You’re developing an AI product. What do you need to consider first?

Technology is an enabler, same applies to AI products as well. Understanding the process, pain points, analysing the approaches is important. AI is exploratory so Proving the concept is first step even if it’s non-AI approach and then thinking about how the product will be embedded in process and how to measure value/impact is the key.

✴️Clearly define the problem your AI product aims to solve. Understand the pain points of your target users and how AI can address them effectively. ✴️Plan how your AI product will integrate with existing systems and processes. Determine the data you need to train and validate your AI model. Consider the quality, quantity, and variety of data required to build a robust and reliable model. ✴️Select the appropriate algorithms and models based on the problem you are solving and the characteristics of your data. Consider factors such as scalability, interpretability, and performance.

Before diving into developing an AI product, the first thing you need to think about is what problem you're trying to solve. It's like laying down the foundation of a house before you start building it. What specific issue or challenge are you aiming to address with your AI product? For instance, are you trying to automate a tedious task, make predictions based on data, or improve efficiency in a certain process? Once you've clearly defined the problem you want to tackle, you'll have a clear direction and purpose for your AI development journey. It's like having a roadmap to guide you towards creating a solution that truly makes a difference.

💡 It's pivotal to meticulously identify the issue your AI aims to address, ensuring relevance and impact. 🎯 Target Audience Understanding the specific group you're designing for is crucial. This knowledge directs feature development and user experience optimization. 🔍 Needs Analysis Pinpointing exact user needs and expectations guides your AI's functionality, making it truly beneficial for its intended users. 🌉 Solution Relevance Ensure your AI solution directly addresses the identified problem, enhancing efficiency or solving the core issue effectively. 📌 An effective problem definition is the cornerstone of developing impactful AI solutions, guiding every subsequent step.

Never create a product merely for the sake of creation, nor implement a technology just to be trendy. First, define the use case: identify the issue you want to address or the value you aim to create. Consider whether the product is intended for internal or commercial use, and identify its target audience. Then, evaluate technological alternatives. Ask yourself if AI is the only or the best way to achieve your goals from a cost/benefit perspective; assess whether you have the necessary competencies and capabilities to build and maintain a robust AI-based product. If everything points to AI as the solution, be prepared to design a framework and a viable product development plan before proceeding.

The initial and paramount step is to thoroughly comprehend the problem statement that the product aims to address and the value it intends to deliver. Following this foundational understanding, subsequent steps can be strategically planned and executed.

When it comes to utlising AI tech/techniques in your solution/product, then we have a choice of using existing AI services as-is (Taker), or going one step further to configure or modifying existing services (Shaper) to suit your needs and in some cases, building your own AI service ground-up (Maker) * Taker, Maker, Shaper is a McKinsey terminology but its not different to the classic, Rent-buy-build option from cloud/data/infra adoptions. This is important regarding AI, as we know contemporary AI services can be very expensive to modify/build and run. The key is to determine your AI Adoption option for your product.

Select the appropriate AI techniques and algorithms based on your problem statement. Consider factors like data availability, computational resources, and the complexity of the problem. If you're developing a recommendation system for an e-commerce platform, you might choose collaborative filtering or content-based filtering based on the available data and the type of recommendations desired.

Your choice depends on the problem you'd want to solve, without neglecting available resources as these will limit your impact. Machine and Deep learning are used to understand and model complex patterns in data. Computer vision deals with problems related to visual data such as images videos etc. Natural language processing is used in problems involving speech or text.

To find the right AI approach, start by defining your project goals and researching which AI methods, like machine learning or natural language processing, fit your needs. Assess your data availability, computational resources, and the problem's complexity. Engage with the AI community for insights and experiment with different models, beginning with simpler ones to set baselines. Iteratively refine your approach based on performance feedback and stay updated on AI advancements to ensure your methodology is effective and up-to-date.

