Your step-by-step journey toward impactful AI solutions
Modern businesses attempt to accelerate their digital transformation efforts by building data-driven solutions to enable higher degrees of automation, cut costs and open new revenue streams. These efforts lead to a widespread adoption of Artificial Intelligence (AI) efforts across all business units.
However, managing the realities of the process of adopting AI solutions is easier said than done. Applying a phased approach to AI adoption helps create business value from the earliest stages and increases exponentially as you advance.
At the core of impactful AI solutions is a purpose-driven and sound data foundation, developed to fit your individual business needs and strategy.
Step 1 – Assess
The first step on the journey toward impactful AI solutions is the Opportunity Assessment. The goal is to identify together with you, as a customer, the most pressing pain points and business problems, then prioritize them. Based on a selected business problem, we assess the availability and quality of the data to ensure the feasibility of an AI solution. Once the feasibility is verified, we conceptualize the solution in more detail and further expand the feature roadmap of your AI solution.
Moreover, we also provide you support with assessing and designing the data infrastructure required for the development and deployment of these AI capabilities.
Step 2 – Explore
In the second stage, we perform an extensive Exploratory Data Analysis (EDA) on the relevant data. We analyze and provide statistical summaries that help us gain a thorough understanding of the data characteristics, as well as how well the data reflects the reality of the underlying business process. The aim of this step is to develop an AI solution prototype that validates our initial hypotheses. If the resulting model meets the performance expectations of the customer, we can move forward to the next step.
Step 3 – Develop
The objective is to further develop the resulting AI solution prototype from the previous step into a production-ready MVP. This means implementing the data pre-processing, feature engineering, model training and inference pipelines, while applying software engineering best practices and ensuring code, data and model correctness.
Step 4 – Deploy
Once the production-ready AI solution is developed, the next natural step is to deploy it into a production environment. This environment could be either configured on a pre-existing infrastructure, enhanced to fit the requirements of the AI solution, or be an entirely new set-up specifically designed and implemented as a Data and ModelOps Platform. As mentioned earlier, we are also here to support you in developing and establishing this foundation, which will help you continuously speed up future model deployments and gain a competitive edge.
The deployment of the AI solution to a production environment ensures continuous business value generation, which is why it was developed in the first place.
The journey, however, does not stop after deployment.
Step 5 – Manage
To ensure that the deployed AI solution stays relevant over time and is not affected by issues such as concept drift or training/serving skew, we further enhance your Data and ModelOps Platform with model management capabilities. This aims at automating model monitoring and management toward timely identification and notification of incorrect results, as well as automatic handling of these issues (e.g. model re-training and evaluation).
By automating these capabilities, you can avoid downtime and ensure that the end-users are continuously provided with relevant results.
The framework presented above represents our best practice approach toward delivering impactful AI solutions. It is the result of our many years of experience with hundreds of AI projects.
Interested in learning more or want to share your experience with AI projects?
Contact us today!