The launch of ChatGPT, a few months ago, has catapulted Generative AI to the spotlight, much like the fevered excitement surrounding the production of a new Broadway Show soon to debut in your town. Echoing the calls of “lights, camera, action!”, organizations are scrambling to embrace the new mantra of “AI First” approach.
Data scientists, like experienced directors, acknowledge the hype with a knowing smirk. They appreciate the applause while they diligently point out that the technology’s early stage and the unseen limitations yet to overcome around this new development.
Yet, it's the backstage crew - the often-overlooked tech and data architects - who continue to remain under the shadows. In my opinion, they deserve the spotlight just as much as the rest of the front-line crew. Without the foundation of strong data strategy and durable tech architecture, Generative AI’s show can't progress from a hyped-up dress rehearsal (POC) to a full-scale production.
Welcome to 'Data Strategy and Technology Architecture – The Unsung Heroes of Generative AI POC to Production.’’ Much like how a successful Broadway production needs an entire cast and crew, Generative AI also requires a synergistic team of business leaders, data scientists, data strategists, and tech architects to transition from POC to large-scale production.
POCs are most often conducted on a small scale to serve as an exploratory medium to evaluate the potential of a specific model/s in addressing a particular use case. However, do not fall into the fallacy that the passage from POC to production is a mere rerun of the same process.
The defining difference between POC and production lies in scale. Training and deploying Generative AI – a subset of AI that learns from existing data sets to create new content such as text, music, images, videos -requires vast volumes of pertinent data and significant compute power, factors often under-considered during a POC.
Beyond an idea (business use case) and appropriate Generative AI modeling techniques, the voyage from POC to production requires robust data strategy and a solid tech architecture.
Indeed, my friends, data continues to be that pesky thing that continues to demand center stage. Without a well-designed data strategy, your game changing idea might get lost in the wilderness of deployment. Before you think about training the chosen Generative AI model, you must identify and procure the right kind of data needed for various stages, including training, reinforcement, and production. This data can be sourced from either your ecosystem or from external sources. For example, if your use case is creating new content, you will need plenty of textual data; if your use case is to generate images, vast amounts of image data is needed for training, testing, and deploying the model into production.
After identifying the right data, it should be further augmented with data preparation, an often-underappreciated step in the process. This process could involve building pipelines, centralizing, cleaning, structuring, and encoding. This is the underpinning to ensure that your model gets the best-quality input for learning. Without relevant data, your model will be unable to output expected results with the required accuracy.
Your data strategy, along with considering business use case imperatives, must consider regulatory, privacy, and ethical compliance. It is judicious to establish data governance policies well in advance of initiating the training your chosen model, thus ensuring a path towards successful production.
Deploying a model from POC to production exponentially increases the data scale and model performance complexity. This warrants the need for a robust, scalable, and flexible technology architecture and infrastructure. This encompasses hardware – servers, storage, network facilities; software that facilitates efficient and effective data processing with speed and accuracy.
Imagine attempting to stage a grand Broadway show in a run down, ill-equipped theatre - it simply would not do justice to the show. Similarly, without a strong tech foundation, Generative AI deployment risks efficiency and reliability roadblocks. Insufficient computation power or a sluggish training process impedes model scaling, while system crashes or model runtime failures can engender mistrust and friction among stakeholders.
Keen focus should be on security and reliability as well. In this era of mounting cyber threats and strict data regulations, the sturdiness of the tech architecture will dictate the success or failure of your Generative AI model’s functioning.
Poorly planned tech architecture will have a profound financial impact on your organization. Direct costs include higher operational and maintenance costs which will impact business value. Indirect costs could include sub optimal or wrong business decision making, dissatisfied customers, and loss of business opportunities. More importantly a weak infrastructure will expose your organization to security and data breaches resulting in hefty fines, legal battles, or irreparable reputational damage.
Therefore, it is imperative that enough attention must be put into establishing tech policies to ensure security, reliability, and seamless operations as these elements are crucial for successful model deployment.
The bond between data strategy and tech infrastructure in the journey from POC to production is not only essential but also symbiotic. Your data strategy informs the requirements for your tech infrastructure, ensuring it is equipped to handle the data needs and computational demands of your generative AI model.
Conversely, your tech infrastructure enables the effective execution of your data strategy. It allows for the safe, efficient, and ethical management, storage, and processing of the data that your AI model relies on.
As we draw the curtain on our exploration of Generative AI's journey from POC to production, it's high time we applaud the unsung heroes of the AI Broadway Show: the data strategists and tech architects. Their roles in managing the transition from a small-scale POC to full-blown production, often underestimated, are just as crucial, if not more, than the more visible actors and directors of our AI drama.
Data strategists and tech architects work meticulously behind the scenes, translating raw ideas into efficient processes that enable the models to operate seamlessly at scale, becoming the stepping stone for the entire Generative AI production. Their expertise is critical to feeding meaningful data to algorithms and ensuring smooth model deployment. Let's celebrate their invaluable contribution, positioning them at the heart of the process from POC to production.
What to learn more about Generative AI and the data science solutions Blend offers? Let's get in touch.
