Building your AI stack with MongoDB, Anyscale, Cohere & Fireworks AI (AIM104)

Building AI Stack with MongoDB and Partners

Key Factors for Building AI Applications

  1. Data

    • Clean and high-quality data is the foundation for building effective AI applications.
    • Data can be used for retrieval, augmentation, generation, fine-tuning, or pre-training.
  2. Performance, Efficiency, and Cost

    • Training and inferencing of large language models (LLMs) can be computationally expensive.
    • Designing applications that can run on smaller hardware while maintaining low latency and high throughput is crucial.
  3. Privacy and Security

    • Ensuring the privacy and security of customer data used in the AI applications is a key concern.
    • Deployment strategies, such as using isolated environments or air-gapped environments, need to be considered.
  4. User Interface (UI) Design

    • Intuitive and user-friendly UI is essential for driving adoption and usage of the AI applications.
    • Good UX can lead to better feedback and model improvements.

Lessons Learned from Scaling AI Applications

  1. Multimodal Data and Compute Infrastructure

    • Models are becoming more multimodal, requiring the handling of diverse data types (text, images, audio, video, etc.).
    • Distributed computing tools like Apache Ray can provide the necessary flexibility and scalability to set up the required compute infrastructure.
  2. Compound AI and Specialized Models

    • Combining specialized models into compound AI systems can outperform general-purpose frontier models for specific use cases.
    • Investing in customized, fine-tuned models for specific applications can yield better performance than relying solely on large, general-purpose models.
  3. Contextual Relevance over Synthetic Benchmarks

    • Models that perform well on synthetic benchmarks may struggle in real-world, messy business environments.
    • Customization and fine-tuning of smaller models for specific use cases can be more effective than using the largest, most general models.
  4. Collaboration and Open-Source Tools

    • The open-source community provides a wealth of tools and libraries (e.g., LangChain, LangGraph, LlamaIndex) that can streamline the consumption and integration of LLMs.
    • Leveraging these collaborative efforts can help make it easier for developers to build and deploy AI applications.

Exciting Use Cases for Generative AI

  1. Personalized Medicine

    • Generative AI can help reduce the time spent on documentation in the healthcare industry, allowing more time for patient care.
    • AI-generated summaries of patient records and treatment plans can improve efficiency and outcomes.
  2. Application Modernization

    • Generative AI can help generate code documentation and even create new code for legacy applications, facilitating modernization efforts.
    • Automated code transformation and testing can help address the challenge of certifying the correctness of the modernized application.
  3. Semantic Search and Drug Discovery

    • Generative AI can enable powerful semantic search capabilities, allowing users to find relevant information and drugs based on multimodal data (e.g., images, text).
    • This can significantly accelerate drug discovery and other research processes.
  4. Personalized Healthcare Guidance

    • Generative AI can provide personalized advice and suggestions to healthcare providers at the point of care, leveraging the patient's electronic health record (EHR) data.

Transitioning from Prototype to Production

  1. Cost and Performance Optimization

    • AI models can be expensive, particularly during the experimentation phase with frontier models.
    • Techniques like model customization, fine-tuning, and using smaller, optimized models can help reduce costs while maintaining performance.
  2. Addressing GPU Shortages and Infrastructure Challenges

    • The industry is still dealing with GPU shortages, which can impact the ability to scale AI applications.
    • Developing models that can be served on smaller infrastructure, while still meeting latency and accuracy requirements, is crucial.
  3. Handling Hallucination and Feedback Loops

    • Hallucination, where LLMs generate incorrect or nonsensical output, is an inherent characteristic that needs to be addressed.
    • Implementing processes for continuous feedback loops and validation mechanisms is important for production deployments.
  4. Embracing the Evolving AI Landscape

    • While traditional deep learning techniques will continue to be relevant, the capabilities of generative AI and agentic AI are expected to grow significantly.
    • Companies may start offering their own specialized AI models and expertise as "Service as a Platform" offerings, leveraging their domain-specific data and knowledge.

MongoDB AI Applications Program

  • The MongoDB AI Applications program is a holistic initiative that brings together technology providers, hosting providers, and expertise to support the development of AI applications.
  • It enables developers and customers to leverage a diverse ecosystem of partners, including firms like AnyScale, Cohere, and Fireworks AI, to build and deploy robust AI solutions.
  • The program provides access to flexible data storage, scalable compute infrastructure, and specialized AI models, allowing developers to focus on building innovative applications.

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