Branding Best Practices for Professionals for AI & Machine Learning

Branding Best Practices for Professionals in AI & Machine Learning

• What problems do you enjoy solving the most? Is it optimizing model performance, building explainable AI systems, deploying models to production, or exploring new research frontiers? Your passion will shine through your work and your brand.
• What unique experiences or perspectives do you bring? Do you have a background in a specific industry (e.g., healthcare, finance, sustainable energy) that gives you a unique edge in applying AI? Perhaps you have a non-traditional background that provides a fresh perspective.
• What makes you different from other AI/ML professionals? This could be your communication skills, your ability to lead cross-functional teams, your ethical framework, or your dedication to open-source contributions.
• Who is your ideal client or employer? Defining your target audience helps tailor your message. Are you aiming for startups, large corporations, academic institutions, or specific research labs? ### Crafting Your Brand Statement Once you've reflected on these questions, try to synthesize your answers into a concise, compelling brand statement (often called an elevator pitch). This isn't just for job interviews; it's the core message you'll weave into your online profiles, your networking conversations, and your content. Example Brand Statement:

"I am a Machine Learning Engineer specializing in computer vision for autonomous systems, focused on building, explainable, and production-ready models. My expertise lies in translating complex research into deployable solutions, particularly for real-time applications requiring high accuracy and efficiency. I empower organizations to visual data for smarter, safer operations." Notice how this statement goes beyond generic skills to highlight specialization, impact, and target application. The more specific you are, the more memorable and appealing your brand becomes to the right audience. Remember, a clear value proposition helps you avoid being seen as a generalist in a field that increasingly rewards specialization. This clarity is especially important when marketing yourself on platforms for independent workers like our own talent marketplace at [/talent]. ### Identifying Your Niche: Hyper-Specialization in AI/ML The AI/ML field is vast. Trying to appeal to everyone means appealing to no one. Niche down. This doesn't mean limiting your future opportunities, but rather establishing yourself as an authority in a specific area first. For instance: * AI Ethics & Governance: Professionals focusing on responsible AI, fairness, bias detection, and regulatory compliance.

