Welcome, future AI engineers of THD/DIT! In your journey, beyond algorithms and data, you'll find that how you work is just as important as what you work on. Let's dive into the workflows of enterprise AI development.
- What does "enterprise" mean in AI development? 🤖
- In the world of AI, "enterprise" typically refers to large-scale, structured development processes often seen in bigger companies or significant projects. It involves not just creating AI models, but ensuring they're reliable, scalable, and integrated seamlessly into applications or services. The emphasis here is on collaboration, robustness, and maintaining AI solutions at scale.
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Issue trackers: 📋
- These are essential tools in any development environment. They help you report, track, and resolve problems or tasks. Imagine you're training an AI model, and it's not giving the expected output. With an issue tracker, you can log this problem, ensuring it gets addressed.
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Code reviews: 🔍
- Once you've written your code or developed your AI model, it's crucial to have peers review it. This process ensures quality, facilitates learning, and encourages a collaborative spirit. Remember, two eyes are good, but multiple eyes are even better, especially when it comes to complex AI algorithms.
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Roles in an AI team: 🧠
- AI development isn't a one-person job. Typically, there's a mix of roles like:
- Data Engineers: Manage and preprocess data.
- Machine Learning Engineers: Design, implement, and deploy ML models.
- Research Scientists: Dive deep into advanced algorithms and techniques.
- AI Product Managers: Oversee the product's lifecycle and ensure its alignment with business goals.
- QA Engineers: Test the models and ensure they meet the required standards.
- Understanding and respecting each role is key to a harmonious and productive AI team.
- AI development isn't a one-person job. Typically, there's a mix of roles like:
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Communication: 🗣️
- Discussing work with your team is paramount. Regular check-ins, meetings, and open channels of communication ensure that everyone is aligned. When developing AI solutions, clarity is crucial. Misunderstandings can lead to models behaving unpredictably. So, always speak up, ask questions, and share insights!
- "The Art of Enterprise Naming Patterns: Optimizing Commits and Branching"
- "Planning next to your code - ..."
- "How we’re using projects to build projects"
Remember, while the technical bits are fascinating, the collaborative spirit, understanding of workflows, and effective communication are what truly drive successful AI projects. Embrace the journey at THD/DIT and happy learning! 🚀📚