Enterprise AI Platform—Key Features for Success | Nexla
Enterprise AI Platform—Key Features for Success
Table of Contents
- Essential features in a complete enterprise AI platform
- Exploratory data analysis
- Data product generation
- Multi-model support
- RAG workflow creation
- Centralized data management & governance
- Production-grade security
- Recommendations
- Conclusion
Essential features in a complete enterprise AI platform
An enterprise AI platform has the following features.
| Feature | Description |
|---|---|
| Exploratory data analysis | Allows data discovery and analytics for initial data exploration and identifying the best AI use cases in your enterprise. |
| Data product generation | Allows non-technical business users to create data products that the AI team can use for LLM training. |
| Multi-model support | Allows developers to choose from a range of LLMs to find the best fit for their use case. |
| RAG workflow creation | Provides no-code features for creating your own RAG pipelines for LLM customization. |
| Centralized data management & governance | Provides a centralized system to manage all data integrations for ensuring AI application accuracy overtime. |
| Production-grade security | Provides access control and authorization features for regulatory compliance and enhanced customer trust. |
Exploratory data analysis
Data is the building block of LLMs, providing the foundational element that enables these models to learn, understand, and generate human-like responses. High-quality and diverse datasets provide the raw material from which LLMs derive contextual understanding.
An enterprise AI platform should provide data analysis and discovery features for AI development. It should make it easy for data scientists to search for data available in your organization, preview it, and use it.
For example, Nexla is an enterprise AI platform that allows data scientists to access data instantly and test their AI application ideas quickly.
Data product generation
A data product is a cleaned and ready-to-use dataset designed to meet specific business needs—similar to a consumer product ready for use. They streamline data consumption, making it easier for stakeholders to access and utilize data effectively.
An enterprise AI platform makes it easy for AI development teams to create and use data products. For example, Nexla connects to your data sources – files, databases, APIs, streams, etc. – and automatically generates data products.
Multi-model support
Enterprise developers can choose from many different large language models.
| Model | Developer | Availability | Key Features |
|---|---|---|---|
| GPT-4 | OpenAI | Closed Source | Advanced text generation and understanding |
| Claude 3 | Anthropic | Closed Source | Advanced reasoning, multimodal capabilities, enhanced factual accuracy |
| Gemini | Closed Source | Multimodal capabilities, strong performance on benchmarks, different sizes (Ultra, Pro, Nano) | |
| LLaMA 3 | Meta | Open Source | Versions with 8B 70B parameters, high performance. |
| Code Llama | Meta | Open Source | Specializes in code generation, various sizes (7B to 70B). |
| Mixtral 8x7B | Mistral AI | Open Source | Mixture-of-experts architecture, efficient inference. |
| Zephyr | Hugging Face | Open Source | Focuses on alignment and safe outputs. |
RAG workflow creation
Using off-the-shelf LLMs is cost-efficient and practical. Retrieval augmented generation (RAG) combines retrieval-based methods with generative models to inform LLM’s generation process.
- Convert prompt to vectors: Use an embedding service to transform the user prompt into a vector representation.
- Retrieve similar vectors: Find vectors identical to the prompt vector in your vector database.
- Create an improved prompt: Use the context from the retrieved search results to enhance the original prompt.
- Generate response: Pass the improved prompt to the AI model to generate a response.
Centralized data management & governance
An enterprise AI platform provides a centralized system to connect data from anywhere in your organization to your LLM, ensuring compliance with regulations and maintaining accountability.
Production-grade security
Security and compliance are non-negotiable in enterprise AI platforms, requiring protection against model theft and adversarial attacks. Detailed logging and monitoring are crucial for AI systems to track data access and model usage.
Recommendations
- Monitor LLM output quality: Regularly monitoring the quality of model outputs is essential to maintain user trust.
- Human-in-the-loop: Incorporate human oversight into your AI systems to ensure accuracy and relevance.
- Ethical AI practices: Adhere to ethical guidelines and standards to ensure fairness and transparency in your AI applications.
Conclusion
Organizations need an enterprise AI platform that is reliable and offers a range of low-code/no-code data management features for data engineers, AI developers, and business leaders.