• MLOps Specialist: Individuals dedicated to the deployment, monitoring, and maintenance of ML models in production environments.
• Generative AI Architect: Experts in designing and implementing solutions using large language models, image generation, and other generative techniques.
• Reinforcement Learning for Robotics: Applying RL algorithms to robotic control and automation.
• Healthcare AI Consultant: Specializing in applying AI/ML to medical imaging, drug discovery, or personalized medicine. Choosing a niche immediately sets you apart and makes it easier for your ideal collaborators or employers to find you. It also allows you to focus your learning and content creation efforts, making your brand development more efficient. Consider how these niches align with the specific job categories we offer on our platform, like AI & Machine Learning Jobs. ## Building Your Online Presence: Digital Assets for AI/ML Professionals Your online presence is your primary showroom. For digital nomads and remote workers, it often replaces the traditional office or in-person networking events. A well-curated digital footprint is essential to showcase your expertise, project portfolio, and thought leadership. ### 1. Professional Website/Portfolio Your personal website is your digital home base – the central hub for your brand. It should be clean, professional, and easy to navigate. * About Me/Bio: A compelling narrative that goes beyond your resume. Tell your story, your passion for AI/ML, and what drives you. Include a professional headshot.
• Portfolio/Projects: This is critical for AI/ML professionals. Showcase your work with: Case Studies: Detail the problem, your approach (methodologies, algorithms, tools used), the results, and the impact. Explain the business value. Code Repositories (GitHub/GitLab): Link directly to well-documented, clean code for relevant projects. Highlight your contributions. Interactive Demos: If possible, offer live demos of your models or applications. Publications/Presentations: If you have academic papers, conference talks, or webinars, feature them prominently.
• Skills & Technologies: A concise list of your core competencies (e.g., Python, TensorFlow, PyTorch, scikit-learn, AWS SageMaker, specific ML algorithms, data visualization tools).
• Testimonials/Endorsements: Social proof from previous clients or collaborators can significantly boost your credibility.
• Contact Information: Make it easy for people to reach you. Practical Tip: Use platforms like GitHub Pages, Netlify, or even WordPress with a portfolio theme to quickly set up a professional site without needing extensive web development skills. Focus on content quality and clarity. ### 2. LinkedIn: The Professional Network Hub LinkedIn is non-negotiable for AI/ML professionals. It's often the first place recruiters, clients, and peers look. * Headline: Your brand statement in action. Be specific (e.g., "AI Architect | NLP Specialist | Building Intelligent Systems for [Industry]").
• About Section: Expand on your brand statement, highlighting your expertise, experience, and career aspirations. Use keywords relevant to your niche.
• Experience: Go beyond job titles. Describe your achievements, using quantifiable metrics where possible (e.g., "Developed an ML model that improved prediction accuracy by 15%").
• Skills & Endorsements: Curate your skills. Seek endorsements from colleagues and managers.
• Recommendations: Request recommendations from people you've worked with.
• Activity & Engagement: Share relevant articles, comment thoughtfully on posts from industry leaders, and contribute to relevant groups. This showcases your thought leadership.
• LinkedIn Learning Certificates: If you've completed relevant courses, showcase them to demonstrate continuous learning. ### 3. GitHub/GitLab: Your Coding Credibility For AI/ML professionals, a well-maintained GitHub profile is a powerful testament to your technical abilities. * Pinned Repositories: Showcase your best work – projects that demonstrate your core skills and interests.
• README Files: Ensure every project has a clear, descriptive README.md file explaining the project's purpose, technologies used, how to run it, and results.
• Contribution Graph: A consistent contribution graph signals ongoing engagement and activity.
• Open Source Contributions: Participating in open-source projects demonstrates collaboration skills and a willingness to contribute to the community.
• Gist/Notebooks: Share useful code snippets, tutorials, or Jupyter notebooks that demonstrate practical applications of AI/ML concepts. ### 4. Specialized Platforms & Communities Beyond the general platforms, engage where your target audience congregates. * Kaggle: If you're into data science and predictive modeling, Kaggle competitions and notebooks are excellent for demonstrating skills and learning from others.
• Stack Overflow/AI Stack Exchange: Answering questions and contributing to discussions establishes you as a knowledgeable resource.
• Medium/Towards Data Science: Publishing articles on these platforms positions you as a thought leader.
• Reddit (e.g., r/MachineLearning, r/datascience): Engage in discussions, share insights, and learn from the community.
• Discord/Slack Channels: Join relevant communities (e.g., specific ML framework channels, AI ethics groups). Remember that consistency across all platforms is key. Your profile picture, bio, and overall message should align to create a coherent brand identity. This consistent digital footprint is vital for remote professionals showcasing their abilities to a global audience, whether they're seeking opportunities in Berlin or Seoul. For more tips on setting up your digital presence, check out our blog on remote work essentials. ## Content Creation as Thought Leadership in AI/ML Publishing content is perhaps the most effective way to establish yourself as a thought leader in AI/ML. It allows you to demonstrate your expertise, share your unique perspectives, and attract individuals and organizations interested in your specific niche. Content creation isn't just about showing what you know; it's about showing how you think and solve problems. ### 1. Blogging: Deep Dives and Tutorials A personal blog (or articles on platforms like Medium/Towards Data Science) is an excellent channel. * Tutorials & How-To Guides: Break down complex AI/ML concepts or demonstrate the use of new tools. For example, "A Step-by-Step Guide to Fine-Tuning BERT for Custom Classification" or "Implementing Graph Neural Networks for Fraud Detection."
• Project Walkthroughs: Document your personal projects from conception to completion, sharing insights, challenges, and lessons learned.
• Opinion Pieces: Share your informed opinions on industry trends, ethical considerations in AI, the future of specific ML subfields, or the impact of AI on society.
• Book Reviews/Summaries: Review important AI/ML research papers or books, offering your analysis.
