phi-3,5-Mimini-Instruct and the Phi-3,5 Vision-Instruct most suitable model of the expert model could be used, increasing accuracy and Relative model output. This approach can improve AKCCAIR reaction and reduce large models.
Automatic manufacturer
One example of this type of routing comes from a large car manufacturer. They have implemented the Phi model, which quickly processes the most basic tasks, while at the same time directs more complicated tasks to a large language model such as the GPT-4O. The Offline Phi-3 quickly processes most data processing locally, while the online GPT model provides processing performance for larger and more complicated questions. This combination helps to use the cost-effective PHI-3 capabilities and at the same time effectively process more complex, business critical questions.
Wise
Another example shows how to use cases specific to the industry from professional routing. SAGE, leader in accounting, finance, human resources and wage technology for small and medium -sized enterprises (SMB), wanted to help its customers discover efficiency in accounting processing and increase productivity through driven AI services that could routine tasks and provide and provide provided provided and to provide and provide and provide and provide and provide and provide and provide real -time knowledge.
Recently, Sage has deployed Mistral, a commercially Available model of a large language, and gently tuned it with accounting data to deal with gaps in the GPT-4 used for their sage kopilot. This fine fine -tuning has made it possible to better understand the master and respond to accounting questions to categorize user questions more efficiently and then direct them to the relevant or deterministic systems. For example, while the model of the large out-of-the-box language could fight with the question of cash flow forecasts, the finely tuned version could direct the accuracy to a question-specific data and receiving response to users.
Scenario 2: Online and Offline Use
Online and offline scenarios allow dual benefits of storing and processing locally with the Offline AI, as well as using the online AI model to access globally available data. In this setting, the organization could run a local model for specific tasks on devices (such as Chatbot Customer Service), while still accessing an online model that could provide data in a wide context.
Hybrid deployment of a model for diagnosis of health care
In the healthcare sector, AI models could be deployed in a hybrid manner to provide online and offline capacity. In one example, the hospital could use the Offline AI model to process initial diagnostics and data processing locally in IoT devices. At the same time, the online model AI could be used to access the latest medical research from cloud databases and medical journals. While the model is processed by patients locally, the online model provides worldwide medical data. This combination of online and offline helps to ensure that employees can effectively perform patient evaluation while benefiting from access to the latest progress in medical research.
Intelligent home systems with local and cloud AI
In intelligent home systems, multiple AI models can be used to manage online and offline tasks. The AI offline can be inserted into a home network for basic functions such as lighting, temperature and safety system, allowing faster responsibility and allows essential services to work even during Internet outrage. Meanwhile, the AI online model can be used for tasks that require access to cloud services for updating and advanced processing, such as voice recognition and integrating intelligent devices. This dual approach allows intelligent domestic systems to maintain basic operations separately while using cloud skills for improved features and updates.
Scenario 3: Combination of tasks and larger models
Companies that want to optimize cost savings could consider a combination of a small but powerful SLM task such as a Phi-3 with a robust model of a large language. One way that this might work is the deployment of Phi-3-one of the family of powerful, small Microsoft language models with pioneering at low cost and low latency-on computational scenarios or applications with strict latency requirements together with performance Processing and larger model as GPT.
In addition, the Phi-3 could serve as an initial filter system or sorting, handling direct quarries and only escalate more nuancement or demanding GPT applications. This graded approach helps optimize the efficiency of the workflow and reduce unnecessary use of more expensive models.
Consciously building the settings of additional small and large models can potentially achieve cost -effective performance adapted to their specific cases of use.
Capacity
The A-Powred response engine capacity will load accurate answers for users in seconds. Thanks to the use of top AI technologies, the capacity provides organizations of personalized research assistant AI, which can smoothly expand in all teams and details. They needed a way to help unify various data sets and make information available and understand for their customers. Using Phi, the capacity was able to provide businesses with effective AI knowledge that increases accessibility, safety and efficiency, saving customers time and difficulties. After successful implementation of the Phi-3-Medium, the capacity now tested eagerly by the PHI-3,5-MOOE for production.
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Start with Foundry Azure AI
If you want to learn more about increasing the reliability, security and performance of investment in cloud and AI, explore the other sources below.
- Read about the Phi-3-Mini, which works better than some models twice its size.