Leading software outsourcing companies are now doubling up as generative AI companies via their expertise in delivering intelligent digital applications to their clientele. Whether it’s capitalising on existing big data to seek insightful trends or building a conversational chatbot that can understand human sentiment, software outsourcing companies have been front and centre of building bespoke AI applications for automating workflows, making personalised recommendations, and instilling intuitive customer journeys.
Azure AI services offer a full-range of intelligent solutions that businesses can make the most of, through careful consideration and implementation via their software development teams. As customers consider an endless array of preferences and businesses grapple with shifts in demand, AI-backed automation, assistance and forecasting can make all the difference for both parties as each strives to meet its own objectives.
In this article, we therefore introduce Azure AI services, what it offers, and how businesses can implement it to serve their own needs. Read on to know more!
Keen to implement Azure AI services, or elevate any existing AI services to the next level? Being an exclusive Azure partner, EFutures delivers unmatched expertise and resources to accommodate both brand new projects, as well as offer leverage to existing AI projects you may need reinforcement on.
Contact us today to receive a customised assessment for your business needs, and learn how we can help deliver a range of Azure’s AI capabilities to provide your business the lift it deserves.
Azure AI services, offered by cloud computing leader Microsoft Azure, are a range of AI-based services and models that can power web, mobile and any other form of digital application with intelligent capabilities. With Azure AI, cloud managed services further offer in-built hosted infrastructure, to build, test, run, store and maintain AI models.
Getting started is also easy, as an account can be opened on Azure AI for free. However, if your organisation or software outsourcing company is an Azure partner, access becomes easier and more scalable.
Some of Azure AI’s key uses cases include (but aren’t limited to):
The below Azure AI services, while each being popular on their own, are often integrated with one another to deliver a complete and fitting solution for unique business needs. For example, Azure AI Video Indexer is based on other services such as Azure’s very own Face, Translator, Azure AI Vision, and Speech models.
Additionally, software outsourcing companies that are exclusive partners with Azure can avail most (if not all) of these services within a quick turnaround time, thereby providing them with a high level of autonomy to in turn deliver customised AI-powered solutions for their clientele.
Determining the correct suite of Azure AI services always depends on the unique requirements and objectives that your business has. Having a discussion with your software development team should be the first step, as they are at the frontlines of servicing your project day in and day out – and will therefore have insightful perspectives on what’s lacking, what can be improved, and what to implement in line with budgetary and time-related constraints.
As a result, an assessment is always the foundational step to start with, for any company irrespective of size or industry. If your software development team already has an Azure AI engineer or two that can contribute to the discussion, this can be insightful, no doubt. However, for projects that have never initiated Azure AI services before, the same assessment can be conducted with existing team members to shed light on:
Once an assessment is complete, this can then serve as a primary point of reference for both business and software development teams.
Being minimalistic and starting with an MVP (Minimum Viable Product) is especially important for software development projects that are implementing Azure AI services for the first time, and any kind of AI/ML service in general. This means that only the most essential of functionalities shall be released during the first iteration.
An MVP can therefore be advantageous in two key ways. For one, development teams have less pressure owing to only having to focus on a smaller list of deliverables. Conversely, new functionalities that do get added following the MVP are bound to be those that are requested by end users of the product. This ensures that development teams are only focusing their time and effort to build capabilities that end users shall actually find valuable – as opposed to releasing features based on assumptions.
With every release/update, analysing progress, performance, speed, accuracy or any other objective within your AI deliverable(s) is imperative, in order to quantify the effectiveness and overall success of your application, as well as your team’s efforts. Establishing specific KPIs for your deliverables is always recommended, as monitoring processes can then be narrowed down to exact and specific statistics, to aid future decision-making for your application’s intelligent capabilities.
Azure AI services offer a one-stop solution for all things AI, thereby enabling businesses to build, test, run, maintain and scale any number of intelligent applications. From speech and facial recognition to forecasting and pattern matching, use cases are practically limitless, as AI models can be trained and customised as needed, for even the most niche of business requirements.
To understand which services are required for the purpose of building AI-powered applications, businesses need to conduct a thorough assessment of existing problems, bottlenecks and customer feedback, before implementing any solutions within the software development lifecycle. The same is also applicable for when companies need to hire AI developer expertise, therefore making assessments more indispensable than ever.
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