in Blog

June 22, 2023

Unlock the power of Generative AI. How to incorporate ModelOPs (or MLOps) for Generative AI into internal data infrastructure?


Edwin Lisowski

CSO & Co-Founder

Reading time:

9 minutes

Generative AI has gained traction in various sectors, especially in the creative and tech space. According to a Gartner report, generative AI will account for almost 10% of all data produced by organizations by 2025, up from 1% in 2021. [1] Unfortunately, despite its potential to revolutionize operations and streamline processes, generative AI comes with a few constraints.

For starters, large generative AI models like ChatGPT and DALL-E have large digital footprints and slow inference times. This means that they require significantly larger and more sophisticated infrastructures to run on an organizational level.

To overcome some of these challenges, developers have come up with ways to leverage ModelOps for generative AI adaptation. This article will evaluate the relationship between ModelOPs and generative AI and how you can incorporate ModelOPS for generative AI tools into your internal data infrastructure.

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What is generative AI?

Generative AI is a broad term used to describe artificial intelligence systems that use algorithms to create new data from their training data. Depending on your input and the generative AI model questions, you can generate different types of data, including text, audio, video, and 3D models.

These models utilize neural networks to identify patterns and structures in their training data in order to generate new data. Developers typically train generative AI models using two different approaches – supervised and unsupervised learning. The latter enables for seamless creation of generative models from corpus amounts of unlabeled data – something that’s much harder to do with labeled data sets.

What is ModelOps?

ModelOps, also known as Operations for Machine Learning Models, is a set of practices primarily focused on the governance and lifecycle management of a wide range of operationalized AI and decision-making models. These include machine learning, optimization, knowledge graphs, rules, linguistics, and agent-based models. [2]

What Is ModelOps and How Is It Different From MLOps?


It offers a structured approach to operationalizing all machine learning, predictive analytics, and machine learning models by allowing data scientists to monitor and govern AI models while ensuring their scalability.

Read more about What Is ModelOps? ModelOps vs. MLOps

What is ModelOps for generative AI?

ModelOps applies DevOps practices to machine learning models to streamline various processes, including deployment, monitoring, and updating current models by integrating them into existing data infrastructures.

Importance of ModelOps for generative AI

Incorporating ModelOps for generative AI into internal data infrastructures can offer numerous benefits including the following elements:

Importance of ModelOps for generative AI

Effective model lifecycle management

By leveraging ModelOps for generative AI, organizations can manage the entire life cycle of their models, including development, deployment, and maintenance. ModelOps achieves this by establishing standardized processes, tools, and best practices, thus ensuring efficient model management while reducing the risk of errors and inconsistencies.

Ultimately, this can help automate the entire model lifecycle, which can free up time for data scientists to focus on building and improving new models. It also facilitates faster deployment, improves model accuracy, and leads to more efficient use of organizational resources.

Streamlined development and deployment

By providing a systemic approach to model development and deployment, ModelOps enables development teams to collaborate effectively and ensure smooth model integration into production environments.

ModelOps also facilitates reproducibility and version control, leading to faster development cycles and efficient deployment processes.

Improved model performance and accuracy

By leveraging ModelOps for generative AI in internal data infrastructures, organizations can implement robust monitoring and performance tracking mechanisms which can help detect anomalies, identify model drift, and address performance issues promptly before they impact the organization. Ultimately, this leads to improved model accuracy and reliability.

Scalability and resource optimization

Generative AI models are huge and have equally large computational requirements, making it harder for organizations to implement them within their data infrastructures. By leveraging ModelOps, organizations can effectively optimize resource allocation and scale their existing infrastructures to meet the requirements of the generative AI model.

Organizations can also take this a step further by leveraging technologies like cloud platforms, distributed computing, and containerization. Ultimately, this can help organizations to efficiently manage resources and ensure scalability.

Agility and iterative development

The successful development and adaptation of generative AI models require an iterative approach. Approaches like implementing continuous monitoring, feedback loops, and continuous improvement processes can enable organizations to enhance their generative AI models such that they effectively adapt to changing business requirements and evolving business needs.

