Enhancing Telco with Computer Vision
#CVaaS, AutoML or DIY – What is the Best Approach for Capturing the Benefits of Computer Vision?
PUBLISHED ON 08/11/2023
AI is fast becoming a popular solution for telecom companies to address many operational challenges, opening up opportunities to optimise business processes, increase Right First Time operations, reduce cost, and improve the overall customer experience. With many enterprises still in the early stages of exploration and deployment, AI integration can be a complex and time-consuming process.
In our latest article, we explore the best approaches for enterprises to capitalise on AI opportunities, especially those new to AI. We'll review how Computer Vision as a Service (CVaaS), AutoML, and DIY approaches compare and discuss their key pros and cons when choosing the optimal solution for infrastructure quality control and maintenance.
The introduction of AI technologies in telco
Telcos are increasingly integrating artificial intelligence (AI) solutions into their workflows and system stacks to achieve significant business benefits. Machine learning algorithms analyse vast amounts of data and can make predictions across various use cases, from network planning and building to customer installation and support. In doing so, operators can help accelerate network deployment, optimise network performance, improve resource allocation, and enhance the overall customer experience.
At one end, chatbots and virtual assistants powered by AI enhance customer support, offering instant responses and personalised interactions. On the other, AI-driven predictive analytics can anticipate and prevent network issues, ultimately leading to more reliable telecom services. For example, AT&T has been using AI for predictive maintenance of its network infrastructure for a while now. By analysing data from various sources, including sensors and historical maintenance records, AI algorithms can predict equipment failures before they happen.
Computer vision is a field of AI and computer science that interprets visual data. It involves developing algorithms and systems that allow machines to process and analyse visual information from images or videos. In layman’s terms, an example use case that we have deployed is when a field agent uploads pictures of equipment they are working on, which gets forwarded to a trained machine learning model for analysis to identify any potential quality issues with the equipment or associated operations they are undertaking.
Defining the pros and cons of CVaaS, AutoML and DIY solutions
There are several implementation approaches, and choosing the best solution for an individual business requires careful consideration. Some companies that are more advanced and already have a good grasp of AI often build home-grown solutions using their data scientist and MLOps teams, whilst others that are less mature consider using AutoML software tools or engaging a third-party provider such as Inveniam, which offers end-to-end managed solutions (or CVaaS) across the computer vision lifecycle. While each method has distinct advantages, weighing the pros and cons is important.
Fully managed computer vision solutions (or CVaaS)
Computer Vision as a Service (CVaaS) is a cloud-based service offering access to powerful computer vision capabilities without requiring extensive in-house infrastructure or expertise. It leverages AI techniques, including both classic machine learning and deep learning algorithms, to analyse and interpret visual content from images and videos. While some services cover most of the computer vision lifecycle, Inveniam AI facilitates the entire process, from use case exploration to model deployment and continuous learning.
Inveniam’s end-to-end managed solution helps customers starting out on their AI journey to realise the benefits of computer vision solutions without the burden of investing in their own software, hardware and dedicated AI resources. Inveniam also complements existing AI teams that may be operating at maximum capacity or can collaborate on specific use cases that haven’t reached their full potential with their internal teams.
- Time to value – dedicated outsourced teams can quickly ramp up, accelerating the time to realise tangible outcomes vs. using in-house teams.
- Attractive commercials – often priced on a usage-based subscription model with no setup fees, this approach can be significantly more cost-effective than investing in your dedicated teams and compute infrastructure.
- Latest ML or AI technologies – this model helps ensure you always keep abreast of the latest developments and access the latest cutting-edge architectures.
- Domain expertise – access to highly experienced teams, resulting in quality annotation, analysis and predictions, often more accurate than you can deliver in-house.
- Resource consumption – the least effort and expense for the enterprise, remaining the responsibility of the solution provider.
- Guaranteed performance levels – some providers, such as Inveniam, offer performance-related SLAs for complete peace of mind that an acceptable performance level will be achieved.
- Customisation – suited to highly bespoke use cases and detections that cannot be found in AutoML models or off-the-shelf pre-trained models.
- Outsourced approach – less suited for enterprises wanting to use their data scientist teams for model training and development.
- Limited control & ownership – whilst the input and output data reside with the enterprise, the model IP often remains with the solution provider.
- Reliance – for use cases that require effort to integrate into operational workflows, it can sometimes be more difficult to bring back in–house.
- Integration complexity – integrating CVaaS solutions with existing infrastructure systems and data sources can be complex (and expensive) if a solution provider does not offer a standard.
The real power of CvaaS lies in its ability to enable companies to utilise cutting-edge detection capabilities without investing resources in creating and maintaining AI models in-house. CVaaS allows companies to focus on their core competencies while relying on AI experts to ensure the desired goal is reached on a technical level.
