In general a Project Manager in Technology is tasked with planning and leading all end-to-end activities of delivering projects for implementing technology-based solutions within a corporation. In doing so the Project Manager is required to ensure at the start that both the scope of the solution as well as the methodology of delivering the project are properly defined and agreed upon. There needs to be an allocated budget covering all the costs of the project and a planned target timeline of delivery. Successful projects meet the objectives of being on time, within budget and meeting expected delivery outcomes which are usually pre-defined as acceptance criteria for the various items of scope.

Unfortunately Machine Learning Projects today tend to be ill-defined in terms of scope and methodology. They often run over time and budget and may not produce expected outcomes. These syndromes are not uncommon in a new discipline.

In the following sections we address these gaps by defining a 6 step roadmap of project delivery to successfully plan, manage and deliver your Machine Learning project. Project and Program Managers and Delivery Executives will all benefit from reading this Blog.

Project Delivery Roadmap

As a first step to your delivery roadmap: Identify the stakeholders and the objectives (Project Charter)

As the second step on your delivery roadmap: Identify the scope of the project.  

As the third step in your delivery roadmap: Identify the delivery methodology: procedure/steps, phases, sequence of activities, etc

As the fourth step in your delivery roadmap: Identify the key artefacts, deliverables and their inter-dependencies.

As the fifth step in your delivery roadmap: prepare a Detailed Plan in accordance with the delivery methodology you have previously identified.

Important!: while preparing your plan of delivery apply project management standard best practices

As the sixth and final step of the roadmap: Deliver your Project according to your Plan and your chosen Best Practice Standards.

In conclusion it is extremely important for a Project Manager to adhere to the  recommended approach that we have described in this Blog in order to ensure successful delivery outcomes for a machine learning project. Deviations from our recommendations (which are intended to constitute best practice guidelines for this domain) could significantly reduce the probability of project success.

For more details on the above roadmap and a methodology that really works (for the third step in the above roadmap), we can provide consulting advice: you may write to us at Turing-point

Resourcing: Essential Roles

Not only does confusion regularly occur regarding AI/ML project delivery methodology, there is often also much uncertainty regarding resourcing and skillsets. We name below the roles which are essential for delivering AI/ML projects for a corporation. As an AI/ML project is also an IT project it should be noted that a few of these roles do not necessarily require advanced specialized knowledge/skills of AI, however they are essential for contributing to the successful overall delivery of an enterprise AI solution.

ROLE: AI Product Owner

ROLE: Senior AI Project Manager/Scrum Master

ROLE: Senior AI Business Analyst

ROLE: Senior AI Solution Architect

ROLE: AI Data Engineer

ROLE: Machine Learning Specialist

ROLE: AI Support Programmer

ROLE: AI Consultant Analyst

ROLE: AI Infrastructure/Cloud Engineer

ROLE: AI Security and Network Engineer

It needs to be also noted that

• Depending on the size of the project it may be necessary to engage more than one resource with a particular skillset.
• It is possible that other roles such as UX Designer, QA/Test Manager, Tester, OCM, etc will be required to support a significant AI-driven business transformation project for a large organization. Of these OCM can play a major role in providing effective communication to address issues of paranoia, concern and resistance which often arise with regard to AI impacting people’s careers within an organization.
• While collecting and preparing data for an AI project it will be necessary to consult various Business SMEs within the organization.

AI/ML project teams are therefore cross-functional in composition.

For more details of each of the above roles including their skill sets, responsibilities and the work outcomes they need to produce we can provide consulting guidance: to request for such help please write to us at Turing-point.