• Explainable AI (XAI) Concepts: Demystify black-box models, exploring techniques like LIME or SHAP. Practical Tip: Aim for clarity and conciseness. Use visuals (code snippets, diagrams, charts) to make complex topics more digestible. Optimize for SEO by using relevant keywords naturally. Consistency is more important than frequency; better to publish one high-quality article a month than sporadic, rushed posts. ### 2. Open-Source Contributions & Projects Contributing to open-source software (OSS) or developing your own OSS projects is a powerful way to demonstrate practical skills and community involvement. * Fixing Bugs: Starting with small bug fixes in popular AI/ML libraries.
• Adding Features: Contributing new functionalities that you think would be valuable.
• Improving Documentation: Clear documentation is crucial in open source, and contributing to it shows attention to detail and communication skills.
• Creating Your Own Tools/Libraries: If you develop a useful utility or library, open-sourcing it demonstrates inventiveness and engineering prowess. OSS contributions are highly valued by many companies, especially those that rely heavily on open-source ecosystems. They reflect not only your coding ability but also your collaboration skills and your commitment to the broader engineering community. Many companies actively look for OSS contributions when hiring for remote roles, acknowledging the self-starting and community-oriented mindset it implies. ### 3. Presentations & Webinars Sharing your knowledge through presentations can significantly amplify your brand. * Local Meetups/Conferences: Speak at AI/ML-focused meetups in cities where you might temporarily reside, or online meetups. Many cities, from Singapore to Amsterdam, have thriving tech communities with regular meetups.
• Webinars/Online Workshops: Host your own or participate as a guest speaker. This allows you to reach a global audience without geographical constraints.
• Internal Company Presentations: If you're currently employed, volunteer to lead knowledge-sharing sessions. Practical Tip: Practice your delivery, keep slides visually appealing and concise, and focus on providing real value to your audience. Record your sessions and share them on your website and social media profiles. ### 4. Podcasts and Videos The rise of audio and video content offers new avenues for branding. * Guest on Podcasts: Reach out to AI/ML podcasts and offer to share your expertise on a specific topic.
• Create Short Explainer Videos: Explain complex concepts in AI/ML in digestible video formats for YouTube or LinkedIn.
• Live Coding Sessions: For engineers and data scientists, live coding or project walkthroughs can be incredibly engaging. The key to successful content creation is to provide value, be consistent, and speak genuinely about your passions within AI/ML. This strategy will draw the right kind of attention to your brand and help you build a respected presence in the community. For more ideas on content strategy, explore our articles on digital marketing for freelancers. ## Networking and Community Engagement for AI/ML Professionals Even in a remote-first world, human connection remains paramount. Networking, especially within the specialized AI/ML community, can open doors to collaboration, mentorship, and new opportunities. For digital nomads, this often means striking a balance between virtual and occasional in-person interactions. ### 1. Virtual Communities and Forums * Slack/Discord Groups: Join active channels dedicated to specific AI/ML topics (e.g., MLOps community, PyTorch developers, AI ethics discussions). Engage thoughtfully, ask questions, and offer help when you can.
• Reddit Subreddits: Participate in discussions on r/MachineLearning, r/datascience, r/learnmachinelearning, etc.
• Kaggle Forums: Beyond competitions, the forums are a great place to learn and connect with fellow data scientists.
• Specialized AI/ML Conferences (Virtual & Hybrid): Events like NeurIPS, ICML, CVPR, or KDD often have online components that facilitate networking. Attend presentations, engage in Q&A, and participate in virtual social mixers. Practical Tip: Don't just lurk. Contribute value. Sharing an insightful article, clarifying a concept, or offering a helpful solution will get you noticed much more than simply consuming content. ### 2. Professional Associations and Meetups * IEEE, ACM, AAAI: Membership in these organizations can provide access to exclusive resources, publications, and networking events (some of which are virtual).
• Local AI/ML Meetups (Post-Pandemic & Hybrid): While you might be a digital nomad, when you settle in a city, even temporarily, seek out local meetups. Many global cities, whether it’s Taipei or Barcelona, boast vibrant tech meetup scenes. This offers a rare opportunity for in-person connection.
• University Alumni Networks: If you graduated from a university with a strong AI/ML program, its alumni network. ### 3. Strategic Connections on LinkedIn * Follow Industry Leaders: Stay updated on their thoughts and engage with their posts.
• Connect with Purpose: When sending connection requests, personalize them. Mention a shared interest, a recent article they wrote, or how you admire their work. Avoid generic connection requests.
• Informational Interviews: Reach out to professionals whose careers you admire. Ask for a brief virtual coffee chat to learn about their path and insights. Be respectful of their time and come prepared with thoughtful questions.
• Join LinkedIn Groups: Participate in relevant AI/ML groups, sharing insights and asking questions. ### 4. Mentorship and Collaboration * Seek Mentors: Find experienced professionals in your niche who can offer guidance. A strong mentor can significantly accelerate your career development.
• Offer Mentorship: If you're more experienced, consider mentoring junior AI/ML professionals. This reinforces your own learning and establishes you as a leader.
• Collaborate on Projects: Look for opportunities to work with others on open-source projects, research papers, or even small side projects. Collaboration helps you learn new skills and expands your network organically. Effective networking is about building genuine relationships, not just collecting contacts. Focus on providing value, being authentic, and nurturing connections over time. For remote workers, this skill is especially crucial as it replaces many of the casual interactions that happen in a traditional office setting. Discover more about building professional relationships in our section on networking. ## Showcasing Your Projects: The AI/ML Portfolio Imperative In AI/ML, "show, don't just tell" is a golden rule. Your portfolio of projects is tangible proof of your skills and problem-solving abilities. It's often more impactful than any certification or resume bullet point, especially for roles that demand practical application. ### What Makes an Excellent AI/ML Project Portfolio? 1. Variety and Depth: Variety of Techniques: Showcase projects using different ML algorithms (e.g., supervised, unsupervised, reinforcement learning), data types (images, text, time-series), and tools. Depth of Understanding: Go beyond simple tutorials. Demonstrate your ability to preprocess data, choose appropriate models, tune hyperparameters, evaluate performance, and interpret results.

