Lilly Tech Systems
AI Best Practices
Practical guidance for getting started with AI, building projects, advancing your career, and staying current in this rapidly evolving field.
Getting Started with AI
Build a Foundation
Learn Python programming, basic statistics, and linear algebra. These are the prerequisites for all AI work.
Learn Machine Learning Basics
Understand supervised and unsupervised learning, common algorithms, and evaluation metrics.
Study Deep Learning
Learn neural networks, CNNs, RNNs, and transformers. Start with frameworks like PyTorch or TensorFlow.
Build Projects
Apply what you learn by building real projects. Start small and increase complexity over time.
Specialize
Choose a focus area: NLP, Computer Vision, Reinforcement Learning, or AI Safety.
Recommended Learning Path
| Stage | Topics | Resources |
|---|---|---|
| Beginner | Python, NumPy, Pandas, basic ML | AI School courses, Kaggle Learn, fast.ai |
| Intermediate | Deep learning, NLP, Computer Vision | Stanford CS229/CS231n, deeplearning.ai |
| Advanced | Research papers, specialized topics, deployment | arXiv, conference proceedings, open-source projects |
Building AI Projects
- Start with a problem: Do not start with a technique and look for a problem. Start with a real problem and find the right approach.
- Use pretrained models: For most practical applications, fine-tuning pretrained models is more effective than training from scratch.
- Iterate quickly: Build a minimum viable model first, then improve it based on real results.
- Version everything: Track data, code, models, and experiments with tools like Git, DVC, and MLflow.
- Test thoroughly: Evaluate on diverse, representative test sets. Watch for data leakage and overfitting.
Staying Current with Research
- Papers: Follow arXiv (cs.AI, cs.CL, cs.CV, cs.LG sections), Papers With Code
- Newsletters: The Batch (deeplearning.ai), Import AI, AI Weekly
- Communities: Hugging Face community, Reddit (r/MachineLearning), Twitter/X AI community
- Conferences: NeurIPS, ICML, ICLR, ACL, CVPR, AAAI
AI Career Paths
| Role | Focus | Key Skills |
|---|---|---|
| ML Engineer | Building and deploying ML systems | Python, PyTorch/TF, MLOps, cloud platforms |
| Data Scientist | Extracting insights from data | Statistics, ML, SQL, visualization, communication |
| AI Researcher | Advancing AI capabilities | Math, deep learning, paper writing, experimentation |
| AI Product Manager | Defining AI product strategy | AI literacy, product management, user research |
| AI Ethics Specialist | Ensuring responsible AI | Ethics, policy, fairness metrics, bias auditing |
| Prompt Engineer | Optimizing AI model outputs | LLM knowledge, experimentation, writing |
Ethical Considerations
- Always consider the potential negative impacts of your AI system
- Test for bias across different demographic groups
- Be transparent about what your AI can and cannot do
- Implement human oversight for consequential decisions
- Respect user privacy and comply with regulations
- Document your model's limitations and failure modes
Frequently Asked Questions
No. While a PhD is valuable for research roles, many AI engineering and applied roles prioritize practical skills and portfolio projects. Strong programming skills, understanding of ML fundamentals, and demonstrated project work can open many doors.
Python is the dominant language for AI and ML. It has the richest ecosystem of libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face). JavaScript is increasingly relevant for deploying AI in web applications. Rust and C++ are used for performance-critical AI infrastructure.
AI is transforming software development but is unlikely to fully replace programmers in the near term. AI tools like Copilot and Claude Code augment developers by handling routine coding tasks, but human judgment, system design, and problem-solving remain essential. The role of a programmer is evolving toward higher-level thinking and AI collaboration.
Work on projects that interest you personally. Join communities (Kaggle, Hugging Face, local meetups). Set small, achievable goals. Do not try to learn everything at once — focus on one area at a time. Remember that even experts are constantly learning in this rapidly evolving field.
For learning and small projects, any modern computer works. For training models, you will want a GPU (NVIDIA RTX 3060 or better). For larger projects, use cloud GPU services (Google Colab free tier, AWS, GCP, Lambda Labs). Many modern workflows use pretrained models that require minimal compute for inference.