01 Scope of the Enterprise AI Transformation Playbook

First of all, thank you to everyone for the overwhelming support and for subscribing.

Here's my plan for the topics. This is such an evolving and promising field with plenty of opportunities to discuss :). To subscribe, please click here

These topics will be a good blend of strategy and tactics, challenges and solutions, business and technology. Once we start discussing "Evaluating AI/ML platforms and services"?below, it can get a bit technical for some. Well, at least you have been forewarned. so please don't complain :)

Here it goes...

1. Current State of Enterprise AI:?First, we will discuss the current state of AI transformation in companies and industries and the benefits of enterprise-wide AI transformation. We will then review some critical considerations for kickstarting and scaling an AI transformation effort.

You will be surprised to know that many companies are just starting while some are really excelling (about 15 percent, according to some research)

2. How to begin and develop an AI portfolio:?In this section, I will outline what it takes to begin an AI transformation and address some of the challenges. We will discuss defining the AI strategy, identifying use cases, and prioritizing them. We will discuss how to assess the current AI maturity level of the organization and put together a plan to develop an AI POC.

The final step is to develop an AI portfolio from what we have learned from the POC.

3. Developing an AI action plan:?We will then discuss developing an action plan to scale AI transformation efforts in the organization. It means getting the business on board with AI and figuring out the business and technical scope needed to make AI work.

Most of the inputs will come from the POC we developed earlier. The idea is to iron out all the kinks, address the gaps, and build organizational capability. This is really the key, folks, and is one of the big reasons some companies fail while others excel.

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4. Identifying AI/ML use cases:?We will discuss the use cases of AI and ML in different fields, like image recognition, speech recognition, natural language processing, predictive maintenance, fraud detection, recommendation systems, and chatbots for customer service.

You will see that the opportunities to optimize processes and drive innovation are pretty mind-boggling. This is where the real power of AI lies, and quite honestly, everything starts from here!

From here on, it can get technical, at least you have been forewarned :)

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5. Evaluating AI/ML platforms and services?focuses on different machine learning platforms and services available in the market, including AWS, Google Cloud, Azure, and others. We discuss why a cloud platform is critical for AI and review the most important things to consider when picking a machine learning platform.

6. Selecting your AI/ML algorithms and frameworks?introduces us to various AI/ML algorithms and frameworks. We discuss ML algorithms like linear regression, logistic regression, decision trees, random forests, etc.

This can be a complex topic, and we will try not to get too deep into statistics but review them from the standpoint of how to choose which algorithm for which use case.

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7. Building your AI cloud platform?explores AI cloud platforms, including AWS Machine Learning, Google Cloud Machine Learning, and Azure Machine Learning. We review each platform, its services and tools, and best practices. We will discuss how to go about choosing cloud technology.

8. Achieving Operational Excellence:?The focus is on achieving excellence in AI by unlocking the power of MLOps and setting up a scalable and efficient infrastructure. We discuss the evolution from DevOps to MLOps and the benefits, principles, and lifecycle of MLOps.

9. Execution challenges -> from POC to production present?various challenges while building and deploying AI/ML POC projects into production. In addition to business, organizational, technical, and project-specific challenges, we discuss data problems, such as availability and quality, labeling, security, privacy, and drift.

NOTE: Sections 10, 11, AND 12 are all about building AI/ML models

10. Processing data?covers data processing, its critical elements of data preparation, and data engineering. Then, we look at ways to collect and prepare data for use, like cleaning, normalizing, and augmenting it.

11. Develop and deploy models?focuses on model building and deployment in machine learning. We discuss problems when building models and how to solve them. We discuss the various stages of the modeling development lifecycle: model training, tuning, and evaluation.

12. Monitor and govern models;?the focus is on model monitoring and governance in AI. We discuss model monitoring and aspects such as model explainability, drift detection, and manual update.?

This is an important topic that ensures compliance and trust on the part of the end users.?

13. Connect to apps and systems?focuses on AI apps and system integration. We cover integration with on-premises systems, other cloud systems, and front-end systems. When implementing an AI platform at the enterprise level, there is a need for integration into existing systems, so we review the options available and the best practices for integration.

That's it, folks, it's a lot, but hopefully, people will get a good feel of the end-to-end process of an AI transformation effort and choose whichever path they want to take. There are so many options or paths one can take based on past experience, skills, and aptitude, whether they are from a business or technical background.?

I hope those who want to learn, use, or drive AI transformation in their companies will find it useful.

Until next time, see you!

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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