Helping our clients solve their most complex problems
I want to analyse past behaviour and predict future trends to make better business decisions.
I need to modernize our BI and analytics capabilities using the latest machine learning and artificial intelligence technologies.
What it takes to successfully develop an Advanced Analytics competency
Accelerate traditional analytics with modern technology
Ease of Access
Use modern analytics solutions to connect to your data anywhere on any platform
Speed to Market
Build more intelligent analytics that require less time and effort
Building your Advanced Analytics Competency with Bitwise
Bringing niche experience in predictive analytics modeling and big data management to help you successfully build your Advanced Analytics competency
Proven Analytics Experts
Extensive experience developing innovative machine learning processes.
End-to-end Business Intelligence implementations including data warehouse design, report development and support, analytics, advanced analytics, and visualization.
We have the expertise, in-house built accelerators, industry partnerships, training and proven approach needed to help you succeed.
We align the business requirements, use case, platform identiﬁcation, tool evaluation and resource requirements needed to implement advanced analytics capabilities.
Bitwise Analytics Solutions
Bitwise offers a complete set of analytics solutions to modernize your decision-making capabilities leveraging cutting-edge technologies and proven methodologies.
Data Science consulting services, implementation roadmaps and technology migration & upgrade strategy
Enterprise Data Science solutions including end-to-end analytics implementation and analytics as a service
Big Data Solutions for big data explorative data analysis, big data analytics, and data visualization and analytics
Enterprise Analytics Maintenance and Support
Advantages of Bitwise Analytics Solutions
‘Bag of Models’ Approach to Machine Learning Deployment on Cloud
Traditionally, multiple models (like Decision Tree, Random Forest, Support Vector Machine, Logistic Regression) are trained and then compared to choose the best performing model. However, the best model selected on the basis of performance metrics (like accuracy, confusion matrix, Cohen’s Kappa, A/B Test, ROC Curve) after training and validation at development may not remain the best choice with the changing data pattern at production.
In order to get the best predictions as each batch of production runs with minimum production support, Bitwise proposes a ‘Bag of Models’ approach to machine learning deployment on cloud.
Gather, clean, combine, structure and organize data from different sources by using statistical data analysis techniques
Split dataset for Training and Validation model, train the model, tune hyper parameters of best performing model and calculate accuracy metrics
Predict result for future data using Champion Model
Select the best performing model as Champion by comparing accuracy metrics and others as Challenger models
Validate prediction result of previous execution and update the reference to point to champion model
Execute Bag of Models for latest predictions and store their results for validation. Then use champion model result for presentation
Champion Selection Framework
Prediction Run in Production Framework
Advantages of ‘Bag of Models’ Approach to Machine Learning Deployment
Most Common Use Cases
Use machine learning models on IoT device logs to predict the probability that a piece of equipment or any device fails in the near future.
Improving Marketing Conversion Rates
Improve marketing efficiency and increase user conversion rates using machine learning algorithms with historical data, clickstream, CRM and other data sets.