Mastering AI Product Management: A Kickoff Guide for Product Managers.

Can Machine think? mathematician Alan Turing in 1950, in his paper titled “Computing Machinery and Intelligence.”

As artificial intelligence (AI) continues to reshape industries and transform the way we live and work, the role of AI product managers and developers has become increasingly important. that’s why Turing question is back to be public question everyone in or outside tech is asking.

To build effective AI products, it’s crucial to understand the essential components and tools needed for success. In this comprehensive guide, we’ll explore the key components of an AI infrastructure and the tools that are used to develop AI products.

What is AI and is not?

AI, or artificial intelligence, can mean many things depending on who you ask. However, for product managers AI, or artificial intelligence, refers to the ability of machines to perform tasks that typically require thinking, such as learning, problem-solving, and decision-making. The more you humanize it make it able to “Think”, the better results you can achieve because you can leverage the advantages of machines, which are speed and accuracy, while also adding the creativity of human thinking. This can lead to improved products and services in various industries, including healthcare, finance, and e-commerce.

What DLC AI Product Manager chose?

Feed it with data and make it learn from its mistakes: that’s how machine works with reinforcement model, sounds like iterative to you right?

In the rapidly evolving field of artificial intelligence (AI), managing AI products requires an iterative and agile approach. AI products are often developed using machine learning algorithms, which require extensive testing and refinement to achieve optimal performance.

So, what is the Iterative Nature of AI Product Development?

AI product development is an iterative process because it requires continuous evaluation and refinement of the system to ensure that it is performing optimally and meeting the needs of the users. Product managers work closely with cross-functional teams, including data scientists, engineers, and designers, to develop and refine the AI product. The team works in short sprints to develop and test new features and functionality. At the end of each sprint, the team reviews the results and adjusts the product roadmap and development plan as needed.

The agile approach allows product managers to quickly respond to changes in the market, user feedback, and emerging technologies, and to continuously improve the product over time. By working in an agile manner, product managers can ensure that their AI products are meeting the needs of their users and are competitive in the fast-paced and rapidly evolving AI market.

Machine Learning vs. Deep Learning vs. Artificial Intelligence

In short it’s not seperated models because Machine learning is considered as a subset of artificial intelligence and Deep learning is a subset of Machine learning.

Machine learning is a method of teaching computers to learn patterns and make predictions based on data inputs. To do so, it requires two main components:

1. The model: This refers to the algorithm or set of algorithms that are used to analyze the data and make predictions. The model is designed to identify patterns and relationships in the data, and it can be customized to suit different types of data and tasks.
2. The training data: This is the data that the model uses to learn and make predictions. The training data can be labeled or not depending on the type of data you are working with.

Together, these two components form the basis of machine learning. The model is trained using the training data, and once it has learned to recognize patterns and make predictions, it can be used to analyze new data and make predictions based on that data.

In machine learning we take the data and use it in training model and you will iterate back and forth to have the model working the way you want and you are creating the model based on the data and features in Deep Learning the process is the same but in deep learning the patterns are highly picked up.

What is the optimal flow and where every part of the process live?

“Companies that fully absorb AI in their value-producing workflows by 2025 will dominate the 2030 world economy with +120% cash flow growth,” according to McKinsey Global Institute.

whether for internal usage or AI is the product itself the process is going to be hard to optimize from data collection tell generating the working model and using it lets break it into simple steps:

1. Define the problem: The first step is to clearly define the problem you’re trying to solve with AI. This involves understanding the business context, identifying the stakeholders, and defining the success criteria.
2. Collect and prepare data: The next step is to gather and prepare the data that will be used to train and test the AI model. This may involve cleaning, transforming, and normalizing the data, as well as handling missing or invalid values.
3. Feature engineering: This step involves selecting and creating the features that will be used to train the AI model. This can involve domain knowledge, creativity, and experimentation to identify the most relevant features.
4. Model selection: The next step is to choose the appropriate AI algorithm and architecture for the task at hand. This may involve experimenting with different models and parameters to identify the best one for the data and task.
5. Model training: This step involves using the prepared data to train the selected AI model. This may involve using techniques such as cross-validation and hyperparameter tuning to optimize the model’s performance.
6. Model evaluation: The trained AI model is evaluated on a separate validation dataset to assess its performance. This may involve using metrics such as accuracy, precision, recall, and F1-score to evaluate the model’s performance.
7. Model deployment: The final step is to deploy the trained AI model in a production environment, where it can be used to make predictions on new data. This may involve integrating the model into an existing software system or creating a new application to expose the model’s predictions.

