How deploying machine learning models can enable significant business growth
In today’s world, businesses across all industries are generating massive amounts of data. With the advancements in artificial intelligence (AI), it has become easier than ever to derive insights from this data and make informed decisions. Using your AI a growing number of businesses are increasing their sales, conversion rate, returning customers and much more.
However, it is important to note that every business has unique data requirements and specific use cases that may not be met by pre-built solutions. This is where hosting your AI models becomes crucial. By hosting their own AI models, businesses have the flexibility to customise their models to fit their specific needs and ensure that they are tailored to their unique data sets. The pre-trained models and starting solutions are a great starting point, but they are not what will give you a competitive edge.
Additionally, with AI becoming a must-have technology for most businesses, hosting your own models can give you a competitive advantage by enabling you to make faster, more informed decisions and better meet the needs of your customers. Overall, hosting your own AI models is a critical step towards unlocking the full potential of AI for your business.
Using machine learning models to enhance your business is not just a nice-to-have, it is a must-have if you want to remain competitive in today’s fast-paced business environment. Machine learning models allow businesses to analyse vast amounts of data to gain insights that can inform decision-making, improve operational efficiency, and drive growth.
By leveraging machine learning models, businesses can optimise their operations, personalise customer experiences, and develop innovative products and services that meet the changing needs of the market. Failure to adopt machine learning can put a business at a disadvantage against its competitors who have already embraced this technology, making it imperative for companies to integrate machine learning into their operations.
In this post we will review some of the challenges with machine learning and what options we have for dealing with them.
Complexity with deploying machine learning models in production
Deploying AI models in production can be a challenging and time-consuming process due to their resource-intensive nature. Machine learning models, in particular, require significant computing power to run efficiently. This can be a major obstacle for businesses, especially smaller ones or those without dedicated machine learning infrastructure.
These models often require large amounts of memory and processing power, which can be challenging to provide without incurring significant costs. Additionally, training and tuning these models can take considerable time, which can further increase the resources required for deployment.
One reason for the resource-intensive nature of AI models is that they often require vast amounts of data to train effectively. For example, natural language processing (NLP) models may require millions of sentences to train effectively, while computer vision models may require hundreds of thousands of labelled images. Storing and processing these large datasets can be a major challenge, requiring specialised hardware or cloud infrastructure.
Furthermore, deep learning models, which are a type of machine learning model that can achieve state-of-the-art performance on complex tasks, can be particularly resource-intensive. These models consist of multiple layers of interconnected neurons that are trained to identify patterns in data. Training these models can require vast amounts of computing power, often provided by specialised graphics processing units (GPUs) or tensor processing units (TPUs). Deploying these models, therefore, requires similar hardware infrastructure or specialised cloud services, which can be costly and challenging to manage.
In many cases, a single machine learning model may not be sufficient to address the complexities of a particular business problem. Instead, several machine learning models may need to work together, or multiple models may need to be integrated into a larger system for optimal gains. For example, a computer vision system for a self-driving car may require several models working together, such as object detection, lane detection, and pedestrian recognition.
Similarly, a predictive maintenance system for an industrial plant may require multiple models for different types of equipment, each trained on different types of sensor data. Integrating these models effectively can be a significant challenge, requiring careful coordination and management of resources.
However, the benefits of integrating multiple models can be significant, leading to better accuracy, improved efficiency, and greater flexibility in addressing complex business problems.
The different deployment options for machine learning models in production
If you have your machine learning model as part of your monolithic application, it can be easier for deployment and development, as there is less boilerplate code and infrastructure to manage. Monolithic applications are self-contained and do not require additional components or services to be deployed, which simplifies the deployment process. Additionally, having the machine learning model integrated into the monolithic application makes it easier for developers to work on the application as they do not need to switch between different environments or tools to develop and test the model.
This approach can also simplify the scaling of the application, as the entire application can be deployed and scaled as a single unit. However, it is important to note that this approach may not be suitable for all use cases and may require careful consideration of the trade-offs between simplicity and scalability.
If the machine learning model is implemented as a separate service, it provides several scalability and optimisation options that may not be possible with a monolithic application. For example, you can deploy the service on multiple servers to handle high traffic loads, and use load balancing techniques to distribute incoming requests across these servers.
Additionally, you can optimise the performance of the service by using specialised hardware such as GPUs or TPUs to accelerate the training and inference of the model. This approach allows for greater flexibility in scaling and optimising the machine learning model, and can provide significant benefits in terms of performance and accuracy.
However, it comes at the cost of increased complexity in deployment and maintenance, as multiple services need to be managed, and the additional code and infrastructure required to support the service.
The increased complexity in deployment and maintenance of a separate service for the machine learning model arises due to the need for additional infrastructure such as a message queue, service discovery, and load balancer. Additionally, you need to handle communication between the main application and the machine learning service, which requires implementing APIs and handling data serialisation and deserialisation.
Overall, this approach may result in more code to write and maintain, and may require additional expertise in managing and deploying microservices. However, if scalability and optimisation are critical requirements for your application, this approach can be a suitable option, allowing you to easily scale and optimise your machine learning model independently of the rest of your application.
Another option for hosting and deploying machine learning models is to use third-party solutions that provide a framework for building and hosting models. These solutions can simplify the deployment process by handling infrastructure management, load balancing, and scaling, reducing the amount of boilerplate code you need to write. Additionally, these platforms often provide additional tools such as data processing pipelines, data visualisation, and model versioning, which can make it easier to develop and maintain your machine learning models.
However, a potential downside of using third-party solutions is that part of the system will be a black box, and you may have limited control over the underlying infrastructure and technology stack. This may limit your ability to customise and optimise the system to meet your specific needs, and you may need to work around the constraints of the platform.
Another potential drawback of using third-party solutions is the cost. Many third-party platforms charge for usage, either on a per-model or per-API call basis, which can add up quickly for high-traffic applications. Additionally, some platforms may charge additional fees for advanced features such as auto-scaling, data storage, and team collaboration tools.
Thus, it is important to carefully consider the pricing model of any third-party platform before committing to using it for your machine learning model deployment. Overall, using third-party solutions can be a convenient option for deploying and hosting machine learning models, but it is important to weigh the trade-offs between ease of use, cost, and flexibility.
Choosing the right approach to deploy the machine learning models for your use case
When it comes to choosing the right approach for hosting and deploying machine learning models, it is important to be lean and iterate, and choose the approach that best fits the specific requirements of your project. As we have seen, there are several options available, including integrating the model into a monolithic application, building a separate service, or using a third-party platform. Each approach has its own advantages and disadvantages, and the best option will depend on the specific needs of your project, such as the scope, timeline, budget, and scalability requirements.
Thus, it is important to evaluate each option based on its suitability for the project, taking into account factors such as ease of development and deployment, scalability, performance, flexibility, and cost. It is also important to remain agile and be prepared to adapt and iterate as the project evolves, and to continuously evaluate and refine the approach based on feedback and changing requirements. Ultimately, the key to success is to choose an approach that strikes the right balance between efficiency, scalability, and maintainability, while meeting the specific needs of your project.