What are the key components of a successful generative AI project?

Having built multiple GenAI PoCs for various enterprise use cases, data collection and cleaning is the single most important part of a successful GenAI project. With the current pace of innovation, your current model is going to be subpar in 2-3 months, but if you have high-quality data, you'll stay on top regardless. For enterprise use cases, one of the biggest pains is data ingestion. Every organization has its data all over the place - data warehouses, NFS, S3, and half a dozen SaaS solutions. To add to the pain, organizations have their specific document templates and unique formatting rules. If you can ingest all this data and preserve its semantic properties (i.e. relations, significance, and structure) - you're doing great.

There are very helpful tools that can make these models more reliable and thus trustworthy. One example is the work done at @Themis AI - they create model-agnostic tools to detect bias and uncertainty in ML models. Their tool is helpful to detect lack of representation and noise in dataset

A generative AI project's success hinges on precise objectives, diverse data, robust models, iterative testing, feedback incorporation, and ethical considerations. Structure it around a roadmap that integrates these elements, anticipates risks, and involves stakeholders for informed, adaptable progression.

This description here is assuming that your generative AI project depends on the creation of a custom model for your use. Not all generative AI projects of value will need their own custom model. You could also just choose to use a method called Retrieval Augmented Generation (RAG), that uses an underlying text generating model, and just fine tunes it to achieve your particular end goals.

High-quality data is crucial for the success of generative AI projects. Thoughtful data collection and preparation involves clearly defining the project scope. Then a largely diverse dataset needs to be sourced. This data must be carefully formatted to remove correctly. Useful techniques to format include data augmentation, benchmarking, and rigorous validation/testing splits. Particularly for image, video or audio generation, data standardization and preprocessing is essential.

Unlocking AI's potential in healthcare pivots on the right generative model. Consider variational autoencoders (VAEs) for structured data like patient records. Turn to generative adversarial networks (GANs) for realistic medical imaging. And for textual medical content? Transformers are key. So, how to choose? Weigh healthcare application, data nature, and compute capacity. Don't forget, optimizing architecture, tweaks, and smart optimization strategies are crucial for the model's efficiency. Leverage tools like TensorFlow, PyTorch, and Keras to ease model implementation and training. Remember, the right generative model selection means quality, efficiency, and accuracy in AI-driven health solutions.

Model selection should be heavily influenced by the customer use case: are they trying to find suggestions for code they are writing? Or are they looking to find the insights in large blocks of text? Or they looking for a forecast of volume orders? Once this is established, then this can inform the choice of using Large Language Models or even custom trained ones.

Selecting and training the right model is crucial in a generative AI project. Models like VAEs, GANs, or transformers have their own strengths, and the choice depends on your project goal. Training involves setting the right parameters to help the model learn effectively. Tools like TensorFlow, PyTorch, or Keras can be used for this step. This stage sets the foundation for how well the AI can generate new content.

- As mentioned by LinkedIn, different models are for purposes (I am referring to use of LLMs). Some are optimal for summarization, some are good in giving answers to questions etc. Also some of the models are for a specific purpose, for example there are LLMs created only for contracts and legal purposes. So right model selection is critical.

LLM model selection can depend on your use-case - at Datatonic we prefer Google's PaLM models, which are available pre-trained in several specialisations for text, chat, code, image and multimodal generation. Whilst these models can be fine-tuned on Google Cloud, for instance using LoRA techniques, it can be far more efficient to tune LLM outputs using prompt engineering techniques, such as few-shot prompting.

- Best way to evaluate Generative AI model's performance is by human evaluation which is expensive. There are statistics available, however these statistics h/ave worked only to a limited extent for me. - Performance improvement can be done by fine tuning, or even building a new LLM specific to your business problem or industry. However today building a new LLM is very expensive. - In industry, what's being most used today is RAG (Retrieval Augmented Generation). That's because RAG in multiple situations have given higher performance than prompt engineering, but it's not expensive.

Thinking about one specific type of Gen AI project, Large Language Model (LLM) evaluation is still a hard problem with the current state of the art. Two main solutions currently exist - self-evaluation (where an LLM reviews it's own response to a query and rates it for accuracy, relevance, etc.) and human-evaluation (where an expert reviews model output and identifies flaws in the response). At Datatonic we prefer to follow an iterative development cycle for receiving the most relevant human feedback during development, and recommend a human in the loop for quality assurance during initial deployment, but we are also finding that LLM self-evaluation can be appropriate for specific use-cases when the chance of hallucinations is low.

Metrics like accuracy, diversity, and fidelity help evaluate your model's output. Testing your model under various scenarios helps uncover issues, biases, or limitations. For instance, if you're developing a text generation model, test it with different language styles or topics to ensure robustness. Data augmentation enhances diversity by creating variations in your training data. Understanding the interplay of these is where your technical skills shine. Proficiency in Python and deep learning frameworks will enable you to implement these strategies effectively. Try innovative approaches, such as generative adversarial networks (GANs) for data augmentation or reinforcement learning for model optimization.

