Some of the ways I would take this up-

1. Understand the Basics:

• Start with foundational knowledge in ML/AI. Familiarize yourself with key concepts, algorithms, and terminologies. Online courses and tutorials can be helpful for this.

2. Take Online Courses:

• Enroll in structured online courses from reputable platforms like Coursera, edX, or Udacity. Courses such as "Machine Learning" by Andrew Ng on Coursera are highly recommended for beginners.

3. Explore Specializations:

• Dive deeper into specific areas within ML/AI that align with your interests and career goals. This could include natural language processing, computer vision, or reinforcement learning.

4. Hands-On Projects:

• Apply your knowledge by working on practical projects. Platforms like Kaggle provide datasets and competitions to practice and showcase your skills.

5. Networking:

• Connect with professionals in the ML/AI field. Attend industry conferences, webinars, and join online communities to stay updated on trends and best practices.

6. Read Relevant Literature:

• Stay informed by reading research papers, articles, and books related to ML/AI. This will deepen your understanding of the field and help you stay current.

7. Leverage Your Product Management Skills:

• Your product management experience is valuable. Explore how ML/AI can enhance product features, user experience, and overall business strategy.

8. Experiment with ML Tools:

• Gain hands-on experience with popular ML tools and frameworks like TensorFlow or PyTorch. Experimenting with these tools will enhance your practical skills.

9. Collaborate with Data Scientists:

• Collaborate with data scientists and engineers within your organization. This can provide real-world insights and opportunities for collaborative projects.

10. Consider Advanced Education:

• If feasible, consider pursuing advanced education in ML/AI. There are executive programs and online master's degrees tailored for professionals.

11. Showcase ML/AI in Your Profile:

• Update your professional profiles (LinkedIn, resume, etc.) to reflect your interest and progress in ML/AI. Highlight any relevant projects or courses you've completed.

12. Seek Guidance:

• Reach out to mentors or professionals who have successfully transitioned from your role to include ML/AI in their skill set. Their insights can be invaluable.

13. Attend Workshops and Meetups:

• Attend local workshops, meetups, or virtual events focused on ML/AI. This provides networking opportunities and exposure to the latest industry trends.

14. Build a Portfolio:

• Showcase your ML/AI projects in a portfolio. This can be a powerful asset when demonstrating your practical skills to potential employers.

15. Stay Committed to Learning:

• ML/AI is a rapidly evolving field. Commit to continuous learning and staying updated on emerging technologies.

Remember, integrating ML/AI into your skill set is a gradual process, and consistent effort will yield positive results over time. Embrace the learning journey, and leverage your existing strengths as a product manager to bring a unique perspective to the field.