Elevate your business with cutting-edge Machine Learning projects
Machine Learning solutions
We tailor our AI solutions to fit your unique business needs, from data automation to model development and operationalization. Our team works closely with you to understand your goals and develop machine learning models that drive real-world results. Whether you're aiming to streamline processes, personalize customer interactions, or gain deeper insights into your operations or your competition, we’re here to bring cutting-edge AI technologies to your fingertips.
Our delivery approach
Data-driven solutions, particularly those involving machine learning, have an intrinsic element of uncertainty involved. The best way to mitigate this risk is by following a standardized delivery approach, where we can assess at every step the expected business value being delivered.
01. Problem Definition
Initial problem understanding, identify your business goals/objectives and determine the key business indicators used to determine success.
02. Proof of Concept (PoC)
Evaluate if the given problem can be effectively solved using machine learning with the data at hand. Identify success criteria for PoC.
03. Feasibility Check
The current basic model meets the minimum required benchmarks for machine learning and system performance. Building on the insights gained up to this point, we will establish the project's scope, set clear objectives, outline the high-level architecture, define the criteria for completion, and develop a comprehensive plan for the project's entirety.
04. Model Engineering
Leverage engineering best practices to develop and operationalize the selected models. Use rapid model/data exploration to validate results and provide feedback to fine tune the models.
05. Model Operationalization
Prepare production grade code for deployment, through CI/CD pipelines. Integration testing to ensure production code behaves in the way it is expected and that results match those in the Model Engineering phase. Deployment of all production artifacts to production environment. Setup of logging and monitoring tools to ensure model is working correctly in production.
Our team unit
At Stokedge, our AI Pod is designed to deliver comprehensive and cutting-edge AI solutions tailored to your business needs. Each Pod is a self-sufficient unit with a diverse team of experts who work collaboratively to bring your AI projects to life. Our unique Pod structure ensures that we provide high-quality, efficient, and secure machine learning solutions, driving tangible results for your business.
Meet the team
5. DevSecOps Engineer (Infra): Our DevSecOps Engineers focus on infrastructure architecture and implementation, model deployment, security, and monitoring. They guarantee that the AI solutions are deployed securely and efficiently, with minimal downtime.
6. Domain Expert (1-4): Domain Experts provide specialized knowledge in specific verticals or functional domains, such as industrial production, forex trading, or retail pricing. They may also offer expertise in cross-cutting technical domains like software engineering or UI/UX design. These experts ensure that our solutions are tailored to the specific needs and challenges of the industry or function.
1. Pod Lead: The Pod Lead is responsible for project management and acts as the single point of contact (SPOC) with the client's management team. They ensure that the project stays on track, all team members are aligned with the goals, and communication with the client is seamless.
2. Data Scientist: Our Data Scientists focus on algorithm selection, model testing, and feature engineering. They bring the theoretical and practical expertise needed to choose the right models and refine them for optimal performance.
3. Data Engineer: Data Engineers handle data collection and integration, information architecture, quality checks, bias checks, and data policy enforcement. They ensure that the data pipeline is robust, reliable, and compliant with relevant policies.
4. ML/AI Engineer: ML/AI Engineers are tasked with the software engineering aspects of the models, including production systems, continuous integration/continuous deployment (CI/CD) pipelines, and monitoring integration. They ensure that the AI solutions are scalable and maintainable.