The Challenges (and Solutions) of Building Generative AI Models

Ted Naseri

Data Scientist, Datastreamer

March 2023 | 10 min. read

What Is Generative AI?

Unlike typical machine learning models, which only analyze or study existing data, Generative AI can create new content that closely resembles the original inputs or prompts. The applications of Generative AI are diverse and include live chat, text, images, audio, videos, music composition, and more. Generative AI models promise exciting outcomes, but to reach this end, there are unique challenges that need to be addressed.

The Unique Complexities of Generative AI

The nature of generative AI comes with different complexities on the modeling side. Some of the modeling challenges that companies may face include: building advanced machine learning and neural network models, simultaneous use of supervised and non-supervised techniques, developing similarity clustering and recommender engines, the contribution of experts in reinforcement learning, defining the best strategy to evaluate Generative AI models performance, and real-time data augmentation to prepare required data.

On top of that, the quality of any machine-learning model directly depends on the quality of accessible data.

This is even more critical in the case of Generative AI models. Unlike traditional machine learning models trained for specific goals, Generative AI covers a wider range of context to accurately generate novel content without compromising privacy or confidentiality.

In fact, these models depend on massive historical data and/or real-time data which is current, online, and constantly updated. This dependency introduces different challenges which are discussed below.

To Buy or to DIY?

When CTO’s, data scientists, and developers run into the obstacles of Generative AI development they must make a fundamental decision: “Will we build in-house or buy a pre-made solution from a vendor?”

Although building in-house seems like a straightforward solution, many teams opt to look for an alternative after realizing the drawbacks of this approach:

Significant upfront labor by advanced technical personnel (Software development, DevOps, Cloud infrastructure, etc) to build the pipeline efficiently
Continuous system maintenance that drains time from other critical roadmap efforts
Difficulty connecting development efforts to clear business use cases that justify the resource investment

This leads many in-house unstructured data projects to end up being delayed, over budget, abandoned, or never started in the first place.

About Datastreamer

Datastreamer is a data pipeline platform that empowers you to leverage unstructured data in minutes instead of months. While most other data pipeline solutions provide limited or no functionality to work with unstructured data – we specialize in it. We work with partners like PrivateAI and Cohere AI to deliver the unique functionality that Generative AI companies need.

Our customers are able to build up to 95% faster and save an average of $700,000 annually. We have case studies that showcase how we make it effortless to ingest, unify, enrich, and extract value from unstructured and semi-structured data.

Below, we map out challenges specific to the Generative AI space and elaborate on how Datastreamer helps you solve them.

Challenges & Solutions of Building Generative AI Models

Challenge: Unstructured & semi-structured data sources.

The majority of required data sources for Generative AI are unstructured and semi-structured data.

They do not have a predefined structure or schema
Unlike structured data, these data sources are not easily searchable

💡 Solution:

Datastreamer offers data transformation that provides a structured version of the unstructured data.

Challenge: Data aggregation over different unstructured data sources.

There is no doubt that the more comprehensive data can be captured, the more knowledge the Generative AI models can produce. However, data aggregation can be difficult when:

There is no global schema for different unstructured data sources
Each data source can have its own schema
Lack of schema consistency prevents search capability in different data sources at the same time

💡 Solution:

Datastreamer offers a unified schema over different data sources that lets customers pull out data from different sources using a single query.

Challenge: Capability of search in unstructured data sources.

Although unstructured data sources are not as searchable as structured data, we cannot fully benefit from the integration of data sources without search capability.

💡 Solution:

Datastreamer’s full-text search API which is based on Apache Lucene is able to offer advanced search facilities on top of a high-quality content index. This API allows you to search for arbitrary text strings, search with complex boolean logic, and so forth.

Challenge: Building an in-house unstructured data pipeline to feed the Generative AI models.

Requires advanced technical skills and resources (Software development, DevOps, Cloud infrastructure, etc) to build the pipeline efficiently
Requires continuous system maintenance
Time consuming and expensive process

💡 Solution:

Datastreamer offers a powerful and user-friendly unstructured data pipeline that customers can access to all services with a single API key.

Challenge: Access to massive historical and real-time data to efficiently feed Generative AI models.

Providing real-time data requires an efficient system design and dynamic system maintenance which is costly to host
Hosting massive historical data and managing the data storage requires significant technical skills and resources

💡 Solution:

Datastreamer API provides access to different kinds of data sources both historical and real-time very efficiently.

Challenge: Scalability and system infrastructure.

Continuously feeding a large Generative AI model requires reliable sources that should be scalable according to the dynamic flow of the system.

Requires technical skills to design and implement and maintain an efficient scalable system
Requires great DevOps and data engineering skills to manage the cloud infrastructure and computational clusters
Hosting these features in-house are very time-consuming and costly

💡 Solution:

Datastreamer offers a scalable and cloud-based API which is significantly less costly than hosting them in-house

Challenge: Integration of new data sources to the pipeline.

Generative AI companies often try to integrate new data sources to feed their models with an in-house approach. This:

Requires technical skills to standardize the unstructured data sources and design schema efficiently
Requires technical skills both for integration and maintenance
Is immensely costly and time-consuming

💡 Solution:

Datastreamer removes 95% of the effort in integrating new data sources (either your own data or from a third party data partner) to the pipeline.

At Datastreamer, we can use your API key to funnel this data into your system privately and securely. This can be in combination with or separate from our provided data sources, depending on your preference.

By integrating Datastreamer, you can accelerate your roadmap and speed-to-market. All data sources can be managed in the same place so that you can sort through data from separate sources without changing platforms.

Challenge: Noisy Text Data

The quality of a Generative AI model is directly impacted by the quality of the data fed into it. So, introducing lower quality data to the Generative AI model simply reduces the capability of the models.

💡 Solution:

Datastreamer provides different machine learning models as a tool to enhance the quality of the extracted data noticeably. This is achieved by by filtering out non-required data (noise) from them. These tools are very impactful in data extraction, especially in the case of noisy texts.

Some examples of these enrichment tools are:

Filtering event-based and action-based news from opinion-based news
Filtering opinion-based social media posts from action-based ones
Filtering social media content based on violent content
Filtering twitter data based on a different level of geolocation of the post (city, region, country): a case study could be limiting data to particular regions in order to capture specific cultural knowledge
Filtering Instagram data based on the country of the post
Classifying unstructured text documents (social media, news, etc) based on the expressed sentiment
Filtering unstructured text documents (social media, news, etc) based on different named entity mentioned in the text

Challenge: Generative AI models may depend on massive historical data.

The request of applying machine learning models to all historical data can be very much challenging

💡 Solution:

Datastreamer provides a post-processing option as one of the components of Datastremer API. With a single query, you can ask our API to apply postprocessing on the result of a Search API query.

Challenge: Building a new machine learning model for a very special use case.

Although there are many pre-developed and ready-to-go machine learning models, you may need to build a new text classifier, say for example a sentiment model specifically for legal and justice documents. It would be resource intensive to leverage your in-house team to develop these new models from scratch.

💡 Solution:

Datastreamer makes building a new machine learning model easier through our partnership with Cohere AI, whose API is already integrated into the Datastreamer pipeline.

So to create the specific machine learning model mentioned above, you would only need to have your dataset ready and the rest would be as simple as calling the Datastreamer API for model building model functions.