What is generative AI?
Unlike traditional machine learning models that analyze existing data, generative AI can create new content that closely resembles the original inputs or prompts. Applications span live chat, text, images, audio, videos, and music composition.
The unique complexities of generative AI
Building generative models introduces a set of modeling challenges that go beyond standard analytics work:
- Building advanced machine learning and neural network models
- Using supervised and non-supervised techniques at the same time
- Developing similarity clustering and recommender engines
- Reinforcement learning expertise
- Defining evaluation strategies for performance
- Real-time data augmentation
Underneath all of it sits one dependency: data. The quality of any machine-learning model directly depends on the quality of accessible data, and that is especially critical for generative AI. These models require massive historical and real-time data while maintaining privacy and confidentiality.
To buy or to DIY?
Building this data capability in-house carries real drawbacks: significant upfront labor costs, continuous maintenance that drains resources, and difficulty connecting the effort to a clear business justification. As a result, projects often face delays, budget overruns, abandonment, or simply never get finished.
Challenges and solutions
The table below maps the ten most common data challenges in generative AI development to how Datastreamer addresses each one.
| Challenge | Solution |
|---|---|
| 1. Unstructured and semi-structured data sources Data lacks a predefined structure or schema and is not easily searchable. |
Datastreamer offers data transformation that provides a structured version of the source data. |
| 2. Data aggregation across sources No global schema exists. Each source has its own schema, which prevents searching across sources at the same time. |
A unified schema that lets you extract data from many sources with a single query. |
| 3. Search capability in unstructured data Finding specific content inside unstructured data is difficult. |
A full-text search API based on Apache Lucene that supports complex boolean logic. |
| 4. Building in-house pipelines Requires advanced technical skills and is time-consuming and expensive. |
A user-friendly Datastreamer pipeline accessed with a single API key. |
| 5. Accessing massive historical and real-time data Real-time systems require expensive dynamic maintenance, and historical data storage demands significant resources. |
The Datastreamer API efficiently accesses both historical and real-time data. |
| 6. Scalability and infrastructure Requires DevOps expertise, and in-house hosting proves costly. |
A cloud-based, scalable Datastreamer API at lower cost. |
| 7. Integrating new data sources Requires standardization and schema design skills, and integration and maintenance prove costly. |
Datastreamer removes 95% of the effort in integrating new data sources. |
| 8. Noisy text data Low-quality data reduces model capability. |
Machine learning models filter noise by distinguishing event-based from opinion-based news, filtering violent social media content, filtering by geolocation (city, region, country), classifying sentiment, and recognizing named entities. |
| 9. Processing massive historical data Running operations over large historical volumes is resource-intensive. |
A post-processing option available through a single API query. |
| 10. Building specialized models Custom model development is resource-intensive. |
Integration with Cohere AI that simplifies model creation. |
About Datastreamer
Datastreamer specializes in unstructured data pipeline management, partnering with PrivateAI and Cohere AI. Customers build up to 95% faster and save an average of $700,000 annually, managing diverse data sources within a single platform without switching between systems.