Unparalleled growth in digital data combined with easy accessibility as well as affordability of up and coming technologies is enabling enterprises to explore machine learning services and solutions in order to overcome critical business challenges.
As a machine learning solutions provider, we enable rapid decision making, increased productivity, business process automation, and faster anomaly detection by using a myriad of techniques such as mathematical optimization, pattern recognition, computational intelligence and more.
Deep learning is the bedrock of high-level synthetic intelligence. While machine learning focuses on available data and known properties, deep learning uses a layered approach of artificial neural networks to discover scalable solutions through predictive and prescriptive analysis. The model essentially learns, interacts, and performs complex tasks without human intervention.
This method relies on the training of datasets to learn functions from inputs and meet the desired output values through methods like regression, classification and prediction. Multiple iterations ensure efficient mapping and accurate predictions of business outcomes. Yield superior results from our guided learning models, from spam filtering to improved products, meaningful insights, quick decision-making, risk analysis, and more.
The reinforcement learning model focuses on determining actions that can optimize performance and yield the best reward over time. This technique uses experimentative training to figure out how to achieve optimal results in a given environment and stay ahead of disruption. Its dynamic applications span the fields of navigation, robotics, gaming, telecommunications, and more.
Is your business ready for machine learning yet? Let’s find out! Our machine learning consultants help you identify business challenges to resolve and find functional solutions by following the 7 step approach to implementing machine learning solutions.
Our team of consultants and data scientists take on the preliminary work of evaluating your business objectives and determining the relevant solutions to the problems that are posed. Based on the outlined goals, qualitative and quantitative data is extracted for analysis.
Prepare Data for Analysis
Raw data requires a lot of preprocessing to make it usable and efficient. We clean, normalize, label, classify the collected data and eliminate the unusable parts. Pertinent visualizations are prepared to examine its scope and uncover hidden connections.
Transform the Data
This is the consolidation stage of data processing, where the data is transformed into forms appropriate for mining and getting intelligent insights. The data is simplified by normalization, attribute decomposition and aggregated into understandable categories to make it uniform.
Data splitting focuses on 3 main subsets: training, testing, and validation. Training data is a learning sample for the model, test data ensures performance improvement, and validation data equips the model for unforeseen tasks. This process builds a robust and reliable model.
At this stage, the transformed training data is used to create multiple algorithm models. Depending on the desired outcomes of the task at hand, supervised or unsupervised learning method is applied for experimentative analysis using set parameters.
Test and Validate Models
The created models are now put to the test to check for the best results. Cross-validation and ensembling techniques are used to scale speed, accuracy, efficiency, and performance. The goal is to tune the algorithm and develop a successfully optimized model.
By this stage, we have a production-grade model ready for deployment. For optimum performance and smooth integration, A/B testing and modifications are implemented. The model is now ready to make inferences.