2. Real-World Relevance (or Problem-Solving Focus): Impact-Oriented: Frame your projects around solving a specific problem or achieving a particular goal. Even if it's a personal project, think about its potential application. Business Context (if applicable): If you've worked on industry projects, articulate the business problem, your role, and the measurable impact of your solution.

3. Clean Code and Documentation: GitHub/GitLab: Host your projects on version control platforms. Well-Commented Code: Ensure your code is clean, readable, and well-commented. Detailed README: Every project should have a `README.md` explaining: Project objective and problem it solves. Data sources used. Methodology (algorithms, models, tools). Key findings and results (visualizations are great). How to replicate or run the project. * Future improvements or next steps.

4. Demonstrations and Visualizations: Jupyter Notebooks: Use notebooks for exploratory data analysis, model training, and result visualization. Share them clean and organized. Interactive Demos: If feasible, deploy a simple web application (using Streamlit, Flask, or even a Google Colab link) to demonstrate your model in action. This is incredibly powerful. * Visualizations: Use charts, graphs, and images to explain data patterns, model performance, and insights.

5. Storytelling: Narrative Flow: Each project should tell a story: the challenge, your process, the solution, and the impact. Personal Contributions: Clearly articulate your specific role and contributions, especially in team projects. ### Examples of Compelling AI/ML Projects for a Portfolio: * NLP Text Classifier: A model that classifies customer reviews, news articles, or social media posts into categories, complete with a detailed explanation of tokenization, embeddings (e.g., word2vec, BERT), model architecture, and performance metrics.

• Computer Vision Object Detector: A project detecting specific objects in images or video streams, with a focus on annotation, model training (e.g., YOLO, Faster R-CNN), and deployment considerations.
• Time-Series Forecasting: Predicting stock prices, energy consumption, or sales figures using ARIMA, Prophet, or deep learning architectures like LSTMs, emphasizing feature engineering and evaluation metrics.
• Recommendation System: Building a system based on collaborative filtering or content-based filtering, demonstrating data processing, model implementation, and evaluation.
• Reinforcement Learning Agent: Training an agent to play a simple game (e.g., OpenAI Gym environments), showcasing exploration/exploitation, reward functions, and policy optimization. Practical Tip: Curate your portfolio. Don't include every single project you've ever done. Choose your best 3-5 projects that showcase a diverse range of skills and align with your brand's niche. Regularly update your portfolio as you complete new work or refine existing projects. This continuous improvement reflects your dedication to the craft. Building a strong portfolio is particularly helpful for securing the kinds of project-based work often found on platforms catering to freelancers. ## Continuous Learning and Adapting Your Brand The AI/ML is in perpetual motion. New algorithms, frameworks, and ethical considerations emerge with astonishing speed. For your personal brand to remain vibrant and relevant, continuous learning is not optional; it's a core requirement. Furthermore, your brand itself must be agile, ready to adapt to your evolving skills and the changing demands of the market. ### Strategies for Continuous Learning in AI/ML: 1. Follow Research Papers & Journals: Regularly read papers from top conferences (NeurIPS, ICML, CVPR) and reputable journals. Subscribing to AI/ML newsletters can help you stay on top of new developments.

