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Conduct Data Assessment
Exploratory Data Analysis (EDA) combines powerful technology with our team’s expertise to provide the most comprehensive review of your data. With the power to assess any type of data including images, text, and tabular, EDA pulls insights that are then layered with a custom analysis conducted by the Infinia ML team.
Confirm Data Readiness
Data must meet the following criteria before it can be used for machine learning:
Clean + Process Data
After meeting the requirements for ML, formatting must take place so that the model can easily review and learn from the data. Once the data is “clean”, it’s then time for it to be processed where it will be prepared to run through the algorithm.
Build Data Pipelines
In order to ensure long-term success, a “pipeline” to funnel the data through the ML model must be created so your solution can improve over time. This typically involves help from your IT department to ensure that data is running smoothly and securely.
Infinia ML leverages our algorithm library ML to expedite the development of your machine learning model.
Sourcing from an ever-growing pool of pre-built tried and true code, the Infinia ML team assesses your EDA report, selects an algorithm, and then customizes it to fit the specific needs of your data.
Our ever-growing algorithm library enables us to create solutions that span across multiple data types and problem classes to deliver you the best possible business value.
After designing an algorithm to meet the specific needs of the business challenge, the Infinia ML team will then create a model that will be used to train your data.
With the data running through the model, our team will be able to fine-tune the parameters based on the model’s output.
After testing the results and seeing the initial output, the outcomes that the model produces can be advanced in order to get the most business value out of the ML solution.
Package Solution as Software
Infinia ML delivers a container that packages a trained ML model, an API to connect to your system, and methods to monitor your solution’s performance.
Infinia ML uses Kubernetes to make it easier to connect with your IT organization so that your ML solution can be integrated and scaled seamlessly. Kubernetes also makes it possible to deploy additional services like our Model Care Unit so you can monitor your model and ensure it continues to achieve peak performance.
Your solution is cloud-agnostic which means it can deploy packages on AWS, Google, Azure, and on-prem.
Integrate with Legacy Systems
In order for you to gain the full value of your ML model, you need to ensure that it is receiving the latest data. Successfully integrating with your legacy systems, allows the model access data and output results that are accessible to your organization.
Release to Users
Ensuring that your users can access the solution on their platforms is crucial in order for your company to get the most out of machine learning. It’s equally important for your team to understand the results and how to use them to create value for the business.
Upon encountering real-world data outside of the training set, models may begin to deliver lower accuracy and drift to a lower state of performance. Thus, more than traditional software, machine learning implementations require constant oversight.
Infinia ML created the Model Care Unit to constantly monitor each model, notifying our team and your team about any potential data input or output issues that need to be addressed.
After ensuring the results align with performance expectations, it is time to let the model function within your organization. This allows both your company to become fully on-boarded with the change while also giving your team time to report on the impacts that the solution is having on the organization.
Monitor & Audit
The model will be constantly monitored by the MCU and the Infinia ML team. In order to maximize the business value of the solution, the business impact will be audited to confirm it is staying consistent overtime.
Reassess & Retrain
If an audit identifies a lag in performance, then the model can be retrained in order to adapt to changes in your data and your business.