When ML Meets DevOps: How To Understand MLOps
This article covers integrating Machine Learning and DevOps, managing models, best practices, and successful solutions.
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Artificial Intelligence (AI) and Machine Learning (ML) have taken over the world in recent years, becoming crucial components of practically any industry, from retail and entertainment to healthcare and banking. These technologies have the power to radically transform enterprises by automating operations, reducing costs, and boosting decision-making by analyzing huge volumes of data. The number of AI and ML projects has risen dramatically recently, creating the difficulty of effective ML project management. That is how MLOps entered center stage in the tech industry.
What Is MLOps?
MLOps, or Machine Learning Operations, is a methodology for optimizing ML lifecycles in order to accelerate the creation of AI-powered applications. It follows DevOps-like techniques for rapid and dependable software development in the ML sector. MLOps, like DevOps, fosters collaborative thinking, enabling constant communication among data scientists, software engineers, and data experts. It covers all aspects of the ML process, from data collection and model creation to final deployment and constant project enhancement.
What Exactly Are AI and ML?
Despite their close relationship, AI and ML are two distinct concepts that should not be confused. AI is an umbrella term that encompasses a wide range of approaches and techniques for making computer systems imitate human intelligence. ML, in turn, is a subfield of AI that allows a computer to learn automatically from structured data. It focuses on training algorithms with large data sets to create complex ML models capable of performing sophisticated tasks such as analyzing big data, sorting images, and generating human-like speech.
What Does the ML Lifecycle Include?
Almost all ML projects go through the following stages during its cyclical life cycle:
- Data collection: Creating a high-quality dataset is an important part of the ML lifecycle, which includes gathering data such as text, images, sounds, or videos.
- Data preparation and wrangling: Randomising, organizing data into datasets, removing errors and duplicates, and converting it to a usable format.
- Model development: Choosing an effective model architecture, creating the model, training it on the dataset, tuning, and evaluating it.
- Model deployment: Introducing a trained machine learning model into the real-life system and determining whether or not it improves its performance using available data.
- Monitoring: Observing the system to ensure and maintain a desired level of performance through early detection and correction.
How Does MLOps Improve the ML Lifecycle?
MLOps employ DevOps techniques to create an optimal and standardized approach to ML. It heavily relies on the following practices throughout all stages of the ML process:
- Version Control: It is widely used in software engineering and involves tracking, storing, and managing changes made to code over time. In ML, it goes a step further, focusing on the key ML components - data and models - to ensure traceability, reproducibility, rollback, debugging, and collaboration. Data version control is essential for model development, and model version control is essential throughout the model's lifecycle.
- Continuous Integration and Continuous Deployment (CI/CD): These practices revolve around a continuous process of reviewing, reassessing, and identifying problems with the ML model, which allows the model to be changed when new data is received. The CI/CD also automates portions of the ML pipeline, such as the development, build, and testing stages.
- Monitoring and Logging: The ML model monitoring is in charge of continuously tracking the deployed model's performance in production, providing critical feedback, and ensuring the model predicts accurately and delivers value over time. Logging creates a record of application events that can be used to identify the source of performance issues that occur at any stage of the model's development.
- Model Governance and Compliance: These practices aim to reduce the risks associated with poor model performance, which can result in various issues ranging from poor decision-making and bad client experiences to violations of national and international laws and regulations. For example, many businesses must adhere to stringent personal data security regulations in many countries.
- Infrastructure and Scalability: The combination of resources, tools, and processes required to build, train, and run ML models is known as ML infrastructure. It ensures the successful implementation of every stage of an ML workflow by assisting data scientists and engineering teams in managing and operating the various resources, as well as streamlining training and deployment processes. ML scalability refers to the ability of ML applications to handle any amount of data while remaining cost-effective and time-saving while serving millions of users.
- Automated Testing: By automating unit and integration testing, MLOps practitioners can make a long-term difference. Automation accelerates unit tests, which are based on the concept of dividing code into blocks and testing each one separately. It also improves integration testing, which is used to see how components interact with one another. Its primary goal is to ensure that the combined units interact with each other and that the system meets all model standards.
What Is Required to Launch an MLOps Career?
Anyone who wants to take on the challenge of steering their career towards this dynamic AI sector in 2023 should be familiar with the fundamental practices, concepts, tools, and platforms used in MLOps. They are as follows:
- Data engineering, machine learning, and software development: It is essential to understand the fundamentals of the Python programming language, as well as the ML frameworks TensorFlow and PyTorch.
- DevOps: It’s crucial to learn about DevOps principles, version control systems, CI/CD pipelines, and cloud platforms like AWS or Google Cloud.
- MLOps Tools: It is critical to be familiar with the tools for managing the end-to-end ML workflow, which includes MLflow, Kubeflow, and Apache Airflow.
- Practice: To be successful in MLOps, it is necessary to build, test, and deploy ML machine learning models in real-world scenarios. Starting with small personal projects can help pave the way for work in large tech firms.
- Community: Immersing oneself in community life, participating in contests and hackathons, and collaborating with other professionals are essential for staying up to date on MLOps developments, learning new skills, and succeeding.
- Certifications: It is critical to obtain ML and DevOps certifications from cloud providers in order to build a strong resume and land a dream job.
Conclusion: Why MLOps Is Important?
MLOps is a set of efficient practices that represents a shift towards optimizing ML lifecycles, resulting in faster and more agile development, testing, and maintenance of AI applications. Its primary benefits are efficiency, scalability, and risk reduction.
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