Roles and Skills Required for Successful AI Implementation

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Building a Strong AI Implementation Team for Successful Transformation

As part of an AI transformation initiative, one of the essential things that we have to do, irrespective of our role, is identify a team that can help us with the overall transformation and deploy the AI prototype as part of the launch phase. Having a highly skilled, capable team is one of the key determinants of a successful AI transformation.

Assessing the Impact of Cloud Migration on Workforce Talent and Job Roles: A Focus on Innovation and AI Applications

As part of the discovery phase, we need to assess, as part of building a talented workforce, how the cloud can free the current staff to focus on innovation and how their job roles will change when the ML platform and AI applications are migrated to the cloud.

Unlocking the Potential of Cloud: Empowering Teams to Drive Innovation and Transformation

Because the cloud brings the benefits of speed, scale, productivity, and innovation, the team will now be able to focus on larger, more impactful, and innovative transformational efforts in the future. Speed comes from the ability to experiment with multiple models, and scale comes from the ability to spin up and down the infrastructure in response to customer demand. Scale leads to the ability to serve a broader set of customer segments, geographies, and channels. Adoption of infrastructure as code, code deployment pipelines, and security as code enables better compliance, and results in higher productivity from automation. Proper training should be imparted to the current employees to prepare them for the new world of AI.

Cloud Migration and the Evolution of IT Roles: Redesigning On-premises Positions for the Cloud Era

Some current on-premises roles include IT Solutions Architect, System Admin, Network Administrator, Desktop Administrator. Applications Administrator and Database Administrator would likely have to be redesigned. For example, the system administrator previously responsible for managing the onsite hardware and infrastructure would now be relieved of some of these responsibilities as the cloud provider now manages them, be they AWS, GCP, or Azure. 

Mitigating the Risk of AI Initiative Failure: A Focus on Core Team Skills and Responsibilities

Most AI initiatives have a higher risk of failing if they don't have the right skills. Let’s now review the new roles in the cloud-based AI scenario. Let's first look at the core team and then the extended teams later. It should be noted that the actual job roles and responsibilities will vary based on the company, the project goals, and other factors appropriate for a given initiative. Like we discussed before, people domain is one of the four domains in a transformation effort, the other three being business, process and technology domains.

The AI Transformation Architect: Bridging the Gap Between Business and Technical Teams for Successful AI Initiatives

This is a new, emerging role that's primarily focused on defining the transformational architecture that AI introduces. We can think of them as the glue between the business stakeholders, the data engineers, the data scientists, the machine learning engineers, DevOps, DataOps, MLOps, and others involved in an AI initiative. They are responsible for pretty much everything. 

They are responsible for designing the overall AI strategy, – involves identifying the AI application opportunities, use cases as well as the underlying business and technical requirements to enable those use cases. They help design the AI architecture, select the right AI technologies , develop the AI roadmap, ensure the right data quality and security, define the overall framework for building and deploying AI applications, and ensure that the new AI technology is integrated with the existing business processes. 

The Crucial Role of the Data Scientist in Driving Business Success through Data Insights

At its core, the data scientist role is all about taking in data, refining it, and then running models with that data to draw insights to make business decisions. It is a critical role to ensure that the project is successful. They work very closely with the data engineer to refine the data, as well as the machine learning engineer to put the model into production. One of the first things this role has to do is define the problem statement by working with the business stakeholders. 

Enabling Data-driven Insights: The Essential Role of the Data Engineer in Delivering High-quality Data to Data Scientists

The data engineer works closely with the data scientist, and their role is to provide the data to the data scientist. Their primary role is to ensure that the data pipeline is appropriately implemented and works satisfactorily so that the models get the right data in the right format and quality. They focus primarily on data integration, modeling, optimization, quality, and self-service. 

Accelerating AI Innovation: The Key Role of the Machine Learning Engineer in Deploying Models for Business Impact

The machine learning engineer role is another crucial role for an AI project, and they are focused more on drawing insights from the models; the machine learning engineer is focused on deploying models into production based on the clean data from the data engineers. This role attempts to resolve one of the well known issues in machine learning projects, where the focus thus far has been at building a high performing model, but the teams failed to deploy it into production so it performs with scale and accuracy.

I hope this edition was informative and helps you with your AI initiative. Thanks, and have a great day!

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Here's to Enterprise AI Transformation, thanks!

Disclaimer: All opinions are my own. I am speaking for myself only and do not reflect the views of my employer.

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