5. Introduction to MLOps | Issues with Traditional ML Approach | Understanding MLOps(Theory Part)

Welcome to Eliff Academy’s in-depth series on MLOps Fundamentals! This video introduces you to the essentials of MLOps, exploring the concept, its significance, and how it addresses key challenges in machine learning. Whether you're a fresh graduate, IT professional, software developer, DevOps engineer, or AI/ML expert, this video offers valuable insights to help you understand and apply MLOps in real-world projects.

Topic Covered in This Video:

Why Do We Need MLOps?

Now that we have an introduction to MLOps, let’s explore why MLOps is necessary and how it addresses the limitations of traditional machine learning (ML) processes.

Traditional Machine Learning Lifecycle
In a typical ML project, the journey begins with the following steps:

Business Understanding and Planning

Define project objectives and business requirements.
Outline specific goals, such as recommendation engines, fraud detection, or chatbots.
Data scientists and subject matter experts work together to determine how the model can add value.
Data Acquisition

Gather data from diverse sources, including databases, web applications, mobile devices, or files.
Ensure sufficient data quality and quantity, as this directly impacts model accuracy.
Data Preparation

Clean and transform raw data to make it ready for modeling.
Address issues like missing values, duplicates, and irrelevant information.
This phase is iterative, requiring multiple rounds of refinement to create a high-quality dataset.
Modeling

Select features, algorithms, train models, and fine-tune parameters.
This phase is highly experimental and iterative. If the model’s accuracy is insufficient, data scientists may:
Retrain the model.
Choose a new algorithm.
Acquire additional or new data for improved performance.
Deployment

Integrate the final model into production, either within an existing application or a new one, to start making real-time predictions.
Deployment allows the model to add business value by providing actionable insights.
Monitoring and Maintenance

Track model performance and address any decline in accuracy over time due to data pattern changes (data drift).
Retrain the model periodically as new data becomes available to maintain accuracy.
Issues with the Traditional ML Approach
Despite completing these steps, traditional ML pipelines often struggle to remain efficient and accurate in production environments. Key challenges include:

Time-Consuming Retraining: Models often lose accuracy over time as data patterns change, requiring frequent retraining to stay relevant.
Limited Collaboration: Data scientists, engineers, and operational teams typically work in isolation, leading to integration difficulties and slower workflows.
Complex Model Maintenance: Without a standardized process, managing and updating models becomes challenging, resulting in inconsistent performance.
Why MLOps is Needed
This is where MLOps makes a difference. By providing a structured framework, MLOps helps address these challenges with practices that encourage collaboration, standardization, and continuous maintenance. MLOps enables:

Efficient Model Management: Simplifies continuous integration, delivery, and retraining processes.
Enhanced Collaboration: Provides a common framework for data scientists, engineers, and operations to work together seamlessly.
Scalable and Reliable Models: Ensures models stay accurate and adaptable as data patterns change over time.
This overview of the traditional ML lifecycle and its limitations lays the foundation for understanding the value MLOps brings. In the upcoming lessons, we’ll dive into how MLOps principles enhance these processes, ensuring ML projects stay production-ready and efficient.

In the upcoming videos, we’ll dive into hands-on exercises to help you apply these concepts in practice. You’ll get step-by-step guidance on setting up pipelines, deploying models, and implementing MLOps tools.

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