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💫 AI/ML Data Talks Podcast

In this podcast episode I share about my journey and transition from academia to industry and the lessons I learned along the way.

During our chat, we talk about some of the hottest topics in machine learning, like What is MLOps? Data Drift vs Concept Drift, and Monitoring Machine Learning Model.

We also talked about some insights into the latest AI and ML trends and ecosystem. Specifically how important it is for various tools to be able to communicate with each other, and how ZenML helps with that.

Additionally, if you’re interested in pursuing a career in AI or ML, I also discussed what worked for me during my transition and potential pitfalls.

Starting projects and writing about them publicly is a great way to solidify your understanding of the material.

So if you’re curious about the current state of AI and ML, and you’re looking for some practical advice on how to get started in the field, this episode is definitely for you!

Catch my discussion with Poo Kuan Hoong, PhD - Google Developer Expert and Lead Data Scientist at BAT 👇

👍 Tips to Get Started with ML

If you’re interested in AI or ML engineering but don’t know where to start, now is a great time to jump in. There are many resources available to help you learn and get started with these technologies.

One important tip is to start writing about your projects and experiences. Writing can help you solidify your understanding and connect the dots between different concepts. Don’t be afraid to learn in public and share your work, even if you don’t think anyone else will read it.

Remember, writing is for you, and it can do more good for your own learning than for anyone else. So do yourself this favor.

If you don’t know what to write about, start with writing to yourself six months ago. What you wish you knew? What are the lessons learned? What advice you’d give to someone trying to walk the same path you took?

Another tip is to practice coding and work through different tutorials and exercises. Don’t get stuck in “tutorial hell” by only following tutorials without applying your knowledge to real-world problems.

Finally, don’t be discouraged by the steep learning curve of AI and ML especially if you’re coming from another field. With persistence and dedication, you can develop the skills to become a successful AI or ML engineer.

📖 Lessons Learned

One of the key lessons learned is the importance of staying up-to-date with the latest industry trends and tools. While academic research focuses on developing new theories and algorithms, the industry is more focused on practical applications that can generate business value.

Another important lesson is the need to develop strong communication skills, as working with cross-functional teams requires the ability to effectively communicate complex technical concepts to non-technical stakeholders.

Additionally, it is important to be adaptable and willing to learn new skills as the field of machine learning is constantly evolving. Finally, it is essential to maintain a passion for the field, as it is the driving force behind success in both academia and industry.

✅ Conclusion

Transitioning from academia to industry as an AI or ML engineer can be a challenging but rewarding experience.

It is important to keep learning and adapting to new technologies and methodologies, as well as to continuously improve communication and collaboration skills.

Writing and blogging about projects and experiences can also be a valuable tool for personal growth and development. By studying case studies and success stories, one can gain insights into the real-world applications of AI and ML in different industries.

With the right mindset and approach, the transition can be a fulfilling journey towards a successful and fulfilling career.