Essential Resources for Data Science, MLOps, and AI Engineering

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Shirin Khosravi Jam’s curated list of resources provides a structured path for transitioning from Data Science to MLOps and AI Agents. Below are the key resources with actionable steps and commands to reinforce learning.

📊 Data Science Foundations

  1. ISLR
     – <a href="https://lnkd.in/djGPVVwJ">Link</a> </li>
</ol>

- Practice R code for statistical learning: 
[bash]
library(ISLR)
data(Wage)
lm.fit <- lm(wage ~ age + education, data=Wage)
summary(lm.fit)

    2. Practical Statistics for Data Science

     – <a href="https://lnkd.in/dBWucpRX">Link</a> 
- Python example for hypothesis testing: 
[bash]
import scipy.stats as stats
t_stat, p_val = stats.ttest_ind(group1, group2)

    3. Hands-On ML with TensorFlow & Keras

     – <a href="https://lnkd.in/dWrf5pbS">Link</a> 
- Train a simple neural network: 
[bash]
model = tf.keras.Sequential([tf.keras.layers.Dense(10, activation='relu')])
model.compile(optimizer='adam', loss='mse')
model.fit(X_train, y_train, epochs=10)

    ⚙️ MLOps & End-to-End Systems

    1. Designing Machine Learning Systems
       – <a href="https://lnkd.in/dY8NJMRk">Link</a> </li>
</ol>

- Dockerize an ML model: 
[bash]
FROM python:3.8
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY app.py .
CMD ["python", "app.py"]

      2. AWS Cloud Practitioner

       – <a href="https://lnkd.in/dA2wuP44">Link</a> 
- AWS CLI setup: 
[bash]
aws configure
aws s3 ls

      3. AWS ML Specialty

       – <a href="https://lnkd.in/dzjVT3ZX">Link</a> 
- Deploy a SageMaker model: 
[bash]
import sagemaker
predictor = sagemaker.deploy(initial_instance_count=1, instance_type='ml.m5.large')

      🧠 LLMs + AI Agents + RAG

      1. Hands-On LLMs
         – <a href="https://lnkd.in/dVmn83XB">Link</a> </li>
</ol>

- Run a local LLM with Ollama: 
[bash]
ollama pull llama3
ollama run llama3 "Explain RAG"

        2. Awesome GenAI Projects

         – <a href="https://lnkd.in/dtFTZsCs">Link</a> 
- Clone and run a LangChain project: 
[bash]
git clone https://github.com/awesome-genai-project
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt

        3. RAG Techniques

         – <a href="https://lnkd.in/dD4S8Cq2">Link</a> 
- Ingest documents into a vector DB: 
[bash]
from langchain_community.vectorstores import FAISS
db = FAISS.from_documents(docs, embeddings)

        🛠️ AI Engineering

        1. AI Engineering
           – <a href="https://lnkd.in/dqwDjHVa">Link</a> </li>
</ol>

- Monitor model performance with Prometheus: 
[bash]
 prometheus.yml
scrape_configs:
- job_name: 'model_metrics'
static_configs:
- targets: ['localhost:8000']

          You Should Know:

          • Linux commands for MLOps:
            ps aux | grep python  Find running ML processes
df -h  Check disk space for large datasets
          • Windows PowerShell for AI:
            Get-Process | Where-Object { $_.CPU -gt 50 }  Monitor resource-heavy tasks

          What Undercode Say:

          The shift from theory to production requires hands-on experimentation. Use Docker for reproducibility, AWS for scalability, and LangChain for LLM workflows. Always validate models with real-world data before deployment.

          Prediction:

          AI engineering will increasingly merge with DevOps, requiring skills in containerization, cloud orchestration, and real-time monitoring.

          Expected Output:

          A structured learning path with executable code snippets for immediate application.

          URLs embedded for direct access to resources.

          IT/Security Reporter URL:

          Reported By: Shirin Khosravi – Hackers Feeds
          Extra Hub: Undercode MoN
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