Alternative approaches to data acquisition for AI include: Public, Open-Source Datasets: Research institutions, non-profits, and commercial organizations often share pre-cleansed, anonymized datasets for model training. Data Enrichment: Collaborate with industry peers to expand datasets. For instance, Mastercard and 9 UK banks, including Lloyds, Halifax, and NatWest, created a large-scale payment dataset for fraud detection models. Synthetic Data: Use computer-generated datasets for secure model training. Synthetic data can enhance privacy and representation, especially in sensitive fields like healthcare and finance. Gartner predicts 60% of AI data will be synthetic by 2024. All acquired training data must be pre-cleansed and cataloged.

Determining the mechanisms for data collection is the first step. This includes identifying whether data will be sourced internally, externally, or through user-generated inputs. Once data sources are established, the next consideration is how to store, process, and manage this data efficiently and securely.

When developing an AI product, it's crucial to consider the problem you're solving, the target audience, data availability, ethical implications, and potential biases in the data and algorithms. Understanding these factors will help guide your development process effectively.

AI models need ample data for accurate results. Large language models (LLMs) like ChatGPT require 570GB to 45TB of training data. A general rule is to have at least 10 times as many data observations as features to train a viable model. First, ensure you have enough data for your use case. Conduct a data discovery session to identify available data and its usability for AI development. Global data privacy laws and regulations limit the use of sensitive information for analytics. Not all assets can be used unless privacy-preserving techniques like data aggregation, anonymization, homomorphic encryption, or federated learning are applied.

Evaluate the quality, quantity, and relevance of your data for training your AI model. Consider data preprocessing steps like cleaning, normalization, and feature engineering. When building a sentiment analysis model for social media data, assess the sentiment labels' consistency and the diversity of text samples across different demographics.

When designing the UX of an AI-native product, especially when using an LLM, first start by visually architecting your system and indicating where the data that will be input into the model will come from, and then what information will be output or generated. Then, visually audit all of the parts of the product where the user experience could fail, and design high-value error states so that no matter what, users still get value from using your product.

Design the architecture of your AI system, including model selection, hyperparameter tuning, and infrastructure requirements. Create a development roadmap outlining milestones and iterations. Design a convolutional neural network (CNN) architecture for image recognition tasks, considering factors like the number of layers, filter sizes, and activation functions.

Modeling is the next step in the AI project cycle. Generally, all AI systems are based on either machine learning, deep learning, or foundation (generative) models. These, in turn, can be created using different algorithmic approaches. Your use case(s) and data availability will dictate the optimal algorithm method. Our best advice on how to program an AI application? Work from your use case and analytical problem to find the optimal algorithm. As you evaluate different AI frameworks consider factors like accuracy, interpretability, and computational cost.

Designing an AI solution requires a clear understanding of the problem domain, which will influence the choice of features and metrics. The selection of algorithms must align with the nature of the data and the problem complexity. It's crucial to establish a rigorous process for training, testing, and validation to ensure the model's generalizability and to prevent overfitting. This phase is iterative and may require several cycles to refine the AI model to meet the predefined success metrics effectively.

It's blueprint time! With your data assessed, design your AI solution. Define what goes in (inputs) and what comes out (outputs). Decide how to measure success and choose the best algorithms for the job. This design phase lays the foundation for building a powerful and effective AI product.

To integrate your AI solution into a final product, start by rigorously testing and validating the model's accuracy. Optimize the model for production to ensure it can handle real-world demands. Collaborate with developers to incorporate the AI model into the product's architecture, focusing on seamless integration and user interface compatibility. Implement continuous monitoring to maintain model performance and incorporate mechanisms for collecting user feedback for ongoing improvement. This approach ensures the AI solution enhances the final product effectively and remains adaptable to future needs.

Integrate your AI model into the intended platform or application, ensuring compatibility and scalability. Test the integration thoroughly and implement necessary APIs or interfaces. Integrate a natural language processing (NLP) model into a chatbot platform, ensuring seamless communication with users and proper handling of various language nuances.

