What are effective ways to build professional relationships in Machine Learning across industries?

You are a reflection of your network, some say. Invest time to grow and maintain your professional network by writing articles, attending conferences, joining discussions related to your domain. Follow thought leaders, ask for guidance, get inspired and help others. It takes time and effort to build a network, and internet has enabled us to do this on a rather easy mode. Online communities like Reddit, Medium, Kaggle, DataCamp, Stack Overflow, Quora, Twitter, LinkedIn, and Facebook allow you to express your opinions freely and find support from like minded individuals. The more you contribute to the ML community on these platforms, the easier it'd be for you to follow latest trends and find opportunities!

Learn to present yourself and your work related to ML by writing about your projects something you found interesting about an approach or algorithm. Connect with professionals through webinars, conferences and through LinkedIn. Participate in innovation sessions, stay up to date with what are the trends in ML.

If you're diving into the world of Machine Learning, consider joining online communities—it's like unlocking a goldmine for professional growth. These virtual hubs are where minds converge, sharing insights, challenges, and the latest trends. It's not just about staying in the loop; it's your ticket to networking with like-minded folks and finding those with skills that complement yours. The collaborative discussions and resource sharing? Pure gold. In a field that evolves as swiftly as Machine Learning, being an active part of these online communities isn't just smart—it's your secret weapon for staying sharp, expanding your circle, and propelling your professional journey forward. Trust me, it's a game-changer.

I've found Twitter and Reddit to be extremely helpful. Some threads are essentially pros "chatting over a coffee", and it's a joy to be able to gain from their perspectives. I feel that a lesser mentioned resource is Discord, which again is an amazing platform for building online communities.

Apart from what has already been suggest, I would recommend the following: 1. Publish Research/Articles: Showcase expertise, attract industry attention. 3. Speak at Events: Share knowledge, engage with peers. 4. Join ML Organizations: Access resources, connect with professionals. 5. Collaborate on Research: Partner for practical experience, build relationships. 6. Hackathons/Competitions: Showcase skills, meet like-minded individuals. 7. Online Forums/Q&A Sites: Share knowledge, gain recognition. 8. Volunteering: Apply ML skills in non-profits, expand network.

Conferences are my favorite choice for networking with other data science/machine learning professionals. Conferences are packed with the best of the best professionals whom I can learn so much from. The best part about conferences is that the main purpose of them is to share knowledge, therefore I can get the best of both worlds, increase in knowledge and be updated with the current trends in data science/machine learning as well as connect with the best minds in the field.

Workshops or conferences allow better discussion and collaborative opportunities. You can meet people who are leaders in your field of work and may be in just one meeting you can get a good opportunity for work.

Build a network! conferences, meet ups , events are good places to mingle with other in the industry. However the most important part of it is that you actually go and talk with people, share your interests, experiences, get them talk about their work, interests and everything as well. Gain as much as new knowledge and connections. Presenting your work at conferences as an abstract or paper is also highly recommended to gain recognition across professionals. It shows that your work is accepted across the industry. I find it as an interesting challenge to take on, because presenting your work to an audience means you are ready to take criticism and feedback for your work and ready to defend it too.

Build a network! conferences, meet ups , events are good places to mingle with other in the industry. However the most important part of it is that you actually go and talk with people, share your interests, experiences, get them talk about their work, interests and everything as well. Gain as much as new knowledge and connections. Presenting your work at conferences as an abstract or paper is also highly recommended to gain recognition across professionals. It shows that your work is accepted across the industry. I find it as an interesting challenge to take on, because presenting your work to an audience means you are ready to take criticism and feedback for your work and ready to defend it too.

Attending Summits, events, meetups and conferences will help to build the network faster as well as, its an opportunity to understand new things that are coming into the market. This will help to identify industry specific usecases and skills, tools and technique's required to address those at a faster pace.

This is by far the best way. Many companies that are whale hunting start here. They have open source repos that are a huge portion of their work, and are always looking for people extremely familiar with specific libraries and repos. Being a high level contributor to these sets you up to constantly have companies using the tool reaching out.

This is probably your highest ROI avenue. It will provide you with concrete evidence of your programming skill, which is essential for a data scientist to machine learning engineer to possess. Having a track record here is also hard to fake, sending a very strong signal to prospective hiring managers.

You wouldn't want someone to play in your band if they didn't practice in their own time. It implies an underlying passion. People who contribute to open source are by far the most talented people I've encountered. They aren't afraid to share their work with other people and be part of the shared journey of human progress in this field. By working in the open people can find you.

contributing to open source projects in ML is not just about coding or algorithm development; it's also about building a network, showcasing expertise, and staying engaged with the latest industry trends and innovations. This approach aligns well with ML professionals whose career spans multiple sectors and includes a focus on innovative applications of AI and ML.

Contributing to open source projects is the most effective way to build professional relationships in Machine Learning across various industries. For instance, you can search for projects hosted on platforms like GitHub, GitLab, or Bitbucket that utilize or develop machine learning tools, frameworks, libraries, or applications. You can also explore projects aligned with your industry or domain, such as healthcare, education, finance, or entertainment. By contributing to open source projects, you can collaborate and communicate with other contributors, maintainers, and users, making it the best way to build professional relationships in Machine Learning across industries.

