As Bilginç IT Academy, we attach importance to guiding you in the right way with our experience of nearly 30 years in the IT training sector. On our blog page, we often examine the technologies that will be trending in the near future, and we inform you so that you can benefit from these technologies in the right way.
In this article, we will provide information about MLOps, which will increase in popularity in the coming years. As we are coming towards the end of 2023, when artificial intelligence has become almost a necessity for all businesses, we can say that it is the right time for you to learn MLOps. So let's get started!
MLOps, or Machine Learning Operations, is an emerging discipline that amalgamates Machine Learning (ML) with the principles and practices of DevOps. In essence, MLOps is about streamlining and optimizing the process of developing, deploying, and maintaining machine learning models in production environments. It's a set of practices that aims to automate and improve the lifecycle of machine learning models, ensuring that they are not only accurate but also scalable, maintainable, and seamlessly integrated into broader business processes.
MLOps addresses several critical needs in the machine learning landscape:
Efficiency in Model Development and Deployment: Traditional machine learning workflows often involve a significant amount of manual work, from data preparation to model training and deployment. This can be time-consuming and error-prone. MLOps introduces automation, which accelerates these processes, reduces errors, and frees up data scientists to focus on more complex tasks.
Model Scalability and Reliability: As organizations increasingly rely on machine learning models to make critical decisions, the ability to scale these models and ensure their reliability becomes paramount. MLOps provides the framework and tools to manage the lifecycle of models effectively, ensuring they can handle increased workloads and maintain performance.
Reproducibility and Version Control: Machine learning projects involve numerous experiments with different data sets, parameters, and algorithms. MLOps ensures that each experiment is tracked, and models are version-controlled, similar to how software code is managed. This aids in reproducing results and understanding the evolution of models over time.
Continuous Improvement: The world of data is dynamic, and models can quickly become outdated. MLOps facilitates continuous monitoring and improvement of models, ensuring they adapt to new data and remain effective over time.
Collaboration and Standardization: MLOps fosters collaboration between data scientists, ML engineers, and IT professionals. By standardizing processes and tools, teams can work more effectively together, leading to better outcomes.
Regulatory Compliance and Governance: In many industries, machine learning models must comply with stringent regulations. MLOps provides the necessary infrastructure for governance, auditing, and ensuring that models meet regulatory standards.
Cost Optimization: By streamlining the ML lifecycle, MLOps can significantly reduce costs associated with model development, deployment, and maintenance. Efficient resource utilization and reduced downtime translate into substantial cost savings.
In conclusion, MLOps is not just a buzzword; it's a critical component of a successful machine learning strategy. By encompassing these nine stages, MLOps bridges the gap between the promise of ML and its practical, scalable application in real-world scenarios. As organizations increasingly rely on data-driven decision-making, MLOps stands as the cornerstone that ensures machine learning initiatives are sustainable, effective, and aligned with business objectives.
While it's often thought that machine learning models are the exclusive domain of data scientists, this perspective overlooks the broader impact and importance of robust MLOps processes and systems. In reality, MLOps is a critical and integral component of any enterprise AI strategy, extending its influence far beyond the confines of data science teams. It touches and transforms the work of everyone involved in, or reaping the benefits from, the machine learning model lifecycle. From IT professionals and ML engineers to business analysts and end-users, MLOps serves as a unifying force, ensuring that machine learning initiatives are not just scientifically sound but also operationally efficient and strategically aligned with organizational goals.
|Role||Role in ML Model Life Cycle||MLOps Requirements|
Subject matter experts
|- Provide business questions, goals, or KPIs around which ML models should be framed.|
- Continually evaluate and ensure that model performance aligns with or resolves the initial need.
- Easy way to understand deployed model performance in business terms.
- Mechanism or feedback loop for flagging model results that don’t align with business expectations.
- Build models that address the business question or needs brought by subject matter experts.
- Deliver operationalizable models so that they can be properly used in the production environment and with production data.
- Assess model quality (of both original and tests) in tandem with subject matter experts to ensure they answer initial business questions or needs.
- Automated model packaging and delivery for quick and easy (yet safe) deployment to production.
- Ability to develop tests to determine the quality of deployed models and to make continual improvements.
- Visibility into the performance of all deployed models from one central location.
- Ability to investigate data pipelines of each model to make quick assessments and adjustments.
|Data Engineers||- Optimize the retrieval and use of data to power ML models|
- Visibility into performance of all deployed models.
- Ability to see the full details of individual data pipelines to address underlying data plumbing issues
- Integrate ML models in the company’s applications and systems.
- Ensure that ML models work seamlessly with other non-machine-learning-based applications.
- Versioning and automatic tests.
- The ability to work in parallel on the same application
- Conduct and build operational systems and test for security, performance, availability.
- CI/CD pipeline management.
- Seamless integration of MLOps into the larger DevOps strategy of the enterprise.
- Seamless deployment pipeline.
|Model Risk Managers / Auditors|
- Minimize overall risk to the company as a result of ML models in production.
- Ensure compliance with internal and external requirements before pushing ML models to production.
- Robust, likely automated, reporting tools on all models (currently or ever in production), including data lineage.
- Ensure a scalable and flexible environment for ML model pipelines, from design to development and monitoring.
- Introduce new technologies when appropriate that improve ML model performance in production
- High-level overview of models and their resources consumed.
- Ability to drill down into data pipelines to assess and adjust infrastructure needs.
In the section above, we have examined who can achieve what by learning MLOps. If you are in one of the professions in the table but have not yet met MLOps, be sure to read on.
Step 1: Understand the Basics of Machine Learning
Before diving into MLOps, it's crucial to have a solid understanding of machine learning concepts and models. Start with:
Training: Introduction to Machine Learning
Books: Consider reading foundational texts like (Click on the book titles below to download free e-books!)
Step 2: Get Familiar with DevOps Principles
MLOps is deeply rooted in DevOps practices. Familiarize yourself with:
Training: Certified DevOps Foundation
Step 3: Hands-On Practice with Tools and Languages
Familiarize yourself with tools and languages commonly used in MLOps:
Step 4: Enroll in "MLOps Engineering on AWS Training" at Bilginç IT Academy
This course is specifically designed to extend DevOps practices to machine learning. It covers:
Learn more about MLOps - Machine Learning Operations - Amazon Web Services
Step 5: Learn About Data Engineering
Since MLOps heavily involves data handling, understanding data engineering principles is beneficial:
Data Pipelines: Learn how to build and manage data pipelines.
Data Storage and Processing: Understand different data storage solutions and processing frameworks.
Step 6: Dive Into Advanced MLOps Concepts
Explore advanced topics such as:
Model Versioning: Learn about techniques to version and track models.
Model Explainability: Understand methods to interpret and explain model predictions.
Step 7: Hands-On Projects and Case Studies
Apply your knowledge:
Personal Projects: Build and deploy your own ML models using MLOps practices.
Case Studies: Analyze case studies to understand real-world applications of MLOps.
Case Study: Developing a practical MLOps approach to training and optimizing deep learning models
When analyzing case studies, try to understand not just the technical aspects but also the business challenges, solutions, outcomes, and lessons learned. This holistic approach will give you a deeper understanding of the practical applications of MLOps.
Step 8: Continuous Learning and Adaptation
The field of MLOps is rapidly evolving:
Dowload this free e-book: Introducing MLOps by Mark Treveil and the Dataiku Team