2. Online Courses and Specializations: Platforms like Coursera, edX, Udacity, and fast.ai offer excellent courses in specialized areas (e.g., "Deep Learning Specialization," "MLOps Engineering"). Certifications from these courses can be added to your LinkedIn profile and website.

3. Books and Technical Guides: Invest in foundational and advanced textbooks. Practical guides and O'Reilly books can be invaluable for gaining in-depth knowledge on specific topics.

4. Experimentation & Personal Projects: The best way to learn is by doing. Apply new techniques to personal projects, even small ones. This helps solidify your understanding and builds your portfolio.

5. Participate in Kaggle Competitions: Push your boundaries by competing in data science challenges. You'll learn from others' approaches and gain practical insights.

6. Attend Webinars and Workshops: Many leading companies and research institutions offer free webinars on new tools, research, or industry applications.

7. Engage with Open-Source Communities: Learn from expert developers and contribute to projects. This exposes you to best practices and advanced coding paradigms. ### Adapting Your Brand to Evolve with the Industry: As you acquire new skills or the industry shifts, your brand needs to reflect these changes. * Revisit Your Value Proposition Annually: Ask yourself: Is my current brand statement still accurate? Am I still solving the problems I want to solve? Are my target clients/employers still the same?

• Update Your Digital Assets: Website/Portfolio: Add new projects, update your bio to reflect new expertise. LinkedIn: Update your headline, about section, and skills. Remove outdated information. * GitHub: Showcase your most recent and relevant work.
• Strategic Niche Pivot: If you decide to specialize in a new area (e.g., from general data science to "AI for Sustainable Energy"), gradually adjust your content creation, networking, and project focus to align with this new niche. Don't make a sudden, jarring shift, but rather a gradual evolution.
• Solicit Feedback: Ask trusted colleagues or mentors for feedback on your brand. Is your message clear? Does it accurately represent you? Real-world Example: Consider a professional who started as a pure "Machine Learning Engineer" focused on predictive modeling. With the rise of Large Language Models (LLMs), they might decide to specialize in "Generative AI Solutions Architect." Their continuous learning would involve deep dives into transformer architectures, LLM fine-tuning techniques, and prompt engineering. Their brand adaptation would involve updating their portfolio with LLM-based projects, writing articles on prompt engineering best practices, and networking with individuals working on generative AI applications. This proactive adaptation ensures continued relevance and demand for their skills. Embracing continuous learning also makes you a more attractive candidate for full-time remote jobs. ## Ethical AI and Responsible Development: A Core Brand Pillar In the realm of AI and Machine Learning, technical prowess alone is no longer enough. The growing awareness of AI's societal impact, from algorithmic bias to data privacy issues, has made ethical AI and responsible development a critical concern. For professionals in this field, integrating these principles into their personal brand is not just a moral imperative but also a significant competitive advantage. It demonstrates foresight, maturity, and a commitment to building AI that benefits humanity. ### Why Ethical AI Matters for Your Brand: * Trust and Credibility: Clients and employers are increasingly seeking individuals who understand and prioritize the ethical implications of their work. A brand that champions responsible AI builds trust.
• Risk Mitigation: Proactive consideration of ethical risks (e.g., bias, privacy, security) can prevent costly mistakes and reputational damage.
• Industry Leadership: Professionals who actively engage with and contribute to discussions around ethical AI are seen as thought leaders shaping the future of the field, whether in London or Silicon Valley.
• Alignment with Values: For many, working on meaningful projects that align with their values is paramount. A strong ethical brand attracts like-minded collaborators and opportunities.