I need to mention the importance on Continuous maintenance for the system in DevOps we usually refer to it as CICD And while workin on AI system it will be workinf on AIOps where you will work on CTCM continuous training and continuous monitoring.

AI/ML Infrastructure

Highmark Inc, saved more than $260M in 2019 by using ML for fraud detection, GE helped its customers save over $1.6B with their predictive maintenance, and 35% of Amazon’s sales come from their recommendation engine.

Building AI products requires a complex infrastructure that involves multiple components, such as data storage, processing, model training, deployment, and continuous monitoring and maintenance. This complexity requires a holistic approach that combines both AIOps and DevOps methodologies.

DevOps is a set of practices that emphasizes collaboration and automation between software development and IT operations teams. It is designed to streamline the software development lifecycle and ensure that software is delivered quickly and reliably. DevOps is well-suited for building the infrastructure needed for AI products, as it emphasizes automation and continuous delivery. By using DevOps practices, organizations can build and deploy the infrastructure needed for AI products quickly and efficiently.

AIOps, on the other hand, is a set of practices that focuses specifically on the challenges of managing AI-based systems. It involves using AI and machine learning techniques to automate and optimize IT operations, including monitoring, troubleshooting, and maintenance. AIOps is critical for ensuring that AI systems remain accurate and relevant over time, and can provide significant benefits in terms of efficiency, scalability, and reliability. By using AIOps practices, organizations can ensure that their AI systems remain effective and valuable assets in the long term.

By combining AIOps and DevOps, organizations can create a comprehensive infrastructure for building and managing AI products. This involves using DevOps practices to build and deploy the infrastructure needed for AI products, and using AIOps practices to ensure that the AI systems remain accurate and relevant over time. This holistic approach can help organizations overcome the complex challenges of building and managing AI products, and can provide a competitive advantage in the marketplace.

key Product Management metrics For AI products

AI products are inherently different from traditional software, with unique performance characteristics and business impacts. Metrics like model accuracy, precision, recall, and F1 score can provide insights into the technical performance of your AI models. Complementing these with business-focused metrics like user engagement, conversions, and revenue can help you quantify the real-world impact of your AI products. here is most commonly used matrics :

Performance Matrics:

Accuracy:

• Accuracy is the most basic and intuitive performance metric, measuring the proportion of correct predictions made by the model.
• It’s calculated as the ratio of the number of correct predictions to the total number of predictions.
• Accuracy is a good overall measure of model performance, but it can be misleading if the dataset is imbalanced (i.e., one class is much more prevalent than the other).

Precision:

• Precision measures the proportion of positive predictions that are actually correct.
• It’s calculated as the ratio of true positive predictions to the sum of true positive and false positive predictions.
• Precision is important when the cost of a false positive is high, such as in fraud detection or medical diagnosis.

Recall:

• Recall measures the proportion of actual positive instances that are correctly identified by the model.
• It’s calculated as the ratio of true positive predictions to the sum of true positive and false negative predictions.
• Recall is important when the cost of a false negative is high, such as in disease detection or customer churn prediction.

F1-Score:

• The F1-score is the harmonic mean of precision and recall, providing a balanced measure of model performance.
• It ranges from 0 to 1, with 1 being the best score.
• The F1-score is useful when you want to strike a balance between precision and recall, especially when dealing with imbalanced datasets.

Performance Tracking Tools:

• Monitoring Dashboards: Tools like Datadog, New Relic, and Grafana provide customizable dashboards to visualize and monitor key performance metrics for AI models in production.
• MLOps Platforms: Solutions like MLflow, Kubeflow, and Amazon SageMaker provide end-to-end platforms for managing the lifecycle of AI models, including performance monitoring and deployment.
• BI Tools: Traditional business intelligence (BI) tools like Tableau, Power BI, and Looker can be integrated with AI model performance data to generate comprehensive reports and dashboards.

