MLOps Training - Machine Learning Operations Training
                     Simple Ways to Start 
Your First MLOps 
PStreiamplinee Yoluir nMacehine Learning Workflow from Development to 
Deployment
What is MLOps?
Definition Goal Why it Matters
MLOps (Machine Learning Automate and streamline the Improves model reliability, 
Operations) combines ML, ML model lifecycle, from data scalability, and significantly 
DevOps, and data to deployment and speeds up deployment 
engineering to manage the monitoring. processes.
entire ML lifecycle.
Core Components of an MLOps Pipeline
Data Ingestion & Processing
Collecting and preparing raw data for model training.
Model Training & Evaluation
Developing and validating machine learning models.
Model Versioning
Tracking and managing different iterations of models and data.
CI/CD for ML Models
Automating integration and deployment of ML models.
Monitoring & Retraining
Observing model performance in production and triggering updates.
Step 1 - Start with a Simple Dataset
Begin your MLOps journey with publicly available datasets 
that are easy to understand and manage.
• Popular Choices: Iris, Titanic, or MNIST datasets are 
excellent starting points.
• Preprocessing: Perform essential steps like handling 
missing values, scaling numerical features, and encoding 
categorical variables.
• Key Tools: Leverage powerful Python libraries such as 
Pandas for data manipulation, NumPy for numerical 
operations, and scikit-learn for various preprocessing 
tasks.
Step 2 - Build & Train Your Model
Model Selection Training & Evaluation Model Saving
Opt for straightforward machine Train your chosen model and Persist your trained models 
learning models like Logistic evaluate its performance using using serialization libraries like 
Regression or Random Forest for standard metrics such as joblib or pickle for later use and 
your initial projects. accuracy or F1-score. deployment.
Step 3 - Use Version Control & Git
Effective version control is crucial for managing your codebase and models.
• Repository Setup: Initialize a Git repository for your project.
• Commit Regularly: Commit your changes frequently with clear, meaningful messages describing each update.
• Structured Organization: Maintain a well-organized project structure with dedicated folders:
• /data: For raw and processed datasets.
• /models: For trained and versioned models.
• /scripts: For training, evaluation, and deployment scripts.
• /notebooks: For exploratory data analysis and development notebooks.
• Key Tools: Utilize popular platforms like GitHub, GitLab, or Bitbucket to host and manage your repositories.
Step 4 - Deploy with Simple Tools
After training, the next step is to make your model accessible 
for predictions.
• Local Deployment: Use lightweight web frameworks like 
Flask or FastAPI to create a simple API wrapper around 
• yCoounrt maiondeerli.zation: Package your application and its 
dependencies into a Docker container for consistent 
environments.
• Cloud Demos: For quick demonstrations and sharing, 
consider platforms like Streamlit or Google Colab, which 
simplify web app deployment.
Final Step - Monitor & Improve
Performance Monitoring Retraining Strategy Beginner-Friendly Tools
Continuously monitor your Establish a plan for manual or Explore tools like MLflow for 
model's performance in scheduled retraining, ensuring experiment tracking, DVC for 
production and log feedback your model adapts to new data data versioning, or Google 
data to detect degradation. and maintains accuracy. Cloud's Vertex AI for an 
integrated platform.
Contact
Flat no: 205, 2nd Floor, NILGIRI Block, Aditya Enclave, Ameerpet, Hyderabad-16
Mobile No: +91 7032290546
E-Mail Id : [email protected]
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