In order to develop a good AI model we need high quality training data. We build custom tools using domain specific knowledge that can help you annotate content to get high-quality training data with ease. In general, the quality of the AI Model grows significantly with the quality and the size of the data set.
Our tools make tagging very simple! We design our tools to fit right inside your pipeline, which makes data management extremely simple.
Our Tagging tools can augment the annotation with AI and traditional programming, to ensure we have all the necessary data in all categories.
Our tools Govern
Tagging/Labeling/Annotating can be very time consuming! Our tools can take the guesswork out of it, and track what is required. It will always have the data ready for Model Training.
In order to have a good AI Model, you need to start with good AI Architecture. We can help you navigate it safely, and making the right decisions here goes a long way. One size doesn’t fit all.
The most fundamental question in model building is determining what you would like to predict.
We favor Ensemble learning, with each model finding different patterns within the data to provide a more accurate solution. These techniques can improve performance, as they capture more trends, as well as reduce overfitting, as the final prediction is a consensus from many models. We use different techniques, depending upon the need: Bagging, Boosting & Stacking.
We can help you with all aspects of the AI System.
Once we have collected the necessary data for training, we can commence the Model Training. Depending upon the training data set and what we are trying to predict, we can choose either to training from scratch, or we can perform Transfer Learning. Transfer Learning will expedite the training process significantly. The training begins only after we identify the right model architecture.
In order to get a good AI Model, there may be significant training required. This can be very time consuming and mistakes can be very costly. We use specialized techniques depending upon the size of data, and constantly test the model along the way, making necessary adjustments as needed.
We look at a lot of aspects while training, and constantly monitor during the training process to ensure we get the desired results. Below are a few aspects that we monitor:
Once the best model is selected from the model training process, it needs to be integrated into the production environment to make inferences. Model Deployment is the most challenging process. Both the server and model need to be optimized to make the inference faster and reliable. Maintaining robust data pipelines are crucial to model deployment. Models needs to be compatible with the production environment, or it could lead to delaying the timeline of projects by days or weeks. Our experienced teams who deploy and maintain a number of deep learning models can seamlessly move models into production swiftly and efficiently.
Deployment depends on the application and is different for various projects. Some of the models needs to be deployed on mobile for real-time evaluation, while others need to be deployed on servers. Regardless of the use case, our team can handle it. Our team has served models in following cases:
Deployed model needs to be constantly monitored for degrade in performance over time due to changes in the data or for server issues. In order to tackle it we have custom processes in place to continuously monitor the processed data and alerts any discrepancies.