AI Execution
Building AI solutions for business
We build AI-powered solutions from scratch or integrate into your system with existing AI tech.
At this juncture, we are ready to put strategies and plans into action. Equipped with the right team and technology, let us kick off the executing phase.
Data Extraction (ETL) & Centralization
At the outset of AI execution, consolidating clean data for AI and machine learning is necessary to ensure the accuracy and performance of your models. This involves loading datasets and discerning patterns, thereby laying the groundwork for AI model training and validation. Depending on the ML model, the process encompasses structuring the data through the encoding of categorical variables, addressing missing data issues and normalizing numerical inputs. Employing robust data storage and processing tools is essential for handling substantial datasets and facilitating real-time data processing, which nourishes your AI and ML models.
- Outcome: Data loaded/hosted in a format ready for AI/ML models
ML Models
During the Strategy phase, your AI expert(s) assessed various models and solutions to address your challenges. Now, it’s time to initiate the development process by using the appropriate algorithms and libraries/stacks. Selecting the optimal technology stack is crucial for successful AI adoption, considering factors such as data attributes and available computational resources.
The landscape of AI/ML models is diverse, ranging from linear regression for continuous outcomes to convolutional neural networks (CNNs) for image processing tasks. There are several platform/tools options available, including Scikit, Tensor Flow and Azure/AWS AI platforms. For popular business use cases and their corresponding AI technical stacks, please refer to Table 1.
Once the AI/ML models are developed using the appropriate stack, your engineers will commence training and testing them. Typically, the available data is divided into training and testing sets. The models are trained using the training data sets and tested using the testing data set. Fine-tuning activities are then performed to optimize the models further. These activities involve adjusting hyperparameters for better performance and iteratively improving the models for enhanced reliability. Once your model is ready, it’s time to integrate it into existing systems or deploy it to a separate interface.
- Outcome: Fine-tuned AI model
Application Interface Development/Integration
AI implementation requires a user interface that connects customers and employees with new solutions. Whether integrating AI functionality into your existing systems, such as ERP, CRM, E-commerce, websites, etc. or deploying it to a separate interface such as chatbots, the focus should remain on data, user experience and business objectives.
An intuitive front end must be crafted to enable machine learning models to serve business ends effectively. This integration often entails API development to ensure seamless communication. Collaboration with DevOps is vital for aligning AI deployment with overarching infrastructure management practices, thus enhancing efficiency and deriving maximum value.
- Outcome: Integrated AI application - ready to use!
