Quais são as melhores maneiras de projetar modelos de IA para Business Intelligence

To design a AI model for any use case, first step will always be to understand the problem statement. We need to begin with understanding the goal of the dashboard. Once the objective is clear, understanding the kind of data required for the use case comes next. Availability and accessibility to good quality data is paramount when it comes to BI dashboards. With the access to fit for purpose data,choose a suitable machine learning algorithm and validate the performance from real world data. Also, in case of BI, we need to pay attention to who is the audience and need to mould the UI accordingly.

The best ways to design the AI models is special in the business intelligence tools. First of all you need to understand the complete business. Once you understand the business that you need to understand their data architecture, and once you understand the data architecture, then you need to understand how to manipulate and transform the data. Also understand the transformation of the data than start creating visualisation and start creating reports and dashboards after analysing each and every thing than the particularly Ai models can be created.

Defining problem statement need to be deeply thoughtful and multiple dimensions of stated challenges should be understood. Usually I saw enterprises trying with high level problem statements that could results at major failure rate for an AI program. Therefore while defining problem statement, one should keep the solution mindset out of equation and bring out the depth to most granular level of stating problem and multiple dimensions those could potentially influence while realisation

Na minha opinião, essa é a etapa fundamental. Antes de qualquer coisa, precisamos entender o que estamos tentando resolver. Isso envolve conversar com as partes interessadas, entender as dores e necessidades do negócio, e identificar claramente os objetivos. Comece pelo principal, e faça bem feito!

I have seen when a Company aims to boost sales forecasting accuracy for better inventory management and customer satisfaction. Defining the problem involves specifying business goals, target audience, data sources (transactions, inventory, etc.), expected outcomes (precise sales predictions), and considering ethical, legal, and social implications (privacy, bias, regulations). This clarity guides model development, ensuring relevance and effectiveness.

Select the most suitable AI approach based on the defined problem and available data. Depending on the nature of the BI task, choose between supervised learning, unsupervised learning, or reinforcement learning. Consider whether the problem requires classification, regression, clustering, or anomaly detection. Tailor your approach to align with the specific BI goals and data characteristics.

Decomposing problem statements and map it to data needs(completeness,latency,quality), followed by extensive EDA, Model selection based on problem type, required output accuracy levels, batch vs realtime, performance and hardware requirements including hosting options, TCO. Experimental study, Then the standard procedure of iterative learning and testing till it reaches desired results. Every mode of selection can also look at readily accessible apis/models to take advantage of LLMs and possible transfer learning options

Old School is always a good option when adapted to the language. Instructions into prompts can replace deterministic outputs. However, BI must have precision and traceability. Any AI powered BI shall ensure a traceable path. A simple application of AI to BI is debugging the diversity of naming the same or duplicated articles by classification and recommendation. Also restructuring families and predicting and recommending kits would be a good use of AI in an ERP

Choosing the suitable strategy entails selecting algorithms and approaches that are compatible with the problem's complexity, data characteristics, and intended outcomes. Tailoring the strategy to the individual BI scenario improves model performance and relevance.

In my experience, choosing the right approach is vital when designing AI models for Business Intelligence. Determine whether a supervised, unsupervised, or semi-supervised learning approach is most suitable for your BI objectives. Consider factors such as available data, desired outcomes, and computational resources. By selecting the appropriate approach, you can develop AI models that effectively analyze data, uncover insights, and support informed decision-making within your organization's BI framework.

Na minha opinião, esta etapa é sobre como organizar os dados, escolher o modelo certo e definir como os dados serão processados e analisados. É crucial considerar a escalabilidade e a eficiência. Em BI, um data warehouse bem estruturado é fundamental!

In my experience, the architecture of an AI model plays a crucial role. It should be designed based on the selected approach and specific requirements, such as using neural networks for supervised learning or k-means for unsupervised tasks. Additionally, it determines the way data is processed and output is generated. To come up with an effective design, several factors need consideration including scalability, performance, security, and integration with BI tools.

To my experience this would be very crucial step. In order to achieve automation sometimes primary task of value chain is lost. Adding perspective is like defining North Star for your entire process

Creating a strong architecture requires organising data pipelines, feature engineering procedures, and model components to maximise efficiency and scalability. Consider data sources, processing capabilities, and integration needs while developing a dependable AI model for BI.

Understand the problem, who is your target audience, carry out data mining, have a user-friendly UI, pay attention to security, database protection and regulatory compliance, incorporate machine learning algorithms that a will continuously improve performance and grow the database and finally consider a mesh topology optimisation network that will ensure that the system is always up and running with minimal downtime.

Na minha opinião, aqui é onde a mágica acontece. Treinar o modelo com dados de qualidade e depois testá-lo para garantir sua precisão é vital. É um processo iterativo, onde ajustamos e refinamos o modelo. Uso de métricas como precisão, recall, e AUC-ROC são comuns, dependendo do cenário! Fica essas breves dicas! Valeu!

