1. Data strategy, resources, and people
Let's discover now some more elements to enable AI-driven organizations, starting with data strategy.
2. Data strategy and governance
Data strategy refers to the design and development of data-centric organizational strategies, including optimal information and knowledge extraction, and business decision-making.
Building a data strategy implies setting data-oriented objectives, identifying necessary data, its origins, and types, performing predictive and prescriptive analysis, and operationalizing processes around data.
3. AI infrastructure
Cloud-based infrastructure platforms are the most popular type of AI infrastructure in organizations today.
They provide scalable resources for computing, data storage, AI development tools, and pre-built ML models for various applications. Everything is located in their data centers, and their service is typically elastic, allowing organizations to scale needed resources up or down based on demand.
The alternative to cloud-based infrastructure is an on-premise infrastructure, where organizations have their own hardware, software, data storage, and networking resources to support their AI operations.
There are trade offs to both approaches with more control over data and governance with on-premise infrastructures compared to the ability to flexibly scale with cloud services.
4. MLOps methodology
Another important aspect of data strategy is the methodology used to build and operationalize AI systems. This is where MLOps (Machine Learning Operations) comes in the spotlight.
MLOps is a methodology to efficiently manage and operate ML systems in enterprise environments: from inception to deployment to maintenance. Despite its name, MLOps is applicable to the whole spectrum of AI development, not only ML.
5. MLOps methodology
As you can observe, MLOps is a pretty elaborate methodology with well-defined stages. Moreover, MLOps is cyclic and focused on continuous improvement of the AI solution through iterations of model development, deployment, monitoring, and refinement.
6. AI-related roles
To become an AI-driven organization, another imperative is to hire or cultivate some crucial roles. They work collaboratively to drive the implementation of AI initiatives and contribute to the successful adoption of AI technologies.
Some role names you should familiarize yourself with, are:
AI architect,
Data scientist,
Machine learning and data engineer,
and other roles, such as AI ethicists and project managers.
We'll describe what these roles mean shortly.
7. Building your AI team
An ideal AI team should cover three axes: leadership and management, execution, and support.
The AI lead and project managers are responsible for team and project leadership. For small AI departments, these roles can be unified into one, but when scaling up to several AI projects, it's best to have an AI lead who acts as the interface with business teams and stakeholders, along with one or several project leaders.
For execution, the goal is to have a balanced combination of AI architects, data scientists, and ML and data engineers.
- AI architects oversee the AI solution architecture, making decisions like selecting the right tools to use.
- Data scientists analyze complex datasets, prepare the data, train and evaluate ML models, and analyze model outputs.
- ML engineers focus on deploying implemented models into production and managing the AI system infrastructure, and data engineers specialize in building robust data processing pipelines.
This blended team is the ideal recipe to successfully cover the MLOps lifecycle discussed earlier.
8. Building your AI team
Lastly, AI ethics specialists and application domain experts are fundamental pieces in supporting successful and responsible AI solutions for our organization.
9. Let's practice!
Time to consolidate these ideas with a dose of practice!