Our ML Engineers start by analyzing your business needs and evaluating the existing tech infrastructure. Then they identify the data sources, define the project's scope, and create a wireframe of objectives, milestones, deliverables, and timeframe.
Depending on the findings in the previous stage, our ML developers create a solution architecture, analyze the precise ML methodology, and select the best tech stack. They also consider the possible challenges and find ways to overcome them.
Here, our ML engineers clean, annotate and transform the data to feed into ML models. At this stage, our ML developers finalize the design and architecture of the ML model they will be building.
We develop ML models. We feed data to the ML models that were kept ready in the previous stage. We test the model and iterate wherever and whenever necessary to ensure the ML model performs at the highest degree of accuracy.
Our ML engineers then proceed further with appropriate ML algorithms to train the ML model and ensure it learns from supervised and unsupervised ML techniques. We train the ML models until the results are satisfactory.
After testing the performance of ML models, our ML engineers wrap the source code of the ML model to integrate it into the existing solution and publish it. Once the model is deployed, we test it again and iterate it if necessary.
We continue to monitor and enhance the solution by upgrading it regularly and adding new features. We keep adding new data sets, test the model's performance and provide ongoing support.