Os sistemas de IA terão o mesmo nível de qualidade dos dados utilizados para seu aprendizado, base de dados com vieses ou dados não curados irão levar a resultados indesejados ou distorcidos da realidade. Então é importante definir bem os processos de obtenção e preparação de dados que serão usados para treinar e ajustar modelos.

After the development of the solution, the next step will be training and testing the model. To do that, first, you have to create a set of test cases and their respective correct answers. the team needs to be agreed with these test cases, to test the exact scenarios where the solution will solve the problem. After approval, we will run the test cases and fine-tune the model accordingly.

Training and testing. The model's tasks include data preparation, training parameter selection, and model performance evaluation. Iterative testing and validation ensure that the model learns successfully from data and provides correct insights for business intelligence decision-making.

Training and testing the model are critical steps in designing AI models for Business Intelligence. Use quality data to train the model, ensuring it learns relevant patterns and relationships. Implement rigorous testing procedures to evaluate the model's performance, considering metrics like accuracy, precision, and recall. Iteratively refine the model based on testing results to enhance its effectiveness in generating valuable insights and predictions for BI purposes. This ensures reliable and accurate outcomes when deployed in real-world BI scenarios.

Na minha opinião, a implementação é apenas o começo. É crucial monitorar o modelo em produção para garantir que ele permaneça eficaz ao longo do tempo, adaptando-se às mudanças nos dados e nas condições do mercado. Fica a dica!

To deploy and monitor an AI model, package it for production, integrate it with existing systems, and implement monitoring tools to track performance metrics such as accuracy and latency. Set up alerts to detect anomalies and regularly update the model to maintain relevance and effectiveness in real-world scenarios.

Acredito que seja uma das fases mais importantes. A ausência de um acompanhamento adequado pode comprometer seriamente o sucesso do projeto. O monitoramento contínuo é vital para identificar falhas, ajustar estratégias e otimizar o desempenho do modelo de IA no ambiente de BI. Ignorar essa etapa pode resultar em respostas inadequadas, perda de insights valiosos e, consequentemente, prejudicar a eficácia do projeto como um todo.

Deploying and monitoring the model entails integrating it with BI systems, validating results, and constantly monitoring performance. Implementing feedback loops, tracking important metrics, and correcting model drift all help to guarantee that BI solutions continue to give value.

Deployment may vary as per the technology stack.. ex: Azure provides in house deployment through azure portal. Some key points to consider: -Containerize the model for easier deployment and scalability. -Choose a deployment platform (cloud, on-premises, edge). -Implement CI/CD pipelines for automated deployment. -Set up monitoring for model performance and health. -Establish logging for tracking usage and errors. -Create alerts for performance drops or operational issues. -Plan for regular model updates and retraining with new data. -Ensure compliance with data governance and security policies. -Provide user support and documentation for end-users.

Taking the results from AI and actually being able to connect t it to key business metrics visually is extremely important. Also always try to compare AI predicted vs Actual values so you know where the model works and when it does not, so you can identify how to include more parameters or alter the model to make it better.

I think we should not just get stuck in the mindset of getting fed business requirements and go from there. To do proper BI using AI, we need to have a gut feeling of how much the costs of doing so, the likely time requirement, the likely accuracy boundaries and associated business value. Business will need these technicalities for both short and long terms from the expert to create the complete business requirements. A collaboration.

At SAP, we design AI (Business AI) with our guiding principles of "Relevance, Reliability, and Responsibility". Here are 3 key themes: 1. Data: AI is a DATA problem at its core, with business context intact. Implement processes and protocols to ensure the reliability of input data, and to maintain data integrity throughout the BI pipeline. 2. Transparent AI: Prioritize the development of AI models that are explainable; fostering trust and accountability. 3. Ethics: Embed responsible AI principles into the design and deployment of AI models; to mitigate biases and ensure equitable outcomes.

The evolution of AI has significantly transformed our approaches to various tasks, transcending its initial status as a mere buzzword to becoming an indispensable part of our daily lives. Despite its undeniable advantages, several intrinsic issues often accompany AI implementation. Privacy, stereotyping and inclusivity concerns tend to be sidelined in the pursuit of optimal results, heightened productivity, and efficiency. To foster more comprehensive outcomes, I advocate for prioritizing the responsible use of AI as a fundamental consideration in the development of any AI model for business intelligence. By placing ethical considerations at the forefront, we can maximize technological advancements without compromising key human values.

Let's keep an eye on the fundamentals. The BI tells you what has gone and what is going on in the business. The AI is used to give you predictions and prescriptions, as in predictive analytics and predictive analytics. BI is most used in descriptive analytics and diagnostic analytics. However, AI is used in BI tools, for instance, to automate data collection and clean data. The prominent business intelligence tools are Tableau and Power BI. While AI tools are numerous, the most famous packages used in AI are Tensorflow, Pytorch, and Scikit Learn.