• Regulatory Compliance: As governments worldwide introduce AI regulations (e.g., EU AI Act), professionals who understand these frameworks become highly valued assets. ### How to Integrate Ethical AI into Your Brand: 1. Educate Yourself: Learn the Principles: Familiarize yourself with widely accepted ethical AI principles (e.g., fairness, accountability, transparency, privacy, safety). Study Frameworks: Explore frameworks for ethical AI development, such as those from NIST, Google, or IBM. Understand Bias: Deepen your understanding of different types of algorithmic bias and mitigation techniques (e.g., fairness metrics, debiasing methods). 2. Showcase Your Commitment in Your Portfolio/Projects: Explainable AI (XAI): Highlight projects where you've focused on making models more interpretable. Discuss how you've used techniques like LIME, SHAP, or feature importance. Bias Detection & Mitigation: Describe steps taken to identify and reduce bias in your datasets or models. Quantify the improvement in fairness metrics. Privacy-Preserving ML: If applicable, showcase experience with techniques like differential privacy or federated learning. Security of ML Systems: Discuss considerations for adversarial attacks and model robustness. Data Governance: Highlight your understanding of data provenance, consent, and secure data handling practices. 3. Content Creation and Thought Leadership: Blog Posts/Articles: Write about ethical dilemmas in AI, responsible data practices, the importance of diverse datasets, or critiques of biased AI systems. Presentations/Webinars: Speak at conferences or online events about responsible AI development. Open Discussions: Engage in nuanced discussions on LinkedIn or specialized forums about the ethical implications of new AI technologies (e.g., generative AI). 4. Certifications and Courses: Pursue certifications or courses specifically focused on AI ethics, responsible data science, or privacy regulations (e.g., GDPR, CCPA). Mention these on your professional profiles. 5. Connect with Ethical AI Experts: Follow and engage with leading thinkers and organizations in the ethical AI space. This demonstrates your vested interest and continuous learning. Practical Tip: Don't just pay lip service to ethical AI. Demonstrate genuine understanding and concrete actions. For instance, in a project description, instead of just saying "we ensured fairness," explain how you defined fairness for that specific problem, what metrics you used, and what mitigation strategies you implemented. This depth makes your commitment authentic and powerful, distinguishing you as a truly responsible AI practitioner. Our resources on sustainable remote work also often touch upon ethical considerations in tech. ## Managing Your Brand's Reputation and Feedback Building a strong personal brand is an ongoing process, and a significant part of it involves actively managing your reputation and being receptive to feedback. For AI/ML professionals, especially those working remotely or independently, your digital reputation precedes you. Negative sentiment, even if unfounded, can have a disproportionate impact. ### 1. Proactive Monitoring of Your Digital Footprint: * Regular Self-Audits: Periodically Google yourself. Check your LinkedIn profile, GitHub, personal website, and any other public platforms where you're active. Is the information current? Is it consistent? Does it reflect your desired brand message?
• Social Media Privacy Settings: Ensure your personal social media accounts are appropriately private, or at least professional enough to not contradict your AI/ML brand.
• Alerts: Set up Google Alerts for your name to be notified if you're mentioned online. ### 2. Seeking and Responding to Feedback: * Formal Feedback (e.g., Performance Reviews): If you're employed, take performance reviews seriously. Identify areas for improvement in both your technical skills and soft skills (communication, collaboration).
• Informal Feedback (from Peers/Mentors): Ask trusted colleagues, mentors, or even close friends for honest feedback on your professional image and communication style. "How do you perceive my expertise in [X]?" or "Is my website clear and compelling?"
• Client Feedback: For independent consultants, actively solicit feedback from clients at the end of projects. Use this to refine your service offering and client communication.
• Online Interactions: Pay attention to comments on your blog posts, social media updates, or GitHub issues. Respond professionally and constructively, even to critical feedback. ### 3. Handling Criticism and Disagreements Professionally: In technical fields like AI/ML, disagreements over methodology, library choices, or ethical approaches are common. How you handle these can significantly impact your brand. * Maintain Professionalism: Always respond respectfully, even if you disagree. Avoid emotional reactions or personal attacks.
• Focus on the Data/Logic: Base your arguments on evidence, research, and sound technical reasoning.
• Acknowledge Valid Points: If a critic raises a valid issue, acknowledge it gracefully. "That's a fair point about the computational cost; I hadn't considered that optimization fully."
• Learn and Improve: View criticism as an opportunity to learn and refine your understanding or approach.
• Know When to Disengage: If a conversation turns unproductive or becomes overly emotional, it's wise to disengage gracefully. ### 4. Leveraging Testimonials and Endorsements: * Request Recommendations: Proactively ask former managers, colleagues, or clients for recommendations on LinkedIn or testimonials for your website. Specific, detailed recommendations are most effective. For instance, a glowing testimonial about your work on a specific image recognition project in Tokyo will carry a lot of weight.