And what it means for business leaders

By Juan Soriano

The rapid evolution of technology has consistently brought forth new possibilities and opportunities for businesses to grow and thrive and being able to capitalise on these developments is often what marks successful companies from uber-successful ones.

One groundbreaking technology that has captured the world’s attention is Machine Learning (ML) and Artificial Intelligence (AI), the most notable example of which is Open AI’s GPT models and its most publicly recognised use case ChatGPT.

ChatGPT demonstrates the power of AI-driven solutions in an easy-to-use and accessible package meaning that businesses no longer need extensive knowledge or specific domain expertise to tap into the possibilities that large language models afford. This perceived democratisation of AI paves the way for developers who previously lacked the necessary knowledge to create AI-driven solutions and help create business value previously out of their reach.

The days of needing a PhD or years of Machine Learning experience to even access and benefit from such technologies are long gone. Today, companies and organisations, irrespective of their size or industry, can implement these technologies and enjoy the benefits. Ultimately the complexity has been reduced to a point where making use of them is not very dissimilar to consuming REST APIs. This is a game-changer, with easy access to training resources and development tools, companies can now create sophisticated AI solutions to improve their value propositions or use GPT models internally to optimise and automate their business processes.

The newfound accessibility of AI and its applications also means that any company can quickly adapt to changing market needs, lead the competition, and find new ways to provide superior service to their customers. In essence, the widespread availability of AI technology levels the playing field, allowing organisations to innovate and compete effectively in their chosen sector.

The Democratisation of AI: Impact on MLOps and DevOps

The field of Machine Learning Operations (MLOps) has been garnering significant attention in recent years. Previously, incorporating ML into workflows demanded specialised knowledge. Now, with the emergence of new services offered by big tech companies, two major consequences have arisen. On one hand, MLOps has become a discipline more accessible to a wider range of software professionals, as these services simplify the process of integrating ML models into applications and workflows. On the other hand, the popularisation of these services is providing businesses and individuals with a better perspective on how to use AI in their workflows, enabling them to make more informed decisions. This phenomenon is what we refer to as “The Democratisation of AI”.

The MLOps landscape is evolving to embrace a more DevOps-centric approach, leading to an integration of the two methodologies. As a result, organisations now face the challenge of determining how to effectively combine MLOps and DevOps, instead of selecting one over the other. The dilemma stems from the previous perception that MLOps and DevOps were distinct disciplines, each requiring its tools, workflows, and expertise.

Thankfully, incorporating both methodologies into business operations does not require a PhD or extensive knowledge of ML or AI. Engineers can now assume MLOps roles, as third-party APIs often simplify the task of working with familiar REST APIs.

MLOps manages the lifecycle of a machine learning model from development to deployment in a production environment, while DevOps streamlines the process of building, testing, and deploying software. As the boundaries between these methodologies blur, the collaboration between data scientists and software engineers becomes vital. This partnership ensures that data scientists’ models are efficiently and swiftly integrated into the software engineers’ pipelines, ultimately benefiting the organisation as a whole.

In conclusion, the democratisation of AI has made the synergy between MLOps and DevOps more crucial than ever before. Companies no longer need specialised technical knowledge to incorporate ML into their workflows, and engineers can now fulfil the roles of MLOps without that specific knowledge. The convergence of MLOps and DevOps will lead to improved efficiency and collaboration between data scientists and software engineers, ultimately enhancing the overall success of business operations.

AI Models and Tools for Businesses: Streamlining Operations and Enhancing Decision-making Processes

OpenAI and other companies offer a plethora of AI models and tools that businesses can leverage to enhance their operations. These tools include DALL-E, GPT, and Whisper, each with its unique capabilities.

GPTs and other large language models (LLMs) provide conversational AI capabilities that can improve customer service, automate repetitive tasks, and assist in decision-making processes. DALL-E and Stable Diffusion enable the creation of unique artwork and images, opening up new possibilities for marketing and design. Meanwhile, Whisper is a powerful speech-to-text model that can transcribe audio and video content, providing businesses with valuable insights from a variety of data sources.

For developers, Replicate, RunPod and are essential tools that simplify server deployment and infrastructure management. They also facilitate the deployment of AI models selected from HuggingFace. As the largest ML model repository on the internet, HuggingFace offers businesses access to a wide range of pre-trained models without the need for extensive in-house development. By using HuggingFace to choose models and RunPod and for deployment, businesses can streamline their operations and take full advantage of AI capabilities.