The launch of ChatGPT, a few months ago, has catapulted Generative AI to the spotlight, much like the fevered excitement surrounding the production of a new Broadway Show soon to debut in your town. Echoing the calls of “lights, camera, action!”, organizations are scrambling to embrace the new mantra of “AI First” approach.
Data scientists, like experienced directors, acknowledge the hype with a knowing smirk. They appreciate the applause while they diligently point out that the technology’s early stage and the unseen limitations yet to overcome around this new development.
Yet, it's the backstage crew - the often-overlooked tech and data architects - who continue to remain under the shadows. In my opinion, they deserve the spotlight just as much as the rest of the front-line crew. Without the foundation of strong data strategy and durable tech architecture, Generative AI’s show can't progress from a hyped-up dress rehearsal (POC) to a full-scale production.
Welcome to 'Data Strategy and Technology Architecture – The Unsung Heroes of Generative AI POC to Production.’’ Much like how a successful Broadway production needs an entire cast and crew, Generative AI also requires a synergistic team of business leaders, data scientists, data strategists, and tech architects to transition from POC to large-scale production.
POCs are most often conducted on a small scale to serve as an exploratory medium to evaluate the potential of a specific model/s in addressing a particular use case. However, do not fall into the fallacy that the passage from POC to production is a mere rerun of the same process.
The defining difference between POC and production lies in scale. Training and deploying Generative AI – a subset of AI that learns from existing data sets to create new content such as text, music, images, videos -requires vast volumes of pertinent data and significant compute power, factors often under-considered during a POC.
Beyond an idea (business use case) and appropriate Generative AI modeling techniques, the voyage from POC to production requires robust data strategy and a solid tech architecture.
Indeed, my friends, data continues to be that pesky thing that continues to demand center stage. Without a well-designed data strategy, your game changing idea might get lost in the wilderness of deployment. Before you think about training the chosen Generative AI model, you must identify and procure the right kind of data needed for various stages, including training, reinforcement, and production. This data can be sourced from either your ecosystem or from external sources. For example, if your use case is creating new content, you will need plenty of textual data; if your use case is to generate images, vast amounts of image data is needed for training, testing, and deploying the model into production.
After identifying the right data, it should be further augmented with data preparation, an often-underappreciated step in the process. This process could involve building pipelines, centralizing, cleaning, structuring, and encoding. This is the underpinning to ensure that your model gets the best-quality input for learning. Without relevant data, your model will be unable to output expected results with the required accuracy.
Your data strategy, along with considering business use case imperatives, must consider regulatory, privacy, and ethical compliance. It is judicious to establish data governance policies well in advance of initiating the training your chosen model, thus ensuring a path towards successful production.
Deploying a model from POC to production exponentially increases the data scale and model performance complexity. This warrants the need for a robust, scalable, and flexible technology architecture and infrastructure. This encompasses hardware – servers, storage, network facilities; software that facilitates efficient and effective data processing with speed and accuracy.
Imagine attempting to stage a grand Broadway show in a run down, ill-equipped theatre - it simply would not do justice to the show. Similarly, without a strong tech foundation, Generative AI deployment risks efficiency and reliability roadblocks. Insufficient computation power or a sluggish training process impedes model scaling, while system crashes or model runtime failures can engender mistrust and friction among stakeholders.
Keen focus should be on security and reliability as well. In this era of mounting cyber threats and strict data regulations, the sturdiness of the tech architecture will dictate the success or failure of your Generative AI model’s functioning.
Poorly planned tech architecture will have a profound financial impact on your organization. Direct costs include higher operational and maintenance costs which will impact business value. Indirect costs could include sub optimal or wrong business decision making, dissatisfied customers, and loss of business opportunities. More importantly a weak infrastructure will expose your organization to security and data breaches resulting in hefty fines, legal battles, or irreparable reputational damage.
Therefore, it is imperative that enough attention must be put into establishing tech policies to ensure security, reliability, and seamless operations as these elements are crucial for successful model deployment.
The bond between data strategy and tech infrastructure in the journey from POC to production is not only essential but also symbiotic. Your data strategy informs the requirements for your tech infrastructure, ensuring it is equipped to handle the data needs and computational demands of your generative AI model.
Conversely, your tech infrastructure enables the effective execution of your data strategy. It allows for the safe, efficient, and ethical management, storage, and processing of the data that your AI model relies on.
As we draw the curtain on our exploration of Generative AI's journey from POC to production, it's high time we applaud the unsung heroes of the AI Broadway Show: the data strategists and tech architects. Their roles in managing the transition from a small-scale POC to full-blown production, often underestimated, are just as crucial, if not more, than the more visible actors and directors of our AI drama.
Data strategists and tech architects work meticulously behind the scenes, translating raw ideas into efficient processes that enable the models to operate seamlessly at scale, becoming the stepping stone for the entire Generative AI production. Their expertise is critical to feeding meaningful data to algorithms and ensuring smooth model deployment. Let's celebrate their invaluable contribution, positioning them at the heart of the process from POC to production.
What to learn more about Generative AI and the data science solutions Blend offers? Let's get in touch.
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