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Mastering AI Operationsby@amir-elkabir
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Mastering AI Operations

by Amir ElkabirJanuary 5th, 2023
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The data whisperer is the function sitting between the business and the technologists. Bridging the gap between the builders and the users of AI and finding the proper management structure for AI governance. The modern program manager needs to be skilled and knowledgeable about these material differences to manage expectations well with the external stakeholders.
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The Operations of AI Programs

The data whisperer is the function sitting between the business and the technologists. Bridging the gap between the builders and the users of AI and finding the proper management structure for AI governance.


She, or he, are experts in using data analysis to help organizations better understand their customers and make more informed decisions. They have the ability to interpret large amounts of data and transform it into actionable insights that can inform business decisions.


They are also skilled at visualizing data in ways that are easy to understand and interpret. They often work closely with marketing and sales teams to help them identify trends in customer behaviors, develop targeted campaigns, and optimize their overall performance.


This is, in essence, the program manager that is very well familiar within the technology enterprises landscape, with the foundational ability to understand, work with, and analyze data.


For the modern program manager to effectively lead the data teams in a world of big data strategies, the former Software Development Life Cycle (SDLC) is now managed as a Model Development Life Cycle (MDLC).


The Lifecycle of ML Projects

  1. Define the Problem: The first step in any ML project is to define the problem you are trying to solve. This includes understanding the business objectives, available data, and desired outcomes.


  2. Data Collection and Exploration: The next step is collecting and exploring the relevant data. This includes understanding the data types, the number of features, the distribution of the data, and any relationships between the features.


  3. Data Preprocessing: Once the data has been collected and explored, it needs to be preprocessed. This includes cleaning the data, handling missing values, and transforming the data into a suitable format for the model.


  4. Model Building: After the data has been preprocessed, it is time to build the model. This includes selecting an appropriate algorithm and tuning the parameters to get the best results.


  5. Model Evaluation: Once the model has been built, it needs to be evaluated. This includes measuring the model's performance on a validation set and comparing it to other models to choose the best one.


  6. Model Deployment: The final step is to deploy the model. This includes putting the model in production, setting up a system to monitor the model's performance, and ensuring the model works as expected.


The production operationalization of AI:

Software deployment refers to the process of making a software application or system available for use. This can involve installing the software on a computer or server, configuring it to work with the necessary hardware and software components, and testing it to ensure that it is functioning correctly.


Bridging the gap between the builders and the users of AI and finding the proper management structure for AI governance.


Respectively, AI operationalizing refers to the process of implementing and using artificial intelligence (AI) in an organization. This involves building and training AI models, integrating them into business processes and systems, and deploying them to allow the organization to take advantage of its capabilities.


AI operationalizing also involves developing and implementing strategies for managing, maintaining, and updating the AI models over time and addressing any issues that may arise.


While software deployment is focused on making a software application or system available, AI operationalizing involves implementing and using AI in an organization to drive business value. Both processes involve a range of technical and logistical considerations. Still, they have different goals and focus on different aspects of the technology implementation process.


The modern program manager needs to be skilled and knowledgeable about these material differences to manage expectations well with the external stakeholders while keeping a realistic and focused project plan.




To conclude, there are key aspects that program management has evolved into while enterprises continue to grow their data activities and AI projects. Program managers must be able to stay up to date with the latest technology, best practices, and trends in order to be successful in this role.


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