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Generative AI is revolutionizing industries, and understanding it is essential for aspiring AI professionals. Cracking an interview requires deep knowledge of Gen AI’s capabilities and workings.
Here are some key areas to focus on:
- Fundamentals: What are Generative Models? How do they differ from Discriminative Models?
- Architectures: Explain GPT, BERT, and other transformer-based models.
- Training and Fine-tuning: What is transfer learning? How do you fine-tune a large language model?
- Applications: How can Gen AI transform industries like healthcare, finance, and e-commerce?
- Ethics: What are the risks and ethical concerns surrounding generative AI?
Prepare to demonstrate your understanding of algorithms, frameworks, and real-world applications! Master these areas to stand out as a Gen AI professional.
You Should Know:
1. Working with GPT Models
To interact with OpenAI’s GPT models, use the following Python code:
import openai openai.api_key = 'your-api-key' response = openai.Completion.create( engine="text-davinci-003", prompt="Explain how GPT-3 works.", max_tokens=150 ) print(response.choices[bash].text)
2. Fine-Tuning BERT for Custom Tasks
Use Hugging Face’s `transformers` library to fine-tune BERT:
from transformers import BertTokenizer, BertForSequenceClassification
import torch
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
inputs = tokenizer("Hello, world!", return_tensors="pt")
labels = torch.tensor([bash]).unsqueeze(0)
outputs = model(inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits
3. Ethical AI: Detecting Bias in Datasets
Use `AI Fairness 360` toolkit to check bias:
pip install aif360
from aif360.datasets import BinaryLabelDataset
from aif360.metrics import BinaryLabelDatasetMetric
dataset = BinaryLabelDataset(df=your_dataframe, label_names=['target'], protected_attribute_names=['gender'])
metric = BinaryLabelDatasetMetric(dataset, unprivileged_groups=[{'gender': 0}], privileged_groups=[{'gender': 1}])
print("Disparate Impact:", metric.disparate_impact())
4. Deploying AI Models with Flask
Create a simple API for your AI model:
from flask import Flask, request, jsonify
import torch
app = Flask(<strong>name</strong>)
@app.route('/predict', methods=['POST'])
def predict():
data = request.json
inputs = tokenizer(data['text'], return_tensors="pt")
outputs = model(inputs)
return jsonify({"prediction": str(torch.argmax(outputs.logits))})
if <strong>name</strong> == '<strong>main</strong>':
app.run(host='0.0.0.0', port=5000)
5. Linux Commands for AI Workflows
- Monitor GPU usage:
nvidia-smi
- Train models in the background:
nohup python train.py &
- Kill a process using GPU:
kill -9 $(ps aux | grep 'python' | awk '{print $2}')
What Undercode Say:
Generative AI is reshaping technology, and mastering it requires hands-on practice. From fine-tuning transformer models to deploying ethical AI solutions, the key lies in continuous experimentation. Use Linux commands to optimize workflows, Python libraries like `transformers` and `openai` for model interactions, and always validate datasets for biases. The future of AI depends on responsible innovation—stay curious, keep coding, and push boundaries.
Expected Output:
A structured guide on Gen AI interview prep with practical code snippets, ethical considerations, and deployment techniques.
References:
Reported By: Habib Shaikh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