It might be also interesting for you: Best MLOps Platforms in 2023

How to incorporate ModelOPS for generative AI into your internal data infrastructure?

incorporating ModelOPS for generative AI

Identify the business problem

Before you can incorporate ModelOps for generative AI in an organization, you first need to identify the underlying problem you’re trying to solve. This will help you later on in determining the appropriate model. Business problems can range anywhere from automating repetitive tasks to generating synthetic data for training machine learning models.

Choose the appropriate model

There are various types of generative AI models, each suited to specific purposes and with different strengths and weaknesses when it comes to solving specific business problems. The most common types of generative AI models include GANs, VAEs, and autoregressive models.

When choosing a generative AI model, it is essential to evaluate the performance of different models in different scenarios in order to identify the model that best suits the business’s intended purpose or solves a specific business problem.

When choosing an appropriate model, you also have to consider the cost of training the model. In this regard, the most appropriate generative AI models for cost-effective large-scale operationalization are:

  • Hosted open-source models
  • Closed-sourced models that can be accessed via API

Hosted open-source models typically perform well for most use cases without requiring any major changes. However, these models still require to be fine-tuned with relevant domain data for use cases that require industry or function-specific context.

For companies that don’t have the expertise to own or run their own generative AI models, closed-sourced models specifically designed for specific purposes would suit them best. These models come equipped with APIs that enable even the least experienced IT teams to use them effectively.

Prepare training data

Before incorporating generative AI models, you first have to train them with relevant data so they can generate accurate results. For optimum results, the data must be clean, labeled, and representative of the targeted purpose. You can also use data augmentation techniques to increase diversity in the data and improve the model’s performance.

Train the model

Training a generative AI model requires vast computational resources and a large corpus of data, which can be quite restrictive for companies without large AI budgets. For instance, most of the recently released generative AI models like LLaMa by Meta and OpenAI’s GPT-3 have cost between $1 and $10 million to train. LLaMa’s training also took about 1,022,362 hours when being trained on 2048 NVidia A100-80 GB, which translates to about $4 an hour, reaching a total training cost of $4 million. [3]

Besides the overwhelming training costs, training generative AI models also require teams of experienced engineers and data scientists, thus making it harder for small enterprises to train and develop their own models.

However, with more efficient computing systems from cloud providers, you can bring the cost of training a generative AI model to as low as $10,000. This can significantly increase the use of generative AI for domain-specific use cases.


Generative AI models are mainly trained on unstructured data, which makes it difficult to represent results in a typical data flow. This data is typically represented by embeddings, which also serve as the input currencies of these models. The nature in which a generative model presents the information can significantly impact user experience and the model’s inferences, particularly in text-based models. Considering these facts, embeddings can be considered equally important as the models themselves.

With that said, building efficient embeddings is no walk in the park due to cost implications and skilled workforce requirements. However, generative AI APIs are on the rise and have contributed to the development of third-party embedding APIs, which provide access to efficient embeddings at a relatively lower cost compared to in-house developed embeddings.

For instance, OpenAI has an embedding model called Ada, which costs about $400 for every 1 million calls for 1000 tokens [4]. Considering a typical generative AI model would require tons of tokens to achieve embedding results, these costs can quickly add up at scale. Therefore, there is an immense need for cheaper open-source embedding models for long-term generative AI embedding that can be hosted to provide embeddings and management services.

Deploy the model

The final step of the incorporation process is model deployment for use in production environments. It is at this point that data scientists integrate the model into existing data infrastructure and ensure that it meets the necessary performance requirements.

After deployment, you also have to monitor the model’s performance. You can do this by tracking various metrics such as accuracy, precision, and recall. Any anomalies detected should be addressed promptly to ensure model accuracy.

Final thoughts

Incorporating ModelOps for generative AI into your organization’s internal data structure can provide numerous benefits, including automation, creative content generation, simulation and scenario planning, and many more. However, the incorporation process is resource-intensive and requires significant computational resources. But, leveraging cloud-based computational resources could significantly bring down production costs. You also have to follow ModelOps best practices for optimum results.

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[1] Gartner Identifies the Top Strategic Technology Trends for 2022. URL: Accessed June 15, 2023
[2] Information Technology. URL: Accessed June 15, 2023
[3] Large Language Model. URL: Accessed June 15, 2023
[4] Embeddings. URL: Accessed June 15, 2023