MLOps or Machine Learning Operations is a multidisciplinary field that amalgamates software development lifecycle (SDLC) with machine learning (ML). This overlap between IT operations and ML facilitates the performance of ML models in the production environment for making necessary changes and detecting issues. This MLOps tutorial will give you a complete guide on the path to learn MLOps. Additionally, we have also covered how to become an MLOps engineer and what they do.
Machine Learning Operations refers to a set of practices to efficiently and reliably deploy and maintain ML models in the production environment. Once an algorithm is all set to launch, DevOps engineers, ML engineers, and Data Scientists practice MLOps for transitioning this algorithm to production systems. MLOps began as only a set of best practices. Today, however, it has evolved into a completely independent approach to the process of machine learning lifecycle management.
Machine Learning Operations has become an integral part of many organizations. This is because of the kind of results it produces for those who implement it. There are quite a few reasons as to what is the need for MLOps. Some of the top reasons are-
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MLOps and DevOps are related concepts with different focus areas. DevOps focuses on streamlining the SDLC, by facilitating automation and collaboration between both development and operations teams. MLOps extends the DevOps principles to machine learning models. It focuses on the specific challenges that come while dealing with data, deployment, model training, and monitoring.
| Feature | MLOps (Machine Learning Operations) | DevOps (Development Operations) |
| Definition | A set of practices for deploying, managing, and monitoring machine learning models in production | A set of practices for automating software development and IT operations |
| Focus Area | End-to-end ML lifecycle: data, model training, deployment, and monitoring | Application development, deployment, CI/CD, and infrastructure management |
| Key Components | Data pipelines, model versioning, training orchestration, model deployment, monitoring, drift detection | Source control, CI/CD pipelines, infrastructure as code (IaC), automated testing, monitoring |
| Complexity | High - includes data engineering, ML experimentation, retraining, and model monitoring | Moderate - primarily code-centric automation and infrastructure |
| Tool Examples | MLflow, Kubeflow, TFX, SageMaker, DVC | Jenkins, Docker, Kubernetes, GitLab CI/CD, Terraform |
| Automation Focus | Automates model training, validation, deployment, and retraining | Automates code build, test, release, and infrastructure provisioning |
| Monitoring Scope | Model performance (accuracy, drift), data quality, inference latency | System performance (uptime, load), application errors, CI/CD metrics |
| Stakeholders | Data scientists, ML engineers, MLOps engineers | Developers, DevOps engineers, system admins |
| Versioning | Code + Model + Data versioning | Primarily code versioning |
| Primary Goal | Deliver reliable, reproducible, and scalable ML models | Deliver high-quality software faster and more reliably |
An MLOps engineer is a highly skilled professional tasked with the responsibility of streamlining the workflow of the ML platform. This goes from design to development and production, and even further. These engineer works closely with operations staff, business leaders, data scientists, and developers associated with the ML product. Here is a list of some of the responsibilities you will have to undertake for a career in this field:
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MLOps learning path is not an easy one. Or rather, it is not easy to decode it. As Machine Learning Operations engineer, you would need to have strong knowledge in the leading skill suite, along with extensive working experience in a few the below-mentioned fields:
Now, it is important to understand these skills are highly akin to that essential for a DevOps job. However, to ensure a career, you must gain a strong understanding of Machine Learning models and algorithms. An MLOps engineer must possess a solid skill set that includes but is not limited to statistical modeling, IT operation and infrastructure, database administration and construction, scripting and programming, and data science.
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Anyone who wishes to become an MLOps Engineer must be prepared to gain a hefty amount of skills and experience. Even if you have a good amount of experience, an MLOps course is sure to help you better understand and streamline your career ahead. This tutorial covers the key skills and tools to know about, and learn to explore the ability to work as a engineer in this field. However, it is to be noted that every organization and industry is different. Hence, what skills you have might or might not be fit for a certain organization or industry. Here are some commonly accepted norms for an MLOps engineer-
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Machine Learning Operations involve different core principles and concepts. Learning these can help you to build and deploy robust and scalable machine learning systems.
Automation is a fundamental principle of this field that focuses on automating repetitive tasks throughout the ML lifecycle. This includes automating data ingestion, preprocessing, model training, testing, deployment and monitoring. Automation reduces manual effort, minimizes errors and accelerates the development and deployment of machine learning models.
CI/CD for ML extends traditional CI/CD practices to the machine learning context. It involves automating the integration, testing and delivery of ML models. CI focuses on automating the building and testing of ML code and components, while CD focuses on automating the deployment of trained models to production environments. These pipelines ensure that changes to code and models are integrated and deployed efficiently and reliably.
Code testing and validation maintain the quality and reliability of code. It also identifies and prevents bugs. This includes unit testing, integration testing and end-to-end testing of ML components. Data preprocessing scripts, model training code and inference services are examples of these components.
Model testing and validation focus on evaluating the performance and accuracy of trained models. This involves testing model performance on held-out datasets, performing robustness checks and assessing model fairness and bias. Organizations use this thorough model testing to ensure that their models meet performance requirements and are suitable for deployment.
Automated deployment pipelines streamline the process of deploying trained models to production environments. These pipelines automate the steps involved in packaging, deploying and configuring models. This way models are deployed consistently and efficiently. Automated deployment pipelines also provide rapid iteration and reduce the risk of deployment errors.
Version control is essential for managing changes to code, data and models. It allows teams to monitor changes, communicate effectively and regain the previous versions if needed. Git, a version control system, is used to manage code and model versions, ensuring reproducibility and traceability.
Monitoring and logging are essential for managing the health and performance of deployed ML models. Monitoring involves tracking model performance metrics, such as accuracy, latency and resource utilization, while logging involves recording events and errors during model execution. Comprehensive monitoring and logging help teams to detect and address issues proactively.
Collaboration is essential for successful MLOps implementation. It involves effective communication and coordination between data scientists, ML engineers and DevOps engineers. Collaboration tools and practices, such as shared repositories, issue tracking systems and regular meetings, facilitate seamless teamwork and knowledge sharing.
The future of MLOps engineers seems pretty bright as all the technological advancements are in favor of these professionals. These engineers are predicted to work more closely with data engineers in the coming years to address all bias and data quality issues. We hope this MLOps tutorial has helped you enough to understand the basics. For deeper information, you will need guidance from experts.
Yes, Its generally does require coding knowledge to developer codes that can run in production environments. Hence, as an MLOps engineer, you must learn to code and gain basic working experience.
You need high proficiency in programming languages and the best language for MLOps is undoubtedly Python.
The average salary of an MLOps engineer in the US is $87,220 per annum. On the other hand, the average salary of a DevOps engineer in the US is 133,115 per annum.
MLOps is better if you enjoy machine learning and data models, while DevOps suits those focused on cloud, automation and infrastructure. It depends on your skills and career goals.
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Course Schedule
| Course Name | Batch Type | Details |
| MLOps Training | Every Weekday | View Details |
| MLOps Training | Every Weekend | View Details |
