Robotica Machine Learning is committed to providing robust and scalable solutions for every customer. We know that each customer is different, which is why all of our solutions are too. Projects within the same organisation may or many not share technologies, infrastructure and processes; the business objectives may be very different and necessitate contrasting approaches and solutions. There is always a balance between speed to market, accuracy, flexibility, longevity and clarity of result. Our truly agile process enables us to discover how to apply that balance, seeking out and embracing changing requirements so as to deliver a well-fitting solution.
The idea has been around for aeons - start with the goal in mind. By drilling into the business objective and uncovering how best to see it used in a production setting, we have both the start and the destination available before we plan a route together.
Will the application benefit from batch processing or would real-time scoring be better suited? Does the data drift, will the system benefit from remodelling frequently? Can outliers be swallowed by a single prediction engine or do we need to evaluate for anomalies prior to scoring and potentially redirect individual rows to different systems? All of these questions can have a profound affect on the ideal modelling approach as well as the architecture, both of which then feed in to the deployment mechanism.
Our platform is available as a service in our cloud, in your cloud or on premise in your data centres, in which case no public internet connection is necessary. Amazon AWS, Microsoft Azure and Google Cloud Platform are all suitable cloud targets for modelling and scoring solutions.
If your end application has intermittent or no internet connection, such as sensor devices or laptops in the field, it may be necessary to host the scoring engine locally, offline. This consideration gives you greater control to predict anywhere, any time.
Robotica Machine Learning with DataRobot and Microsoft Azure Cognitive Services can automate across the entire ML life cycle:
- Gathering data from multiple sources
- Cleansing data to tidy missing values and correct dirty data
- Enrichment of fields to draw out supplementary information, such as credit score from a person’s identity
- Feature engineering to derive more pertinent information from fields, such as age or day of the week from a date
- Selecting the right algorithms from the hundreds available and applying it appropriately with sensible hyper-parameters
- Row partitioning to ensure an appropriate spread of outlying or unusual values and to prevent over-fitting
- Training models to find and learn from patterns hidden with the data
- Tuning models to refine their efficacy to the problem domain
- Ensembling discrete but related modelling steps into effective workflows
- Head-to-head model competitions to determine the balance of accuracy, spread and performance
- Detailed explanations of the predictive models for regulators and business stakeholders
- Comprehensible insights into the reasons behind predictions
- Bespoke deployment strategies to benefit the application of the predictive models
- Application and API integration
- Model monitoring and management
- Easy-to use
- Fast and accurate
- Replicable data science
- Auditable, transparent
Speak to Robotica Machine Learning to discover where automated machine learning can bring new opportunities and efficiencies to your organisation.