The One Practice That Is Separating The AI Successes From The Failures

The One Practice That Is Separating The AI Successes From The Failures

Somewhere between 60-80% of AI projects are failing according to different news sources, analysts, experts, and pundits. However, hidden among all that doom and gloom are the organizations who are succeeding. What are those 20%+ of organizations doing that are setting themselves apart from the failures, leading their projects to success?

One of the biggest insights from these AI successes is that they don’t see AI projects as application development or functionality-driven projects. Rather, they see them as data projects, or sometimes even data products. A data project doesn’t start with an idea of what the functionality needs to be, but rather focuses on what insights or actions need to be gleaned from the data in whatever current shape it’s in.

Agile is Dead. Long live Agile.

The most popular methodology for application development is Agile, which focuses on short, iterative sprints tied to the immediate needs of the business user versus long development cycles. However, Agile falls flat when dealing with AI because it doesn’t tell you how to deal with data, the core asset of an AI system. CPMAI Methodology updates CRISP-DM with Agile and AI-specific details.

Incorporating AI into your organization's workflow requires a different approach. It requires a shift from the traditional Agile methodology to a more data-centric approach. This is where the CPMAI methodology comes into play. CPMAI combines the best practices of Agile with AI-specific details, ensuring that data is at the forefront of the development process.

Deploying AWS Chalice application using AWS Cloud Development Kit | Amazon Web Services

To successfully deploy an AWS Chalice application using the AWS Cloud Development Kit (CDK), it is important to understand the frameworks involved in the process. The CDK is an open-source software development framework that allows you to model and provision cloud application resources through AWS CloudFormation using familiar programming languages such as TypeScript, JavaScript, Python, C, and Java.

In the case of deploying an AWS Chalice application, Chalice is a Python Serverless Microframework for AWS that enables quick creation and deployment of applications using Amazon API Gateway and AWS Lambda. It analyzes application code to generate API Gateway resources definition, Swagger documentation, and SAM template, among others, saving development time and ensuring the application and infrastructure logic are in sync.

Additionally, the AWS Serverless Application Model (AWS SAM) is another framework that can be used to build serverless applications on AWS. It complements Chalice by providing a standardized way to define serverless applications and simplifying the deployment process.

By leveraging the CDK, Chalice, and AWS SAM together, you can implement a comprehensive service infrastructure for your AWS Chalice application. This includes components like Amazon Virtual Private Cloud (VPC) networking, AWS Certificate Manager (ACM) certificates, Amazon Simple Queue Service (SQS) queues, and AWS Secrets Manager secrets.

Actionable Advice:

1. Shift your perspective: Instead of treating AI projects as traditional application development projects, view them as data projects. Focus on the insights and actions that can be derived from the data, rather than predefining functionality.

2. Embrace the CPMAI methodology: Agile methodologies have limitations when it comes to AI projects. Adopt the CPMAI methodology, which combines Agile practices with AI-specific details, to ensure that data is the driving force behind your AI initiatives.

3. Leverage AWS frameworks: When deploying AWS Chalice applications, make use of the AWS Cloud Development Kit (CDK), Chalice, and AWS Serverless Application Model (SAM). These frameworks provide the necessary tools and resources to streamline the deployment process and build a robust service infrastructure.

In conclusion, the success of AI projects lies in adopting a data-centric approach and utilizing the right frameworks and methodologies. By shifting the perspective from application development to data analysis, organizations can unlock the true potential of AI. Embracing the CPMAI methodology and leveraging AWS frameworks like CDK, Chalice, and SAM can further enhance the effectiveness and efficiency of AI implementations. With these actionable advice in mind, organizations can increase their chances of AI success and join the ranks of the few who have overcome the challenges and achieved their goals.