7 Essential Generative AI Tools for Building Stand-Out AI Applications

5. Weights & Biases

Weights & Biases is a platform for machine learning developers to track experiments, visualize results, and optimize the models. It is a lightweight tool for logging metrics, visualizing model training, reproducing experiments, version data, and collaborating with teams.

W&B helps developers build better ML models through experimentation and insights. The platform offers model monitoring and a suite of LLMOps tools built for language applications.

You can use W&B to track generative AI models' performance during training and in production. As an individual, you can use a cloud server for free or run your server.

If you are new to Weights & Biases, check out the guide Machine Learning Experimentation: An Introduction to Weights & Biases to learn how to structure, log, and analyze your machine learning experiments.

For monitoring large language models in production, WhyLabs has built an open-source toolkit, langkit, that extracts signals from prompts and responses, ensuring safety and security.

6. BentoML

BentoML is a powerful framework that empowers developers and data scientists to build and deploy AI products quickly and efficiently. With BentoML, integrating pre-trained machine learning models into production becomes seamless, allowing you to deliver value to your users in minutes.

BentoML offers bentoml/OpenLLM, designed to simplify LLM operations, deployment, fine-tuning, serving, and monitoring. OpenLLM supports many state-of-the-art LLMs and model runtimes like StableLM, Falcon, Dolly, and more. Learn how to build and server your model with OpenLLM in a few minutes by following the Deploy a large language model with OpenLLM and BentoML tutorial.

7. Gradio

Gradio is a powerful tool that has gained significant popularity within AI communities due to its ability to simplify and accelerate the development of machine learning demos. It offers a straightforward and modular approach for building various AI web applications, chatbots, and language applications, making it a go-to solution for many developers.

Similarly, Streamlit is an alternative to Gradio that provides a simplified interface to build web applications for Generative AI.

Before deploying your generative AI model, it is recommended that you create Gradio demos, share them with the community, and get feedback. This will help you build better applications. Additionally, you can use it as a user interface to quickly test your models and impress stakeholders.

Refer to The Generative AI Tools Landscape cheat sheet to see the tools built upon Generative AI models.

Summary Table: Generative AI Tools, Use Cases, and Alternatives

Tips for Building Generative AI Applications

Let’s explore some tips for successfully building a cost-effective, secure, compliant, and stable Generative AI application.

1. Start with a clear goal

You need to decide which type of Generative AI application you want to build and what problem you are trying to solve with it. Having a clear goal will save you time and money.

2. Pick the right tools

Make sure you have picked the right third-party APIs, models, integrations, MLOps, monitoring, and automation tools. Choosing the right tools that work for your application is crucial for project success.

3. LLMOps is crucial

You have to follow AI guidelines and monitor and maintain your models in production. By focusing on LLMOps, you can reduce operational costs and provide end users with a stable and secure product.

4. Follow security guidelines

Detects and analyzes potential prompt injections, data leakage, and other malicious behaviors. Implement strict input validation and sanitization for user-provided prompts to meet industry standards and avoid legal trouble.

5. Test your models offline

Before deploying, test your LLMs offline for accuracy and other performance metrics.

6. Start small with limited capabilities.

Instead of building a full-fledged AI platform, start with a simple chatbot feature, then regularly add new capabilities like uploading files, generating images, etc.

7. Model infrastructure

LLMOps can be quite costly, so perform a cost analysis before choosing cloud services for your application. Companies usually lose money because they are unaware of the memory and computation requirements.

8. Monitor your model in production

Check for degraded performance, bias, and malicious use. Have a rollback plan ready.

Read Unlocking Creativity with Generative AI guide to explore art, music, and literature with the help of AI-generated models.

Conclusion

Generative AI represents an exciting new frontier for building innovative applications that were previously out of reach. With the right tools and thoughtful approach, developers now have immense creative potential at their fingertips. Pretrained models like DALL-E 2 and ChatGPT have opened up the ability to generate stunning synthetic images, human-like text, and more.

MLOps frameworks help transition generative models from research to production while monitoring biases and performance. As the technology matures, more intuitive interfaces are expected.

Although we must remain cautious about data privacy, security, and societal impacts, generative AI has the potential to enable businesses to deliver truly innovative and valuable experiences, and we have only just begun to explore the possibilities.

Now that you have learned about essential tools, it's time to start building your Generative AI project. Check out this blog for project inspiration: 5 Projects Built with Generative Models and Open Source Tools.