There are several metrics to measure the performance of these models. The Frechet Inception Distance (FID) is a widely used metric that assesses the quality of images generated by AI generative models. Essentially, it measures how close machine-generated images are to real ones. Lower FID scores indicate higher image similarity.

Regretfully, the objectives we optimize for in most GenAI projects are high-level and hard to quantify. Open-ended dialog systems are the hardest to evaluate. The best way to do it would be via human expert feedback, but that's expensive. Alternatively, if your privacy requirements allow it, you can use GPT-4, as it was shown to be on par with people for some domains, but also be aware that it has its own biases, like preferring specifically worded text, or even the first option in a pair preference evaluation setting. Thankfully, what's often required is not a ChatGPT clone but a dialogue-based retrieval and summarization tool. These can be evaluated using ranking-based scores (i.e. MAP@3), and semantic similarity metrics, respectively.

- Deployment is when real value comes from the AI/ Gen AI solutions. - In deployment environment, we need to have an efficient monitoring system, deployed solution should be scalable, security systems should be maintained, solution need to be auditable and traceable etc. - Output from the monitoring systems need to be observed continuously and with diligence. When performance starts dipping, AI solution needs to be recalibrated.

Cloud platforms like AWS, Azure, or Google Cloud offer scalability for deployment. Containers using Docker and orchestration with Kubernetes can streamline deployment across different environments. Familiarity with DevOps practices, and containerization techniques is indispensable. Monitoring your model post-deployment is essential. Tools like Prometheus or Grafana help track performance and alerts for anomalies. Implement version control to track changes, and consider continuous integration/continuous deployment (CI/CD) pipelines for seamless updates. By choosing the right platform, ensuring security compliance, and implementing monitoring strategies, you'll ensure your model remains a valuable asset in a dynamic environment.

GPUs are very expensive, especially the on-demand ones from the clouds like AWS. Let's suppose the GPU costs there are $1000-2000/mo for the on-demand instance. You can have a cheaper dedicated server for $300 (using 4090 instead of A5000, etc). But this is hard to scale and cloud costs don't come from nowhere - dedicated server infrastructure can fail one day because of the SSD drive failure or when the entire data center goes offline. You can use both dedicated and cloud infrastructure - when workers on the dedicated servers go offline or are overloaded, new instances of the worker image can be launched as cloud spot instances. Delays related to peaks (when new instances are just starting) can be dealt with careful load balancing

With MLOps there is simply no "final" step. Deployment and maintenance are indeed critical to the longevity and effectiveness of a generative AI project. However, this "final step" involves a variety of equally important components: - A robust data pipeline is essential to ensure that the AI model is fed with high-quality and diverse data. - Iterative model development and validation, as they enable fine-tuning and ensure the accuracy - Usability and an intuitive UI can have a significant impact on the adoption and success of the project. It's a holistic approach that wins the race, with deployment and maintenance being the final sprint, but one that is prepared by several marathons of groundwork ??

Several companies ( Google Cloud, AWS ,Twilio , Dynamics 365 ) are leading the way in contact center AI solutions. Deployment and maintenance are pivotal stages in the life of a contact center AI system. Deployment involves setting up infrastructure, integrating with existing software, testing, training staff, conducting a pilot phase, and gradually rolling out the system across teams. Maintenance entails continuous monitoring, performance optimization, bug fixing, security updates, scalability assessments, user support, and feedback integration. Proper management of these phases ensures the AI system's effectiveness in enhancing customer experiences and streamlining contact center operations.

I believe the most important thing is to share what you have learned! This will help others learn how to use AI successfully. One of the ways I will be doing this in the near future is to create a newsletter that illustrates how to use specific AI tools for the task at hand. I recently gave my students an assignment to build their LinkedIn profile. Many did not have a professional photo to include. So I steered them in the direction of using Remini. This AI app allows you to upload at least 8 selfies, which can also include other people, and it will create a professional image. This enabled them to post their professional image on LinkedIn. In fact, some of you may have noticed I changed my image using Remini. My students loved using it!

A great model will receive a ton of feedback. Some of it will be useful. Some of it not. Tools like CHatGPT also incorporate feedback as part of the tool. Following each generation you can provide a "Thumbs up" or "Thumbs down" indicating whether the output met your needs in an expected way. This type of feedback data becomes a rich database in itself as to what prospective customers are looking for.

Promotion on social media or blogs isn't just for reach. Use these platforms to gain valuable feedback and insights to further refine your model. A successful generative AI project is a beacon of knowledge for others. Step up, showcase and inspire the direction of future AI innovations.

