Multi-tenant architecture plays a vital role in cloud computing by allowing efficient sharing of resources while ensuring tenant isolation and security. There are various types of multi-tenant architectures, each with its unique approach to resource sharing and data management. Understanding these options is essential for businesses and software developers to select the most suitable model for their specific requirements.
In this model, all tenants share the same database and database schema. Key features include:
In this model, there is a single database instance, but each tenant has its own database schema. Key aspects include:
This model assigns each tenant its own database instance and schema. Key features include:
Multi-tenancy-based applications allow you an additional layer of customizations by maintaining the original codebase which will be the same for all the users as well as new customers.
Multi-tenancy will speed up the process of upgrades, which will also result in cost-saving. It is not necessary to configure a certain set of resources, and your environment may be quite basic depending on your architecture. As the application grows in size, there are more and more opportunities to save money. The vendor can pass on the savings to customers as a result of the decreased cost of doing business.
With the help of Multi-tenant, the vendor does not need to update every instance of their software; they can just simply update a single, central application or codebase and make the changes ready for the users. It is not necessary to configure a certain set of resources, and your environment may be quite basic depending on your architecture. As the application grows in size, there are more and more opportunities to save money. The vendor can pass on the savings to customers as a result of the decreased cost of doing business.
AI, or artificial intelligence, is pervasive. It’s possible that you are utilizing it without even realizing it in some capacity. Machine Learning (ML) is a common application of artificial intelligence (AI), where computers, software, and other devices operate through cognition, which is extremely similar to the human brain.
If you purchase an item online or search about it then you will notice how the platform will give you recommendations for similar items. Also, if you purchased a product online some days back then you will get emails for shopping suggestions. This enhances the shopping experience, but do you know this is possible with the help of Machine Learning?
Today, many of you may be aware of virtual personal assistants. Siri, Alexa, and Google Home are some of the most popular virtual assistants out there today. These virtual assistants can have a conversation with you and thus you can ask them anything you want. For example, you can ask them about today’s weather, flight details, etc. They can also be helpful for your work as you can ask them to design your schedule and what your schedule is today. Likewise, these virtual assistants can help in doing a lot of tasks.
These personal assistants use machine learning, which is crucial to their functionality since it allows them to gather and hone knowledge based on your past interactions. This data set is then used to produce results that are customized based on your preferences.
Nowadays, you will find the option to chat with a bot instead of humans on many websites. Websites use this bot as they can save time and cost for companies. These chatbots extract information from the website and then provide it to the customers. The chatbots are able to better comprehend customer inquiries and provide them with responses, which is made feasible by its machine learning algorithms.
Today, online frauds are on a huge rise. But did you know machine learning can also aid in online fraud detection? One application of machine learning that is showing promise for making cyberspace safe is the monitoring of online financial fraud. For instance, PayPal uses machine learning to prevent money laundering. With the use of a suite of tools, the organization is able to compare millions of transactions and discern between transactions that are legal and those that are not between buyers and sellers.
Machine learning is used by Google and other search engines to enhance your search results. The backend algorithms monitor your response to the search results each time you run one. The search engine will infer that the results presented were relevant to your query if you open the top results and remain on the page for a considerable amount of time. In a similar vein, the search engine surmises that the results it returned did not meet its criteria if you go to the second or third page of the results but do not click on any of them. In this sense, the search results are enhanced by the backend algorithms.
With MLaaS, you can do away with costly software, hardware, and maintenance costs as well as the requirement to hire and train data scientists and engineers. Customers can scale up or down based on usage, and they only pay for what they use.
Machine learning models can be scaled, updated, and deployed quickly and easily with MLaaS. They can also be integrated with current workflows and systems. The cloud’s processing power and storage capacity can also be used by users to manage big and complicated data collections.
An advantage of machine learning as a service (MLaaS) is its ability to increase accuracy by utilizing the most recent developments in machine learning algorithms, data security, quality, and collective intelligence of the cloud to produce predictions that are more accurate and dependable.
It’s crucial to prevent tenants from accessing other tenants’ data or models without permission. Treat models with the same care as the raw data used to train them. Make sure tenants know how their data is used for training models and how models trained on others’ data might affect their work.
Custom models are trained exclusively on data from one tenant and exclusively utilized for that tenant. These models are suitable in scenarios where tenant data is sensitive or when there is limited potential to glean insights from one tenant’s data and apply it to another. The accompanying diagram illustrates the architecture of employing tenant-specific models for two tenants:
In setups employing shared models, all tenants conduct inference utilizing a common model. These shared models could be pre-existing models obtained either through acquisition or community sources. The provided diagram demonstrates how a single pretrained model can be leveraged for inference across all tenants:
We can also develop our own shared model based on trained data of all tenants. This can be depicted in the following figure.
When training a model using data from multiple tenants, it’s essential to obtain clear consent from each tenant regarding the use of their data. Additionally, take measures to anonymize or remove any identifiable information from the data to protect tenant privacy.
It’s also important to anticipate potential objections from tenants regarding the use of their data for training models applied to other tenants. Consider implementing procedures to address such objections, such as providing the option for tenants to exclude their data from the training dataset if they choose that. This ensures that tenant concerns regarding data usage are addressed and respected.
In this hybrid approach involves training a base model on a shared dataset and fine-tuning it using tenant-specific data. It strikes a balance between resource efficiency and customization, offering tenants the flexibility to tailor models to their needs while leveraging shared resources. The provided diagram demonstrates how a single pretrained model can be leveraged for inference across all tenants:
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