Integration with existing eco-system is very important to sell the solutions. No one wants isolated solutions from which they have to spend time/effort to integrate. API access, containerization etc. should be part of the overall technical architecture

Integrating an AI solution into a product requires a multidisciplinary approach, blending AI technicalities with user-centric design principles. It's crucial to ensure that the AI's complexity doesn't hinder the user experience. Therefore, the AI should operate seamlessly in the background, providing intuitive interactions. Additionally, robust APIs and middleware are essential for smooth integration with existing systems, ensuring that the AI components communicate effectively with the rest of the product architecture.

Integration involves seamlessly incorporating the AI system into the existing infrastructure. Prioritize compatibility with existing systems, APIs, and data sources. Rigorous testing and validation are crucial to ensure smooth interaction between the AI components and the broader ecosystem. This phase aims for a cohesive integration that enhances overall system functionality.

Measure the performance of your AI product against predefined metrics and KPIs. Gather feedback from users and stakeholders to iteratively improve the system. Evaluate the accuracy, precision, and recall of a fraud detection model in a financial institution, and analyze its impact on reducing false positives and negatives.

When building an AI product, focus on real-world impact, not just technical achievements. Start by understanding the problem you're solving, ensuring it's significant and your AI solution adds genuine value. Prioritize user feedback, constantly test and iterate based on real usage data, and stay committed to ethical AI development, considering long-term consequences. Always aim for a product that evolves, adapts, and continues to offer relevance and value in the ever-changing tech landscape.

Techniques for monitoring drift are much better defined for traditional Machine Learning than for LLM AI applications. Understanding the sensitivity of your LLM/RAG responses to the currency of your data is critical and should be incorporated into your drift measurement.

As per our design process, the development is cyclic and it never ends by providing the end product. There is always a chance to modify, iterate, evolve and emote. For AI product mapping impact in necessary but mot than that it is indeed necessary to map the evolving need for the same purpose. Which means once we map the impact we must also map the desire of the user to go beyond this AI product.

Evaluate the impact of your AI product by assessing its performance against predefined metrics and user satisfaction. Analyze how well it addresses the defined problem and whether it achieves the intended outcomes. Solicit user feedback and iterate on the model or system to continually improve its impact and effectiveness. Regular impact assessments ensure the AI product aligns with its goals and delivers value.

AI development teams typically choose between building a custom MLOps platform (by combining the best AI software into an integrated developer environment) or using different commercial AI platforms, pre-furnished with different AI developer tools (e.g., Amazon SageMaker, Google Cloud Vertex AI, Azure Machine Learning platform, etc). Whether you’re assessing a commercial offering or building your toolchain, invest in the following MLOps platform capabilities: Data management and pre-preprocessing. Experimentation and model development. Pre-installed frameworks and libraries. A model registry. Model deployment pipelines. Orchestration tools. Model governance tools.

Continuously monitor the AI product post-deployment for any issues or biases. Stay updated with advancements in AI technology and regulations to ensure compliance and ethical use. Implement ongoing monitoring of an AI-driven medical diagnosis system to detect and address any biases or inaccuracies that may arise over time.

Only three things.. Data... lots of clean and valid data.. high quality data Rest everything can be sorted I think everyone will agree with me on this

Firstly, define the problem your AI product will solve and the target audience. Conduct thorough market research to understand user needs and preferences. Consider data availability, quality, and privacy regulations. Determine the appropriate AI techniques and algorithms. Plan for scalability, usability, and integration with existing systems. Finally, prioritize ethical considerations and potential societal impacts.

In developing an AI product, begin by clearly defining its objectives and understanding the problem it aims to solve. Prioritize data quality, privacy, and ethical considerations, focusing on mitigating biases and ensuring transparency. Design a user-friendly experience, considering scalability and flexibility for future growth. Stay informed about and comply with relevant regulations, fostering collaboration among cross-functional teams. Implement rigorous testing and validation procedures, and ensure explainability of the AI model's decisions. Establish a continuous feedback loop for iterative improvements based on user and stakeholder insights.