Automated machine learning solutions (or AutoML)
Automated machine learning (AutoML) offers a different approach to AI implementation. It aims to empower businesses to leverage AI without advanced data science knowledge using low-code and no-code development approaches. This process automatically selects the most appropriate algorithms, hyperparameters, and features to create and deploy the optimal machine learning model. This automation drastically reduces the time and complexity traditionally associated with manual model development.
- Efficiency – AutoML enables users to quickly experiment with algorithms and models to find the best-fit solution, reducing the time required to implement new models, particularly advantageous for industries where quick model deployment provides a competitive edge.
- Accessibility – AutoML AI enables professionals with varying technical expertise to create effective models without in-depth ML knowledge. Businesses can overcome the traditional bottleneck of ML expertise by automating the model training process.
- Scalability – AutoML solutions can scale to handle large datasets across multiple models, making them suitable for tasks that involve extensive data analysis.
- Cost – compared to CVaaS, AutoML can sometimes be a more cost-effective choice if a company has already invested in scaling up a data team.
- Perceived simplicity – data specialists are still required to use these tools, so recruitment or training of existing teams will be required. Similarly, enterprises will also have to outsource or create special teams to annotate images.
- Limited customisation – while AutoML is accessible, it may not provide the same level of customisation and performance as hand-crafted machine learning models of CVaaS or manual approaches, often necessary for addressing complex infrastructure maintenance tasks.
- Compute costs – AutoML solutions often consume significant computational power resources if many models are tested and finetuned in parallel.
- Explainability – A lack of understanding of how an algorithm produces a specific result may contribute to unexpected outcomes and dataset-related biases, leading to the wrong conclusions being made.
AutoML's most significant advantage is its user-friendliness, allowing individuals to navigate machine learning without deep expertise. Whilst AutoML solutions automate and accelerate model development tasks, time to value and performance are not always guaranteed. Due to sector-specific needs and limited customisation options for them, companies that choose AutoML have dedicated data engineers and scientists to manage and fine-tune models to fit their business needs.
The DIY approach involves companies taking on the challenge of manually developing and managing their own AI models in-house. This method requires assembling a team of skilled data scientists, data engineers, and domain experts with the expertise to create, train, and maintain the models leveraging a range of internally developed, open-source and 3rd party solutions.
- Tailored solutions – in-house teams have an in-depth knowledge of current systems, processes and challenges, making it easier to design and implement new AI solutions.
- Full control – companies have complete control over the design and development process, as well as full ownership of the model IP, useful when being used for competitive advantage or when future model evolution is required.
- In-house teams – by investing in training and skill development, companies can cultivate in-house AI expertise, which can be valuable for optimisation and pursuing further use cases.
- Performance – pre-trained AutoML and CVaaS models tend to perform better than DIY solutions due to having been trained on large datasets, which leads to higher success rates and better scaling opportunities.
- Recruitment – building an in-house computer vision solution means companies will have to scale up a full AI team, including data analysts and engineers, data scientists, and MLOps engineers.
- Compute Infrastructure – companies will be solely responsible for the investment in software, infrastructure and data servers
- Long process – developing custom AI solutions is extremely time-consuming, particularly when dealing with massive amounts of data. A dedicated team will have to create and manage the solution, testing different models rigorously.
- Cost – an in-house AI solution will require companies to invest substantially into this endeavour in terms of resources and effort to stay abreast of the latest technologies.
The DIY approach might be the best for companies that see AI as being highly strategic, have invested substantially in building their current AI teams, policies and processes, and want to control data handling and model development using a combination of internal and best-of-breed technologies to address their unique use cases, ensuring compliance and tailoring of AI solutions to their exact needs.
While developing an in-house solution can offer complete control and customisation, companies must allocate significant resources to assemble and maintain a skilled data team, and even that’s just a first step in the entire process. In comparison, a fully managed solution will have everything covered and, depending on the provider – offer custom services.
CVaaS, AutoML, and DIY key factor comparison table
To help review the key pros and cons of each solution, please refer to this infographic.
Embracing AI technologies will become increasingly important for telecom companies, as the use of AI is no longer limited to the most technologically advanced companies or select few use cases. Fully outsourced solutions like Inveniam AI and easy-to-deploy AutoML options will facilitate the rapid adoption of computer vision solutions. However, each approach comes with its deployment considerations, and the choice between them depends on the specific needs of each enterprise.
Companies new to exploring the benefits of AI will turn most to fully managed solutions, as they will be the easiest and most cost-effective to deploy. At the other end of the maturity curve, companies increasingly use hybrid approaches to optimise the ML model performance for the challenge at hand.