A/B Testing Frameworks:

• Experimentation Platforms: Tools like Optimizely, Adobe Target, and Google Optimize enable product managers to easily set up and run A/B tests for AI-powered features or user experiences.
• Deployment Strategies: Techniques like canary deployments, feature flags, and blue-green deployments can be used to gradually roll out and test new AI model versions or features with a subset of users.
• Statistical Analysis: Product managers should use statistical significance tests, such as chi-square or t-tests, to determine whether the observed differences between the control and test groups are statistically significant.

Integrating Performance Tracking and A/B Testing:

• Closed-Loop Feedback: Integrating performance tracking and A/B testing allows product managers to quickly identify the impact of model updates or feature changes and make data-driven decisions about further iterations.
• Automated Experiments: Advanced platforms like Optimizely and Google Optimize offer features to automatically run and analyze A/B tests, providing recommendations for the optimal configuration based on the performance metrics.
• Continuous Improvement: By continuously running A/B tests and monitoring performance metrics, product managers can iteratively refine the AI model and user experiences to drive ongoing improvements and meet evolving business objectives.

Best Practices for A/B Testing AI Products:

• Clearly Define Success Criteria: Establish clear, measurable success criteria for the A/B test, aligning with the overall business objectives.
• Ensure Statistical Significance: Determine the appropriate sample size and run the test for a sufficient duration to achieve statistical significance.
• Randomize and Control: Randomly assign users to the control and test groups to ensure a fair comparison.
• Monitor for Unintended Consequences: Closely monitor for any unexpected impacts on user experience, model fairness, or other critical metrics.
• Communicate and Collaborate: Engage the cross-functional team, including data scientists and engineers, to interpret the test results and make informed decisions.

User Engagement Metrics:

• Active Users: Measures the number of users actively engaging with the AI product within a given time period. This indicates the overall adoption and usage levels.
• Session Time: Tracks the average time users spend interacting with the product. Higher session times suggest stronger engagement.
• Frequency of Use: Measures how often users return to use the product. Higher frequency indicates loyal, engaged users.
• User Behavior: Tracks specific user actions like clicks, searches, feature interactions. Reveals popular/underused product areas.
• Time to Value: Measures how long it takes users to start deriving value from the product. Shorter time indicates a more intuitive user experience.

Retention Metrics:

• Repeat Usage: Percentage of users who continue using the product over time. Higher repeat usage signals loyalty and value delivery.
• Churn Rate: Percentage of users who stop using the product over time. Lower churn is better, indicating fewer issues driving users away.
• Customer Lifetime Value: Average revenue generated per user over their lifetime. Identifies most valuable user segments.
• Retention Rate: Percentage of users who continue using the product over successive time periods. Higher retention is ideal.

Conversion Metrics:

• Conversion Rate: Percentage of users who complete a desired action like signing up or making a purchase. Tracks effectiveness of the product’s core functionality.
• Cost per Acquisition: Average cost to acquire a new user. Helps evaluate marketing efficiency and customer acquisition strategies.
• Average Revenue per User: Average revenue generated per user. Informs monetization models and pricing strategies.
• Funnel Conversion Rate: Percentage of users completing each stage of the conversion process. Highlights bottlenecks in the user flow.

Customer Satisfaction Metrics:

• Net Promoter Score (NPS): Measures likelihood of users recommending the product. High NPS indicates strong customer advocacy.
• Customer Satisfaction (CSAT): Tracks overall user satisfaction levels on a scale. Provides direct feedback on the product experience.
• Customer Effort Score (CES): Gauges ease of use for specific tasks. Highlights friction points in the user experience.

Time to Market:

• Tracks the duration from initial concept to product launch. Faster time to market can provide competitive advantage, but rushing quality may backfire.
• Measures progress through key development milestones. Compares against historical benchmarks or competitor timelines.

Finally, AI is not as scary as it may seem for product managers to get involved. It presents a promising scope that everyone in the tech industry should get involved in.