In this article, let's review the overall AI product landscape. We'll identify areas where organizations can add significant customer value, develop the necessary skills for developers, leverage modern AI development tools, and build teams to improve efficiency.
Generating AI Product Landscape
The diagram illustrates that the development of generated artificial intelligence can be broadly divided into three layers at a high level.
The computational demands of AI computing platforms are growing exponentially, leading to the development of AI accelerators—specifically designed chips to enhance application performance. Graphics processing units (GPUs), originally created for graphics-related tasks, have become crucial in meeting the massive parallel computational demands of building AI models. Furthermore, many specialized AI accelerators utilize ASIC-specific processors, such as tensor processing units (TPS), learning processing units (LPS), and neural processing units (NPS), significantly altering the workloads of training and inference. Above the physical layer, public and private cloud infrastructures enable virtualization and provide abstractions for various computational needs. Parallel computing platforms, such as data processing systems and OBCL, are essential for effectively utilizing both physical and virtual hardware. For AI, computing platforms are necessary, providing the infrastructure required to build generative AI models.
The foundational models are at the heart of generative artificial intelligence, based on neural network architectures and transducer techniques. Various libraries, such as PYOT and TensorFlow, provide computer vision and natural language processing capabilities, primarily for training and inference neural networks. These foundational models are trained using semi-supervised and self-supervised learning processes on large datasets to understand statistical relationships between words and phrases. Furthermore, Large Language Models (LLMS) are evolving into multimodal models, enabling them to meet diverse application needs.
AI applications use a base model and require an abstraction layer. This layer can extract valuable and customized information, using prompts, frames, retrieval-enhanced generative algorithms (RAPs), agents, or fine-tuning. General horizontal and specialized vertical applications can benefit from these models and their underlying abstraction layers, thereby creating highly intelligent applications.
The entire ecosystem supports every level and fosters overall innovation.
Customer Value
Identify the business environment in which your organization generates customer value. Each layer of the AI technology stack delivers value to a different customer segment. Only a few large companies, such as NVIDIA, Google, Amazon, and Microsoft, operate at all three layers of the technology stack.
Customer value can generally be categorized into hedonism and utilitarianism: the functional value of a product or service, such as its usefulness, quality, and value for money. Utilitarian products are typically practical, effective, and necessary, while hedonistic products offer pleasure, enjoyment, and satisfaction. Hedonistic products are usually exciting, pleasant, and stimulating. They evoke stronger emotional responses than practical products.
Investing solely in artificial intelligence does not guarantee a company's success. Avoid making investment decisions out of fear of missing out (Formo). For a business to thrive in the long term, it must focus on creating value through AI integration. Follow standard processes and conduct thorough due diligence to determine in which areas AI can effectively drive product value.
Work closely with product, business, and engineering teams to define the scope of work and develop a strategic vision, ensuring alignment within the teams. Achieving stakeholder alignment is also crucial, especially given the complexity of the project, while setting realistic expectations.
The role and skills of developers
Each of the levels mentioned above requires developers with specialized skills. The landscape changes daily with the release of a new tool or service. As an engineering leader, invest in the appropriate skills required for the project. Empower the team to make the best decisions. Build strong expertise within the team by providing learning opportunities such as courses, conferences, and triathlons.
This is not an exhaustive list, but rather a glimpse into the different roles and the necessary skill sets.
• Artificial intelligence computing platform:
o ICU Design Engineer:: Designs and verifies global public sector accounting standards or international public sector accounting standards
AI Cloud Platform Engineer: Building infrastructure, tools, and platforms for AI in hypervehicles
• Basic Model:
o AI trainers and data annotators review and validate learning, especially in semi-supervised settings.
o Artificial intelligence/machine learning engineers develop, train, and optimize foundational models.
o Amnesty International researcher and research scientist: Designing a neural network algorithm for transformers.
o Robotics Engineer: Assembles physical components and connects them to software and sensors.
• Artificial intelligence application:
o Rapidly develop AI-integrated consumer coding assistants and AI development tools for engineers.
o No-code and low-code developers: Build solutions using AI services.
o AI software developers, AI agent developers: build or integrate agent frameworks, vector databases or rag dolls.
o Artificial intelligence engineers* fine-tune the base model according to organizational requirements.
Development tools
Generative artificial intelligence can provide human-like responses, making AI assistants a revolutionary application. More advanced versions of these assistants have evolved into coding aids and tools for developers.
AI services like FLOVE and UUGIN are no-code and low-code platforms that can completely replace the entire development cycle.
Includes Amazon Codec Integration, among all the most popular development tools, and assists at every stage of the engineering development cycle by designing UIs, developing code, auto-completion, code summarization, debugging and modifying code, generating tests, deploying and monitoring solutions.
Depending on the cloud computing platform used, Amazon Sajmo, Google Cloud Apex AI, and Microsoft Azuma Learning provide integrated services for the entire machine learning lifecycle.
Engineering leaders should analyze and invest in the tools available to developers to improve their productivity.
Organizational structure
After successfully developing an AI prototype, leaders often face the challenge of integrating AI development into the core engineering team. Whether the team is building AI solutions or integrating AI, engineering management must make the right decisions.
When a team is developing an AI solution, there must be a dedicated group of engineers with specific roles and skills. This process typically requires extensive research and development in artificial intelligence, and these engineers should be integrated as part of the core engineering team.
Teams building AI integrations may develop AI applications and integration layers. They typically have an existing application in which they plan to incorporate AI model responses. In this scenario, a subsystems team should focus on creating recycling-enhancing-generation (rag) and agent subsystems that will be integrated into the application. This subsystems team could be part of the core engineering team or the platform engineering team.
Platform engineering should guide the development of reusable GEII components by creating an AI developer platform for infrastructure, tools, libraries, etc.
Ideally, the team should consist of 6 to 8 developers who work in the same location or in a similar time zone to facilitate effective communication and collaboration.
Regardless of team structure, intelligent development practices provide the best approach for AI development teams, employing iterative, introspective, and adaptive methods.
Final thoughts
The field of artificial intelligence has been developing for some time. We will experience more unprecedented applications and integrations. While leadership understands the product landscape, invests in identifying the right customer value, organizes teams to improve efficiency, and invests in developer skills and tools, it is crucial to focus on continuous learning by observing the latest AI development best practices and integrating them with the engineering team.
