Data strategy, resources, and people

Understanding Artificial Intelligence

Maarten Van den Broeck

Senior Content Developer at DataCamp

Data strategy and governance

Data strategy: design and development of data-centric approaches for information extraction and business decision-making

Data strategy steps:

Setting data-oriented objectives
Find out necessary data
Determine data sources and types
Predictive and prescriptive analysis
Operationalize data-driven processes

Setting data-oriented objectives

Find out data needed

Determine data sources and types

Predictive and prescriptive analysis

Operationalize data-driven processes

AI infrastructure

Cloud-based AI infrastructure

Scalable computing resources, data storage, AI tools and pre-built models
Elastic, on-demand

Google Cloud, Azure and AWS

Pros: high scalability, cost-effectiveness
Cons: data location, Internet needed

On premises (self-hosted) AI infrastructure

Organizations own their hardware software, data, and network resources to support AI operations

On premises infrastructure

Pros: enhanced data control, lower latency
Cons: upfront costs, limited scalability

MLOps methodology

Machine Learning Operations (MLOps): efficient and reliable management and operation of ML (AI) systems in the enterprise

MLOps methodology

Machine Learning Operations (MLOps): efficient and reliable management and operation of ML (AI) systems in the enterprise

MLOps methodology steps

AI-related roles

AI Architect

AI architect

Data Scientist

Data scientist

Machine Learning and Data Engineer

Machine Learning Engineer

Others: AI Ethicist, Project Manager

Other AI roles

Building your AI team

Leadership and management

AI manager / team lead
AI project manager(s)

Execution

AI architects: selecting the tools
Data scientists: analyze data and train and evaluate models
ML & data engineers: deploy models intro production and building data pipelines

Support

Building a team

Building your AI team

Leadership and management

AI manager / team lead
AI project manager(s)

Execution

AI architects: selecting the tools
Data scientists: analyze data and train and evaluate models
ML & data engineers: deploy models intro production and building data pipelines

Support: AI ethicist; domain experts

Building a team

Let's practice!

Understanding Artificial Intelligence