With advancements in data science it is possible today to quickly and automatically produce machine learning (ML) models that can analyze higher volumes of more complex data and deliver faster, more accurate results – even on a large scale. By building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding previously unknown risks.
These technological advancements have led to the widespread interest and adoption of machine learning, which finds its application everywhere nowadays – in Natural Language Processing, Cybersecurity, Infrastructure Management, Healthcare Decision Support, Training and Education – the list goes on.
Yet, in spite of the technologies that already exist, many companies are faced with the need for specific ML tools that may not yet exist. We work alongside our customers to design and build those ML tools. In fact, First Line has formed an organizational structure, business processes and methodologies specifically focused on artificial intelligence and machine learning. The structure includes client experts who will have the opportunity to gain valuable knowledge by participating in agile team activities.
Organizing Engagements with Machine Learning
While machine learning may be a critical element of a system under development and require special skills in the engineering team, in most cases it is only one of many elements of the system overall.
Given that machine learning typically deals with a large amount of data, a typical project includes the following steps:
- Integration with external systems and data sources
- Data extraction and preprocessing
- Data profiling, cleaning and validation
- Preparing Docker images and learning infrastructure
- Data workflow orchestration and management
With the supporting data infrastructure in place, the ML-specific tasks may include:
- Definition of the dataset and supervision signals
- Manual feature engineering/automated feature detection
- Definition of the ML method (SVM, decision trees, NN, pre-trained models), technology and pre-trained models
- Definition of deep learning architecture (Convolutional, Recurrent, etc.)
- Definition of the success criteria, then experimenting and fine tuning the whole chain
It is highly likely that the system under development will include other, more traditional elements and components, such as user interface, business logic, and persistence layer.
Following the above implementation patterns, we structure the engagement and staff the teams accordingly: a balanced mix of data engineers, ML experts, and other developers working side by side with each other and with the client’s engineering organization.
A few of our representative engagements involving machine learning are described below.
First Line Software is a premier provider of custom software development, technology enablement services and value-add consulting in big data engineering, digitalization, intellectual integration, industrial Internet, and IoT, digital media and marketing, and enterprise content management as well as healthcare IT.
Headquartered in the US, First Line employs 500+ staﬀ globally. First Line team and company culture is centered around subject matter expertise, technical excellence, consulting capabilities and proven methodologies, with a strong focus on Agile and Intellectual Integration.
The company has been recognized with multiple annual rankings and awards by the International Association of Outsourcing Professionals (IAOP), Global Services, CorporateLiveWire, Insights Success and CNews. We were the first to be awarded the Scrum Capability Medallion by Scrum, Inc. Most recently, research firm Gartner included FirstLine in their first ever Market Guide for Technology Integrators (2014) and the Cool Vendor in Applications Services 2015 Report. We are active members in Object Management Group and Industrial Internet Consortium. FLS is also an EPiServer Premium Solutions Partner.