Different ways projects can stand out include: - In addition to model development, showing evidence of MLOps skill/model deployment. - Working on more recently established datasets where not a lot of work has been done - Applying ML to an area where you show your deep understanding of a domain In these public projects, clean code, great documentation and a nice narrative surrounding the project will also make it stand out.

This is one of the best ways to share knowledge across different learners and grow the professional relationships. This field is growing and many are looking to grow their knowledge. One of the question keep coming is from the student community where they understand how to write the programs but keep asking for practical use cases . Similarly lots of seniors professionals understand business but finding difficult to correlate how to apply machine learning .

In my experience building a long shot personal project in public (i.e. talking about it while you build on social media) helps a lot. People including recruiters get to know you and as well you make meaningful connections.

I think creating your projects is a great way to learn about a new topic that you are interested in. For example, if you see a exciting new tech, whether it's a new API, new library or new concept, if you create a project on it, you learn it the fastest and sharing it on social media is a great way to establish yourself as a thought leader in your area. After all, only those who keep learning themselves can one day be experts in what they do!

Doing a project is the best way of solidifying your skills especially in an applied science. - For beginners, start out with your course projects and try to go above and beyond the requirements - For intermediates, start to read simple papers and replicate them as well as doing harder projects (eg: building and training a non-trivial ML model, using a custom dataset, documenting the results) - For advanced learners, seek out to build a niche, at this point you probably know the area that you love the most, try to build advanced projects (eg: training a model, deployment on cloud/edge) that is like an MVP of an actual product. This can give you an edge over other candidates who do not show similar practical experience.

Embarking on my journey in the machine learning domain, I encountered a defining moment when seeking mentorship and guidance transformed my trajectory. I found mentorship within a cross-industry collaboration initiative, where professionals from diverse sectors convened to share their expertise. Engaging with a mentor from the healthcare industry, I gained profound insights into the nuances of applying machine learning in the medical field. This not only broadened my perspective but also opened doors to collaborative projects that transcended the boundaries of traditional industry silos.

Mentors and sponsors have been my career catalysts. I've been lucky to meet incredible mentors who've guided me not just technically but also in leadership. Their advice has shaped my career. It's not just about learning technical stuff; it's finding mentors who've been there, done that, and can guide you in career development, skill improvement, and project execution. The synergy of mentors and sponsors is a game-changer. They offer guidance and the support needed to turn potential into real opportunities. In the ever-evolving field of machine learning, this support system is indispensable.

Elevate your machine learning journey: 🤝 Connect with Mentors: Seek guidance from experienced professionals for career, skills, and project insights. 🌐 Expand Network: Mentorship broadens your network, offering valuable advice, support, and opportunities. 💼 Business Guidance: Explore mentors for strategic business insights. 🚀🤝 #Mentorship #MachineLearning #CareerGrowth

I love mentoring people to their growth. The suggested approach of seeking guidance and mentorship in the field of machine learning is a proactive and insightful strategy. Engaging with experienced professionals for career development, skill enhancement, and project implementation can significantly contribute to one's growth. The mention of coaches and consultants adds a valuable dimension, emphasizing the importance of strategic thinking in business and product design. This holistic approach not only accelerates learning but also establishes meaningful connections within the machine learning community, fostering a collaborative and supportive professional environment.

A crucial first step in navigating the vast field of machine learning is to look for mentorship. I have personally experienced the transforming power of advice from knowledgeable experts in this field. I've discovered that mentoring is a two-way street. It involves more than just following instructions; it also involves learning on your own, following up on conversations, and leading group projects. It's about using the mentoring relationship to increase knowledge and make wise choices. They give guidance, preventing distractions and making it easier to sort through the many options. Using ML mentorship platforms such as Experfy, MentorCruise, or Kaggle's mentorship programmes can help to create organised growth opportunities.

Be more vocal about your work, experiences and even opinions both online and offline. Sell yourself and your skills. At the same time, don't be afraid to approach or reply to anyone who makes an interesting point. These can often lead to great conversations and valuable networks. If you have the skills and knowledge to back it, you want people to know you as the AI guy. This can open up opportunities you may have not even conceived of yourself. At the same time, the conversations can often be very insightful to both you and your conversation mate. You not only learn from each other, but forcing yourself to vocalize your thoughts can make your own thoughts much more clearer.

Explore new things. Adopt new changes and accept the challenge. This is the only way forward otherwise you’ll stuck on the learning path.

Make your database public. One of the main problem in ML practical problem is lack of database, especially labeled data sets

Once can also host seminar, workshop preparing materials for ML and related topics. This will give you enough confidence about your skills and strengthen your skills while providing training to other people!

Universities produce many new professionals with impressive skills and knowledge in Machine Learning. They are well-versed in both, algorithmic foundations and the latest state-of-the-art models. What they often still need and are very happy to gain, is some insights into how these techniques are used in practice to generate value. If you provide a platform where you bring them together with seasoned professionals for a casual exchange of ideas, everyone can benefit a lot. Joining from the other end, I have found these exchanges to be an effective way to keep in touch with the latest milestones and developments from academia. Once the students become alumni and start their careers these networks become more and more valuable.