In my experience i would recommend the following ways to to share your work and have an impact on the community. 1. Build a Portfolio or Website that presents the model's output. Explain the project's purpose, the generative process, and showcase results. 2. Offering your source code, data, and documentation on platforms like GitHub or Kaggle. 3. Documentation is essential to provide clear explanations of your code, and model architecture. 4. Leverage the power of social media platforms on LinkedIn, Twitter, and Reddit. 5. Participate in online communities to engage with potential users and contributors, and consider hosting webinars or workshops to educate and inspire others. 6. Present your project at AI conferences

Platforms like GitHub and Kaggle don't just serve as repositories; they're communities where peer review and feedback can greatly refine a project. The value of transparency in sharing source code, data, and documentation cannot be overstated—it fosters collaboration, drives innovation, and ensures reproducibility, which are all cornerstones of the open-source ethos that has propelled tech forward. That said, a successful project also hinges on robust ethical considerations, ensuring AI is used responsibly, and alignment with user needs, which maximizes its real-world applicability. Balancing innovation with these aspects is challenging, but can lead to truly transformative AI solutions.

In the dynamic world of generative AI, adopting multiple research and prototyping pathways is essential, despite initial higher costs and inefficiencies. This strategy not only enables exploration of varied aspects concurrently but also mitigates risks associated with single-path dependence. It's a long-term investment; fostering innovation and adaptability in response to the rapid advancements and shifts in the AI field. Each pathway becomes a valuable learning avenue, contributing to a richer, more resilient project development cycle. Embracing this multifaceted approach aligns with the iterative spirit of AI, ensuring sustained progress and success.

The Leaner Startup... In the highly influential book, The Lean StartUp, Eric Reis argues for an iterative approach with validated learning when launching a start-up. Many of the lessons espoused by Reis are even more relevant when building a generative AI project due to the ability to iterate at such a low cost. ChatGPT can be used for Concept Validation, Data Preparation, Content Generation for Marketing and MVP’s, Feedback Analysis, Code Creation, and General Strategy Discussion. In today’s world, all generative AI projects should be designed with the use of generative AI as part of the building process. Not only will this accelerate the learn and iterate cycle, but it will also increase the odds of success. #artificalintelligence #ai

Imagine a soccer coach trying out a new computer program that helps create fun and useful training exercises for the team. At first, the exercises are okay, but some are a bit tricky for the players. So, the coach asks the players for their thoughts and they share some ideas to make things better. The coach also keeps learning about the newest ways to coach and uses the program to come up with fresh ideas. Eventually, they come up with a really silly but effective drill where players have to dribble a ball while wearing funny, oversized shoes. It ends up being a big hit, making the players faster and bringing lots of laughs to practice!

Enhancing generative AI involves diverse data experimentation, applying techniques across different fields, and measuring real-world impact. Regular ethical reviews ensure societal alignment, while community engagement offers valuable feedback and innovative perspectives, driving continuous improvement.

From my experience the following practices are very useful: 1. Encourage open dialogue, actively seek critiques, and be open to collaboration. 2. Engage with online forums, academic journals, and attend AI-related events. 3. Consider working on small, experimental projects to explore uncharted territories in generative AI. 4. Maintain a learning journal where you document your experiments, insights, and lessons learned. 5. Exploring projects in these different fields can lead to innovative cross-disciplinary solutions. 6. Consider expanding your skill set beyond AI. Learning about design, user experience, and business can help you create generative AI solutions that are user-friendly and commercially viable.

Guardrails are vital for building a successful Generative AI project. LLMs are prone to hallucinations, or to being misguided by user inputs into outputting harmful / undesired responses. These risks can be mitigated somewhat, but no single solution can completely prevent these risks. At Datatonic, we recommend a defence in depth solution, combining numerous guardrail solutions, including prompt engineering, constitutional chaining, reinforcement learning from human feedback (RLHF) and conversation design, to mitigate threats. It's also key to consider Responsible GenAI usage. Technical considerations in isolation won't lead to a successful or sustainable project. Environmental and ethical impacts need to be evaluated.

The key to a successful generative AI project has more to do with philosophy than the technology itself. Begin with conceptual analysis to clarify the AI’s goals, functions, and ethics, preventing misunderstandings. Then, use task decomposition to break complex tasks into smaller, manageable pieces. This facilitates a structured development process and results in a more dependable and effective AI solution.

From a high level / 360 perspective, I love the way Microsoft speaks about their own AI Transformation Journey in four blocks: * "Technology: scaling AI in an organization requires a platform shift". It is not about doing a pilot / proof of concept and put the check on AI. * "Strategy: a company-wide cross-horizon porfolio". Don't hire a Head of AI and put him/her somewhere in the org. * "Culture: expanding from tech to business and employees". Transformation is mainly about adoption. If we want people to adopt, we need people to understand. Educate and empower your employees. * "Responsibility: distributed everywhere, centralized governance". Companies need to be part of the Responsible AI conversation, internally and externally.

To build a successful Enterprise Grade Generative AI project, one needs to consider the well architected framework comprises of 6 building blocks or strategies: 1. Operational Excellence: 2. Security and Privacy: 3. Compliance: 4. Reliability: 5. Cost Optimization: 6. System Architecture Excellence:

The most critical component in a successful generative AI project is understanding the end goals and intended uses from the perspective of business stakeholders and target users. All major decisions, from data collection to model selection to implementation, should be driven by the specific use cases and audience needs. Maintaining a constant focus on delivering value to end users leads to the most impactful application of generative AI technology. #generativeAI #Aistrategy #CX