The process of ordering the development tasks and features of an AI product based on their importance and impact.

Adhering to legal standards and ethical guidelines in the development and deployment of AI products.

The repeated process of revising and improving an AI product based on feedback and performance data.

A comprehensive list of tasks and considerations to address before releasing an AI product to the market.

The process of managing an AI product from conception through development, launch, and retirement.

The process of evaluating how well an AI product meets the demands and needs of its target market.

Measurements used to evaluate the effectiveness, efficiency, and impact of an AI product.

The strategic approach to managing a collection of AI products to optimize performance and growth.

Strategies for setting the price of AI products to reflect their value, market demand, and competitive landscape.

A strategic plan that outlines the vision, direction, and progression of an AI product over time.

The unique benefits and value an AI product offers to its users or customers.

The process of choosing the right technologies and tools for developing and deploying AI products.

The application of agile practices to the development of AI products, emphasizing flexibility, collaboration, and customer feedback.

The evaluation of competitors in the AI space to inform strategic decisions and product positioning.

The practice of various specialized teams working together to develop and manage AI products.

A systematic approach to collecting, analyzing, and integrating user feedback into the AI product development process.

The process of dividing the potential market for an AI product into distinct groups of consumers based on specific criteria.

The practice of creating AI products that adhere to ethical guidelines and consider societal impact.

A plan that outlines how an AI product will be positioned, marketed, and sold to reach its target audience.

Key performance indicators specifically tailored to measure the success and impact of AI products.

A minimal version of an AI product designed to meet the most essential needs of users, facilitating rapid feedback and iteration.

Factors that influence the ability of an AI product to grow and handle increased usage or data volume.

The practice of engaging and communicating with individuals or groups who have an interest in the success of an AI project.

Narratives that describe the functionalities of AI features from the end-user's perspective.

The guidelines that ensure AI products are developed with a primary focus on the end-user's needs and experiences.

Strategies to enhance the performance and efficiency of AI algorithms, including speed and resource usage improvements.

The integration of AI technologies with IoT devices, enhancing their capabilities with intelligent data analysis and decision-making.

The use of cloud infrastructure and platforms to build, deploy, and scale AI applications, providing flexibility and scalability.

Security measures and practices to protect AI applications from threats and ensure data privacy and integrity.

A tool that utilizes AI to help prioritize, organize, and manage tasks and projects more efficiently.

The design and structure of data processing workflows that prepare and move data for AI model training and inference.

Guidelines and methodologies that improve the efficiency, reliability, and maintainability of AI development projects.

Frameworks that provide structures and tools for developing AI applications, including libraries, pre-built models, and debugging tools.

Efforts to ensure AI systems operate fairly, transparently, and without infringing on ethical standards or promoting bias.

The process of selecting, modifying, and creating features from raw data to improve the performance of AI models.

AI systems that analyze individual user data to provide tailored suggestions for content, products, or services.

The use of AI to analyze historical data and make predictions about future events, trends, or behaviors.

The deployment of AI algorithms directly on edge devices, allowing for real-time data processing and decision-making closer to data sources.

Ongoing oversight and updating of AI models to ensure they remain accurate and effective over time.

The process of teaching an AI model to make predictions or decisions and then testing its accuracy on a separate dataset.

Software that assists in organizing, tracking, and managing AI project tasks, timelines, and resources.

The hardware and software frameworks that support the efficient scaling of AI systems to handle increasing workloads.

The application of AI to condense large texts into shorter summaries, preserving key information and meaning.

The use of AI to automate complex processes or tasks, often involving decision-making or predictive analysis.

Improvements in user interface and interaction through the application of AI, making systems more intuitive and responsive.

Practices that automate the integration of code changes and the deployment of AI applications to production environments.

AI applications and services that can operate across multiple operating systems and hardware platforms without significant modifications.

The process of tagging data with labels to make it understandable for AI models, essential for supervised learning tasks.

The process of embedding AI capabilities into pre-existing software or hardware systems to enhance functionality or efficiency.

AI software and libraries available for free use and modification, fostering collaboration and innovation in the AI community.

AI systems that can process and respond to inputs or data in a timely manner, often immediately or within a very short time frame.

The process of ensuring the data used for training large language models (LLM) is accurate, consistent, and free of errors or irrelevant information.

Methods used to adjust a pre-trained large language model on a specific dataset or for a particular task to improve its performance.

The ability to understand and articulate the reasoning behind the decisions and outputs of large language models.

Difficulties in efficiently expanding the capacity of large language models to handle increased workloads or larger datasets.

The presence of prejudiced assumptions or unequal representations within the data used to train large language models, affecting their outputs.

Adherence of AI systems to the General Data Protection Regulation, ensuring data protection and privacy for individuals within the EU.

The mechanisms and processes in place to obtain and manage users' permissions for data collection and processing in AI applications.

Methods used to alter personal data in a way that prevents individual identification, enhancing privacy in AI datasets.

The use of cryptographic techniques to secure data in AI systems, protecting it from unauthorized access and breaches.

AI algorithms designed to safeguard user data, ensuring that personal information is not revealed during processing.

Structures and guidelines that hold developers and users of AI systems responsible for their functioning and outcomes.

The examination of AI impacts on fundamental human rights, ensuring technology supports and does not infringe upon these rights.

Practices ensuring AI systems do not perpetuate biases and are accessible and equitable to diverse groups of people.

Foundational guidelines that inform the creation of AI systems with respect for human values and ethical standards.

The clarity and openness with which AI systems and their decision processes can be understood by users and stakeholders.

The process of visually representing the stages a customer goes through in interacting with a product, from initial awareness to post-purchase.

Approaches for ensuring clear and effective information sharing within teams responsible for product development and management.

Various techniques used to gather input and reactions from users regarding a product's performance and features.

A structured approach to messaging and channel selection for announcing a new product to the market.

Methods used to craft and convey narratives around a product, emphasizing its value and relevance to the audience.

Communications that provide users with information about new features, fixes, and changes in product updates.

Guidelines for creating clear, comprehensive, and user-friendly instructions and information about a product.