Here's how you can maintain your relevance in the AI field after a layoff.

Hackathons, coding competitions, and AI challenges offer hands-on experience and exposure to practical problems. These events not only enhance your technical skills but also demonstrate your ability to innovate and apply knowledge under pressure.

In the rapidly evolving field of AI, continuous learning is crucial to maintaining a competitive edge. Allocate Time Daily: Dedicate a specific time each day for learning. Even 30 minutes a day can accumulate to significant progress over time. Online Courses: Platforms like Coursera, edX, and Udacity offer courses on a wide range of AI topics. Webinars and Tutorials: Follow industry leaders and organizations that regularly host webinars and tutorials. Websites like AI Conferences and Eventbrite list relevant events. Reading Material: Subscribe to AI journals, blogs, and newsletters. Websites like arXiv, Towards Data Science, and Medium are great sources for the latest research and insights.

?? ???????? ?????????? ???? ????! ?? To maintain relevance in the AI field after a layoff, commit to daily upskilling. Stay on top of the latest tech and methodologies. Dedicate time to learning new skills or deepening your knowledge in areas like machine learning, data science, and neural networks. Dive into online courses, webinars, and tutorials to keep your skills sharp. Lifelong learning ensures your expertise remains valuable and attractive to future employers. ?????? Keep learning, keep growing!

Update Your Profile: Ensure your LinkedIn profile is current and highlights your AI skills, experiences, and projects. Connect with Industry Professionals: Send personalized connection requests to people in the AI field. Join Relevant Groups: Participate in AI-focused groups to stay informed and engage in discussions. Showcase Your Projects: Maintain a well-organized GitHub profile with your projects and contributions. Collaborate: Engage with other developers by contributing to open-source projects. Follow Influencers: Keep up with AI thought leaders and industry news by following relevant accounts.

?? Connect with AI professionals through online platforms, virtual meetups, and industry conferences. ?? Sharing experiences and learning from others can lead to new opportunities and collaborations. ?? As Helen Keller said, "Alone we can do so little; together we can do so much." ?? Like if you value networking!

Engage in online forums, attend industry webinars, and participate in local meetups to keep your skills sharp and your connections fresh. Leverage platforms like LinkedIn to showcase your expertise by sharing insights and joining discussions. Consider collaborating on projects or contributing to open-source initiatives to maintain visibility. By staying active and visible in the AI community, you not only expand your network but also demonstrate your commitment and adaptability, essential traits for bouncing back stronger and more connected than ever.

Networking and collaboration is a powerful medium through which one can improve AI career opportunities. Join with online networking platforms and stay connect with AI professionals. Contribute to local AI community groups and improve your networking and collaboration. Contribute to open source development. Start publishing AI articles, learning experiences and there by improve your chances of career advancement.

GitHub: Search for popular AI repositories on GitHub that match your interests and expertise. AI Organizations: Follow organizations like TensorFlow, PyTorch, and Hugging Face, which maintain numerous open-source projects. Bug Fixes: Begin with fixing minor bugs or improving documentation to get familiar with the project. Feature Enhancements: Gradually take on more complex tasks such as adding new features or improving existing ones. Join Discussions: Participate in discussions on GitHub issues, pull requests, and community forums. Attend Meetups: Join virtual or in-person meetups organized by the project maintainers. Regular Commits: Make regular contributions to build a track record.

?? Contributing to open-source AI projects or publishing your work boosts your visibility. ?? Showcase your skills and commitment by engaging on platforms like GitHub, participating in coding challenges, or writing articles. ?? These contributions highlight your expertise and foster collaboration with peers.

Contribute to open source projects, course content, technical article developments, giving presentations in open forum, etc will improve your knowledge in AI domain. Writing articles and publishing in Medium, LinkedIn, Kaggle, AIP and other AI mediums will significantly improve your networking and will get visibility of your work.

