I built aiplanet.com where numerous beginners accessed free AI learning resources provided by a diverse group of contributors (primarily experienced AI practitioners). Building on the common advice, here are some insights to consider, given your existing context:
- AI/ML is diverse, with data scientists specializing in different areas. I know AI experts who have still not delved into LLMs; they have their specific focus areas. AI/ML skills encompass a wide range of topics, and data scientists often have specific focus areas. Continuous exploration and reading are crucial. Resources like paperswithcode.com are valuable for discovering new research areas and domains.
- While time-consuming, Kaggle offers exposure to robust modeling and validation skills. These skills are critical, though they are only a fraction of what's needed for real-world projects. It's beneficial to expand beyond these skills. This being said, it does give bragging rights. I've seen company founders, like those at H20.ai, often highlight their Kaggle Grandmasters.
- My current role is at Pathway.com. Over 80% hold of my colleagues PhDs, and our CTO has co-authored with folks like Geoff Hinton and Yoshua Bengio (I find that cool actually :)). But this environment may reflect my bias towards academic research. This being I said, I believe that strong foundational understanding is essential and also valued, especially when tackling complex challenges.
- Active participation in forums and communities related to the frameworks you use is highly recommended, like TensorFlow User Groups. At Pathway.com, we welcome those interested in stream data processing to our community. Engaging in these forums offers the chance to receive support from the original creators and leading community members. Other notable communities include DataTalks.Club and MLOps.Community.
- AI/ML is diverse, with data scientists specializing in different areas. I know AI experts who have still not delved into LLMs; they have their specific focus areas. AI/ML skills encompass a wide range of topics, and data scientists often have specific focus areas. Continuous exploration and reading are crucial. Resources like paperswithcode.com are valuable for discovering new research areas and domains.
- While time-consuming, Kaggle offers exposure to robust modeling and validation skills. These skills are critical, though they are only a fraction of what's needed for real-world projects. It's beneficial to expand beyond these skills. This being said, it does give bragging rights. I've seen company founders, like those at H20.ai, often highlight their Kaggle Grandmasters.
- My current role is at Pathway.com. Over 80% hold of my colleagues PhDs, and our CTO has co-authored with folks like Geoff Hinton and Yoshua Bengio (I find that cool actually :)). But this environment may reflect my bias towards academic research. This being I said, I believe that strong foundational understanding is essential and also valued, especially when tackling complex challenges.
- Active participation in forums and communities related to the frameworks you use is highly recommended, like TensorFlow User Groups. At Pathway.com, we welcome those interested in stream data processing to our community. Engaging in these forums offers the chance to receive support from the original creators and leading community members. Other notable communities include DataTalks.Club and MLOps.Community.