Machine Learning Solutioning Services
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.
Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.
The Machine Learning journey
There are typically ten clearly defined steps in implementing a ML solution, shown above. We offer three services to cover every step of the machine learning journey: Cloud Discover: Machine Learning, Cloud Deploy ML for MVM, and Cloud Deploy ML for Production.
Cloud Discover: Machine Learning
Cloud Discover: Machine Learning is a 2 day workshop aimed to help your organization understand machine learning (ML) concepts as well as identify and qualify potential business problems that can be addressed using ML. This service is ideal for determining if ML is right for your business and which use cases are realistic and achievable.
Cloud Deploy ML for MVM
Cloud Deploy ML for MVM (minimum viable model) helps you take the use case identified in Cloud Discover from theoretical to practical by developing an ML model. This model will prove the value of the use case and its ML solution to stakeholders prior to investing more in the next phase of build-out.
Cloud Deploy ML for Production
Once an MVM is identified, the next step is getting it fully integrated with your production system. Cloud Deploy ML for Production deal with deploying your model into a production environment (via API for interactive use, or with support for batch execution), successfully building a data pipeline to re-train periodically your model, and addressing any service level objective your model need to comply to.