After a layoff, staying relevant in the AI field requires actively seeking feedback. Reach out to colleagues, mentors, and industry experts to review your projects and provide valuable insights. Use platforms like GitHub to share your work and receive constructive comments. Participate in discussion groups and forums where you can present your ideas and learn from others' critiques. Incorporating feedback not only hones your skills but also shows your ability to continuously evolve, essential for standing out and demonstrating resilience in challenging times.

Adapt quickly to new technologies, tools, and methodologies by enrolling in online courses or participating in webinars. Be open to freelance opportunities or part-time roles that can expand your skill set and expose you to diverse projects. Flexibility not only demonstrates your willingness to grow and learn but also positions you as a versatile professional ready to tackle various challenges. In an ever-evolving field like AI, your ability to pivot and adapt is key to staying ahead and showcasing your resilience and commitment.

In my experience, the AI industry’s rapid changes and innovations make flexibility essential. Embracing different roles or specializations within AI, even those different from your previous job, can open unexpected opportunities and deepen your understanding of the field. For instance, experimenting with new programming languages like Python or investigating the ethical implications of AI has broadened my skill set and perspective. Additionally, participating in interdisciplinary projects and collaborating with professionals from various backgrounds has enriched my approach to AI problems. Staying adaptable not only keeps your skills relevant but also positions you to leverage emerging trends effectively.

A wonderful and underrated way to keep learning while increasing your network is to document what you're learning. Either by building a small app, a repo on GitHub or more content-focused like a video or blog post. Remember: what you just learned, someone else is trying to learn and your help is valuable.

Personal projects need to address business problems to help you be marketable and relevant. Learn by doing first understanding the AI basics and applying it to something you find interesting and valuable to a common business problem. You can find many ways to get the experience such as: - online courses that have practical projects - open source contributions in Github - competitions/hackathons like Kaggle - outbound messaging to people on social media asking - posting content on social media showcasing quick demos you made and assessing responses of people to see their thoughts

Boost your AI career with personal projects: 1. Choose relevant projects: Address real business problems. (Example: Create a sentiment analysis tool for customer feedback, demonstrating practical AI application.) 2. Document your process: Share your journey on platforms like GitHub or Medium. This builds your personal brand and attracts potential employers. 3. Collaborate virtually: Join online AI communities to find project partners. Collaboration enhances your network and exposes you to diverse perspectives. 4. Iterate and improve: Regularly update your projects with new techniques. This shows commitment to growth and adaptability in the fast-evolving AI field.

Why personal projects? - Hands-on experience - Portfolio building - Deeper understanding of AI concepts - Freedom to explore your interests - Look beyond the plate: work on data engineering, frontend, design topics. Get your data on Kaggle datasets or better create your own dataset. Start small, then scale up: ? Begin with tutorials ? Modify existing projects ? Gradually build your own from scratch Try to build something that goes beyond the boundaries of your job description. - If you are a data scientist, do data engineering. - If you are an ML engineer, do heavy MLOps. ... The goal should always be something that can be deployed on the cloud.

Trabalhar em projetos open-source, além de projetos pessoais, é uma excelente forma de crescer e melhorar suas habilidades. Isso traz experiência valiosa e visibilidade que pode se tornar um grande diferencial. às vezes, esse envolvimento pode até resultar em ser notado e convidado pela criadora ou mantenedora do projeto.

First: if you are reading this while employed, act like you got the pink slip already...no better day to prepare than today. Second: transform the "it happened to me" into an "it happened for me." I know, I know. Easier said that done. Yet this attitude reflects the requisite self-empowerment to cope competently. This mindset opens doors...the world opens to those who with winning attitudes. Third: calculate your burn rate and length of runway, because you just became a startup. This is how you gain control...now that you know your numbers, you can execute rigorously. You are human after-all.

Consolidate your list of participated AI projects, including customer needs, success criteria, data available, intended solution, final solution and lessons learnt