•  Establish clear objectives

The initial stage of our project encompasses consistent and strategic communication with the client. This crucial phase aims to establish clear objectives and define the ultimate deliverables of the undertaking. For projects involving industrial systems optimization, this stage is particularly vital. It involves close collaboration with the client to understand their specific processes, identify bottlenecks, and establish optimization targets. Additionally, this phase may include data collection from various sources and systems to enrich the data with more dimensions.

  • Gather valuable inputs and requirements

Following the initial stage, it is essential to collaborate with the clients to brainstorm ideas and gather valuable inputs and requirements. These meetings serve as a platform to leverage the client’s expertise and feedback. The objective is to determine the best data sources and algorithms to achieve our defined goals. For projects that incorporate neural networks and deep learning, these discussions become pivotal as we explore the potential applications of advanced machine learning techniques and algorithms. We aim to harness the power of neural networks to model complex relationships within the data, enabling more accurate predictions and optimizations.

  •  Data analysis phase

Subsequently, we move into the data analysis phase. Here, we perform a meticulous examination of the current data and its semantics. In cases where gaps exist in the existing data or sources, we compile a list of recommended data to collect and specify its desired format. Once we confirm that the data and data sources meet our requirements, we proceed to the determination of the algorithms. It is essential that data is formatted in an appropriate way to be used in model creation. Depending on the nature of the optimization task, we may use regression or classification mechanisms.

Regression comes into play when predicting continuous variables like production output, while classification is used for tasks such as choosing the right class of certain items.

  • Preparing the data for modelling

In client discussions, we present one or more algorithms for training and testing. Following this, we transition into the “Preparing the data for modelling stage”. During this phase, we conduct data cleaning, anonymization, transformation, and data integration with external sources when necessary.

For machine learning projects, this phase serves as a key step in which you prepare data for further analysis.

An example of a commonly used technique during this stage is Anomaly Detection Algorithm. This technique is used in data modelling and pre-processing to identify unusual patterns or outliers in the data.  (It uses the statistical and machine learning methods to identify unusual patterns or outliers, ensuring the integrity and reliability of systems.)

  • Train and validate the model

Once the data is preprocessed, its time to move forward to train and validate the model using a dedicated test dataset. At this point it is essential to adjust model parameters, commonly referred to as hyperparameters, to achieve optimal results. For projects involving computer vision, such as image recognition and processing, this phase may incorporate convolutional neural networks (CNNs) and deep learning architectures designed to handle visual data.

  • Verify the model’s performance on production data

Following model training and validation with the test data, the next step is to verify the model’s performance on production data. During this verification phase, we prioritise split testing to ensure unbiased results, ensuring that the model performs effectively in real-world scenarios.

  • Deployment and integration

Upon successful model verification, we proceed to deployment and integration. In the support phase that follows, we continually fine-tune hyperparameters to adapt to evolving circumstances. This ongoing process ensures that the model remains accurate and adaptive, delivering value to the client.

There are different use cases where above mentioned process can be applied:

For projects involving expert systems, rule-based systems and knowledge representation (Medical Diagnosis System, Customer Support Chatbot, Educational Tutoring System, Legal Expert System, Agricultural Decision Support, Inventory Management, Energy Management, Personal Finance Advisor, Environmental Monitoring) are utilised in the model verification phase to ensure that the model’s decisions align with expert domain knowledge.

Similarly, in fraud detection projects, continuous monitoring and algorithm updates are essential to stay ahead of emerging fraud patterns.

For chat bots, the support phase involves ongoing natural language processing (NLP) model tuning and response optimization to enhance user interactions over time.

In scenarios with multi-dimensional optimization challenges (Financial Services Optimization and Automation, Operations Research and Industrial Optimization, Engineering Design and Simulation, Network Design and Routing, Aerospace and Rocket Design), the support phase focuses on dynamic optimization algorithms. These algorithms adapt to changing parameters and constraints, ensuring that the system continues to perform optimally as conditions evolve.

In conclusion, our comprehensive approach to data analysis and model development spans a wide range of applications, including industrial systems optimization, regression and classification challenges, fraud detection, image recognition and classification, chat bots, and multi-dimensional optimization challenges. Described process guarantees that our models remain effective and adaptable in dynamic real-world scenarios.