Charting the Course of Your ML Journey

The journey of transforming businesses in areas such as customer experience enhancement, operational efficiency, faster and better decision making, risk reduction, and new products and services with AI/ML is an exciting yet challenging endeavor that requires careful planning, execution, and ongoing management. Having a good understanding of what an AI/ML journey might look like and what the key challenges are will help ML practitioners and decision makers plan better throughout this journey. In this chapter, I will go over some of the essential topics for understanding the ML journey, such as the stages of adoption and the assessment of ML maturity. We will explore the various challenges, including developing an AI/ML vision, initiating AI/ML projects, and scaling use cases, infrastructure, and governance, to address the growing needs of the market.

Business and technology decision makers who are responsible for establishing an ML strategy...

ML adoption stages

The paths to adopting and maturing AI/ML can vary for organizations. As an ML solutions architect, I have collaborated with organizations at different stages of AI/ML adoption and with varying levels of ML experience. Understanding what organizations look like at different stages of the AI/ML journey can help decision-makers prioritize what’s important at each stage, identify the challenges an organization may face, and determine what needs to be done to move to the next level.

Based on my experience working with various organizations, I have observed that companies generally fall into the following stages.

Exploring AI/ML

Companies in this stage are those that are just beginning to delve into the world of AI/ML. They usually don’t have any prior experience with AI/ML, but they recognize its promising potential and are eager to explore its impact on their business.

These companies often face several challenges as they attempt to assess...

AI/ML maturity and assessment

To assess the level of an organization’s readiness to adopt ML at different stages, the concept of ML maturity is often used as a measure. ML maturity refers to the organization’s capability to implement ML successfully from multiple dimensions. At a high level, there are four key dimensions that can be considered when describing an organization’s ML maturity:

• Technical maturity: This refers to the technical expertise and capabilities of the organization in the domain of ML. Technical maturity can be measured in terms of the sophistication of ML algorithms and models used, the quality and availability of data, the scale and efficiency of ML infrastructure, and the ability of the organization to integrate ML with other systems and processes.
• Business maturity: This refers to the extent to which ML is integrated into the organization’s product development lifecycle, business processes, and decision making. Business...

AI/ML operating models

The AI/ML operating model plays a crucial role in how an organization can achieve its AI maturity goals. It can have a profound impact across a range of key dimensions such as organizational agility, governance and standardization, resource and technology efficiency, domain expertise, risk management, and ownership and accountability.

Organizations will need to consider their unique organizational needs when deciding on the operating model for their AI initiatives. At a high level, there are three main operating models to consider: centralized, decentralized, and hub and spoke.

Centralized model

For organizations starting their ML journey and looking for efficient use of their scarce ML talents, they probably want to consider a centralized model, especially if the main goals are unified AI/ML strategy, consolidation of ML talents, and standardization of technology and tools.

In a centralized model, a single central team is responsible for all...

Solving ML journey challenges

At this point, you should have a good understanding of key ML maturity dimensions including technical, business, governance, and organizational and talent, for the successful adoption of AI/ML. Next, let’s delve into the key steps needed to establish some of these AI maturity capabilities and solve some of the key challenges faced along the ML journey, starting with creating an AI vision and strategy.

Developing the AI vision and strategy

To develop an AI vision and strategy, an organization should first define the purpose and scope of the AI vision. The vision should explain why an organization is pursuing an AI strategy and what business values it hopes to achieve. For example, the vision for a customer support organization in a bank might be to transform its business operations and improve customer experience using AI; a pharmaceutical company might have the vision of using AI to streamline the drug discovery process and improve patient...

Summary

In this chapter, we explored the different phases of ML adoption and AI/ML capabilities. You were introduced to the assessment of ML adoption maturity through a set of questions aimed at identifying key areas for developing AI/ML maturity. We also discussed the best practices in establishing an AI/ML vision, initiating an AI/ML initiative, and scaling your AI/ML adoption across different ML use cases, ML infrastructure, and ML governance.

In the upcoming two chapters, we will delve deeper into generative AI, exploring its impact on businesses, its use cases, technological solutions, architectural considerations, and practical applications that leverage generative AI.

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