Problem understanding
We will define the objectives and methods for needs analysis and guide you through gathering requirements, including competition benchmarking and proof of concept. A kick-off meeting will review project details, ensuring a focused and efficient design process. Clear needs analysis simplifies meeting project challenges.
Get the data
In this phase, we use statistical and AI techniques to build data models, identify patterns, and extract insights. We address data quality, select and acquire data, manage missing values, and utilize Python libraries like Pandas and BeautifulSoup.
Explore the data
The data preparation phase involves refining the raw data into a final dataset, including tasks like attribute selection, data cleaning, and transformation for modeling. We also define business process integration, data integration strategies, and design the machine learning approach. Python libraries used include Matplotlib, Numpy, Scipy, and Pandas.
Development - Training model
Our team’s expertise supports you in the industrialization of your algorithms. We assist with validating algorithms on real cases, quality control of code, integration with design tools, deployment, and ongoing maintenance. We also contribute to feature engineering, model development, and data platform management. Python libraries include scikit-learn and scikit-network.
Tests and evaluation
It’s crucial to evaluate the model to ensure it meets project objectives. This phase includes reviewing the model’s performance and deciding on the use of data mining results. Automated validation compares the new model’s performance with previous versions before production.
Deployment
Creating the model is just the beginning. The knowledge gained must be organized and presented for client use. Deployment can range from generating a report to implementing a full data mining process. Early planning is crucial for using the models effectively. Continuous delivery (CD) ensures the trained model is deployed as a prediction service, with automated updates and production data flows. Tools include TensorFlow, MLflow, and Docker.
Development of document
After project execution, it's time to put it into operation. Once verified and validated, the project is closed with comprehensive documentation. We provide complete software documentation upon request, including specification documents for developers/testers or user manuals for end users.
Continous improvment
The stable model from the previous phase is then delivered to production. During this phase, we apply testing, monitoring, release management, automation, continuous deployment, and governance to the machine learning model. Progress is tracked and reported against goals, insights are mined from the model, and we experiment with improvements while governing and managing data quality.