Generative AI with Microsoft: Explore Azure services

Understanding generative AI 

Generative AI, employing advanced neural networks like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), specializes in creating data and content akin to human output. This branch of AI is distinct from other areas focused on data analysis and classification, emphasizing its role in content generation rather than data interpretation. It's important to distinguish generative AI from chatbots; while chatbots might use generative AI, they can also be developed with other technologies, and generative AI extends to a wider array of applications beyond just chatbots.
Generative AI offers various use cases for organisations, including content generation (text and images), creative design (art, music, graphic design), and data augmentation (improving datasets, synthetic data generation). Effective integration of generative AI requires considering several key factors such as digital and data maturity, priority, IT Environment and cloud adoption.
Want to find out more? For more insights, read our article "Generative AI in business". 

Leveraging cloud infrastructure for Generative AI

Generative AI can significantly benefit from cloud infrastructure by leveraging the scalability, computational resources, and flexibility provided by cloud platforms. Cloud infrastructure allows generative AI models, which often require substantial computational power, to be trained on large datasets and deploy seamlessly, ensuring they can handle varying workloads efficiently. Additionally, the cloud facilitates cost-effective solutions, offering pay-as-you-go pricing models and eliminating the need for significant upfront investments in hardware. Cloud services also provide collaborative tools, extensive data storage, and integration with other AI services, streamlining the development, deployment, and management of generative AI applications, making it accessible and feasible for a broader range of organizations and use cases.

Microsoft Azure Services for Generative AI

Azure AI services, part of Microsoft's Azure cloud platform, provide a comprehensive suite of artificial intelligence tools and capabilities to empower organizations with AI-driven solutions. These services encompass a wide range of AI technologies, including machine learning, computer vision, natural language processing, and speech recognition.

Microsoft Azure and OpenAI collaboration

In September 2021, Microsoft Azure and OpenAI have collaborated to make OpenAI's powerful artificial intelligence models accessible to developers and businesses through the Azure cloud platform. Azure provides a range of services and tools for building, training, and deploying AI models and applications, and OpenAI's technologies are a valuable addition to this ecosystem.

Key Azure services for Generative AI

1. Azure OpenAI

Through the Azure cloud platform, developers can access OpenAI's cutting-edge AI models, such as the GPT series, to develop innovative applications for various use cases. This collaboration allows businesses to leverage OpenAI's state-of-the-art natural language processing capabilities, enabling them to create intelligent chatbots, language translation services, content generation tools, and more.

2. Azure Machine Learning

Azure Machine Learning’s usage for generative AI involves creating, training, and deploying generative models. Azure Machine Learning provides the infrastructure and tools to scale the generative AI projects. Whether it is for training large language models or deploying them to handle high workloads, Azure can accommodate the scaling needs, ensuring the applications are responsive and efficient.

3. Azure Cognitive Services

Azure Cognitive Services is a set of cloud-based AI services and APIs provided by Microsoft Azure that covers a wide range of AI capabilities, including computer vision, speech recognition, natural language understanding, and more. Especially, Azure Cognitive Services can be used to pre-process and analyze data before feeding it into generative AI models. 

4. Azure Databricks

Azure Databricks is a cloud-based, Apache Spark-based analytics platform offered by Microsoft Azure. One of the advantages of Databricks is that Databricks provides a unified platform for data engineering, data science, and machine learning. This means the client can manage the entire generative AI pipeline, from data preparation to model deployment, in a single environment.

Generative AI Microsoft Azure: Use cases      

Based on Codit’s decades of experience in accompanying organization’s journey of digitalization, the following adoption approaches of generative AI are considered as practical for companies that are interested in benefiting from the technology.
•    Leveraging generative AI as internal efficiency booster: Organizations can adopt the Generative AI Technology and integrate it with its own IT environment, in order to automate the highly-repetitive procedures, for example: retrieval of non-sensitive information cross department, automated creation of meeting minutes, and automated creation of summarization of multiple documents. 
•    Combine Generative AI and other Azure services for hyper automation: using a generative AI – based chatbot and integrate with other Azure services to automate the workloads to a next level.
•    Test the Generative AI tool before rolling it out: test the user-friendliness, security policies and the outcome of the tool, before the potential risks are expanded to the whole organization. 
•    Train the users before launching the Generative tool to an organization level: a code of conduct with essential training of the utilization is highly recommended to ensure the fair usage of the technology, and ensure the expected business outcome of the AI. 

It is witnessed that a practical adoption of digitalization would allow IT teams to be well prepared for the taking-over and maintenance of the technology, allowing them to be better aligned with legal and security stakeholders, and in the end, making sure the adoption of the technology by the end users. Moreover, on the organization level, the practical approach brings in a modernized workplace, promotes higher work quality, and the business units are well-supported by the technology. 

     
Ethical and strategic generative AI adoption with Proximus and Azure

Generative AI represents a significant stride in technology, showcasing a future where machine-generated content can match human creativity. Its uses span content creation, artistic design, and data enhancement, but it also raises important ethical considerations. The partnership between Microsoft Azure and OpenAI provides essential infrastructure and tools to leverage Generative AI effectively. As Proximus explores this innovative field, responsible use, transparency, and adherence to ethical standards are crucial. Proximus invites organizations to engage with them for further insights and support in navigating this digital transformation journey.