Fine-tuning and custom embedding with these technologies allow for more accurate insights and more bespoke support from your AI applications. Incorporating AI models and tools into everyday operations can ultimately help organisations to streamline processes, enhance decision-making, and ultimately stay competitive in the market.

Open AI logo
Replicate AI logo
Runpod logo logo
Hugging Face Logo

Revolutionising customer experience with GPT, Hugging Face, and Replicate: A hypothetical use case for an online media company

The widespread accessibility of Artificial Intelligence has given businesses an opportunity to apply powerful AI tools to their specific organisational context and offering. Tools that can revolutionise customer experiences and significantly improve team productivity. Let’s explore a hypothetical use case for an online media company that is considering using  AI for content curation, audience targeting, and content creation.

The Objective

The organisation’s primary goal is to:

  1. Streamline the content curation process, 
  2. Deliver tailored content to the appropriate audience at the right time,
  3. Enhance content creation through AI assistance.

To achieve this, they might incorporate various AI tools, such as GPT models, Hugging Face, and Replicate. Making use of those tools is very similar to the way we typically consume REST APIs; understanding how to use the tool is sufficient, not needing to be experts in all the underlying theories behind them. Consequently, the team that initially handled DevOps functions can transition to MLOps without requiring specific deep knowledge of Machine Learning.

GPT models: Automating Customer Support and Content Creation

GPT models are integrated into the media company’s website and social media pages to automate responses to frequently asked questions (FAQs) from users. This reduces the workload of employees, allowing them to focus on other essential tasks. By providing real-time solutions to customer queries, GPT models enhance productivity and facilitate data-driven decision-making.

Additionally, GPT models can be used for content creation, especially when fine-tuned with embeddings. By training the model on a dataset relevant to the media company’s niche, GPT can generate article summaries, draft engaging headlines, or even create short news snippets that cater to the audience’s interests.

GPT models: Automating Customer Support and Content Creation

Hugging Face: Access to ML Models

The media company leverages Hugging Face to access a wide range of ML models without the need for extensive model development. Hugging Face offers options to deploy models using services like Replicate, which simplifies server deployment and infrastructure. As a result, the organisation can analyse data from various sources, such as social media, website traffic, and customer feedback, to create content that resonates with its audience.

Simplifying Server Deployment

Replicate, Runpod or could then be used to streamline server deployment and infrastructure management. With Runpod or, the team can focus on building applications using models from Hugging Face without struggling with enabling GPUs for their servers, simplifying the deployment and management, and enabling the business to concentrate on delivering high-quality content to the right audience at the right time.

Simplifying Server Deployment

LLMs for Development Teams and GPT Integration

Another practical use case is a development team that incorporates large language models (LLMs), or GPT, to automate tedious tasks, such as writing changelogs, reasoning about code changes, extracting more information from acceptance criteria based on previous tickets in the reporting system, or even as coding assistants or “copilots” while they write software. With GPT integration, the team can also generate human-like descriptions of code changes, making it easier to understand the impact of updates on the overall system. Allowing the team to focus on more complex and creative work.

LLMs for Development Teams and GPT Integration


The democratisation of Artificial Intelligence (AI) has profoundly impacted businesses and developers worldwide and will continue to do so. This transformation has opened up new opportunities and lowered barriers to entry, enabling developers from various backgrounds and skill sets to leverage powerful tools and services. As a result, they can create innovative solutions that were once beyond their reach.

This widespread accessibility has led to a more diverse range of AI-driven applications. In turn, businesses can streamline operations, enhance decision-making processes, and gain a competitive edge in the market. As AI continues to evolve and expand, we can anticipate further breakthroughs and advancements in the coming years.

The ongoing development of AI will foster even greater innovation, empowering businesses and developers to fully harness this game-changing technology. This has the potential to contribute to a more connected and intelligent world, where systems and processes work in harmony to achieve optimal results. There’s no doubt, the democratisation of AI has set the stage for something incredibly exciting, and we have yet to witness its full potential.

Interested in GPT, LLMs and what AI can do for your business? Join the conversation on Linkedin and tell us about what you’re up to. Or, if you want to chat directly drop me an email at

Disclaimer: We used a custom tool to support the research that made this article possible. More info on that coming soon!