The era which undoubtedly belongs to Artificial Intelligence results in the use of machine learning in almost every field, and whether it is healthcare, business, and technical, machine learning is everywhere. With the availability of the latest tools, techniques, and algorithms the development of machine learning models to solve a problem is not a challenge, the real challenge lies in the maintenance of these models at a massive scale. This blog will give an insight into Productionizing Machine learning models with MLOps Solutions. The new chasm of the development process of machine learning involves the collaboration of four major disciplines – Data Science, Data Engineering, Software Engineering, and traditional DevOps. These four disciplines have their level of operations and their requirements with different constraints and velocity.
When we approach modern Machine Learning problems in an AWS environment, there is more than traditional data preparation, model training, and final inferences to consider. Also, pure computing power is not the only concern we must deal with in creating an ML solution. There is a substantial difference between creating and testing a Machine Learning model inside a Jupyter Notebook locally and releasing it on a production infrastructure capable of generating business value. The complexities of going live with a Machine Learning workflow in the Cloud are called a deployment gap and we will see together through this article how to tackle it by combining speed and agility in modeling and training with criteria of solidity, scalability, and resilience required by production environments. The procedure we'll dive into is similar to what happened with the DevOps model for "traditional" software development, and the MLOps paradigm, this is how we call it, is commonly proposed as "an end-to-end process to design, create and manage Machine Learning applications in a reproducible, testable and evolutionary way". So as we will guide you through the following paragraphs, we will dive deep into the reasons and principles behind the MLOps paradigm and how it easily relates to the AWS ecosystem and the best practices of the AWS Well-Architected Framework. As said before, Machine Learning workloads can be essentially seen as complex pieces of software, so we can still apply "traditional" software practices.
This article explains how Machine Learning Operations came to be a discipline inside many companies and things to consider when deciding if your organization is ready to form an MLOps team. Machine learning (ML) is a subset of artificial intelligence in which computer systems autonomously learn a task over time. Based on pattern analyses and inference models, ML algorithms allow a computer system to adapt in real time as it is exposed to data and real-world interactions. For many people, ML was, until recently, considered science fiction. But advances in computational power, frictionless access to scalable cloud resources, and the exponential growth of data have fueled an increase in ML-based applications.
MLOps is the new terminology defining the operational work needed to push machine learning projects from research mode to production. While Software Engineering involves DevOps for operationalizing Software Applications, MLOps encompass the processes and tools to manage end-to-end Machine Learning lifecycle. Machine Learning defines the models' hypothesis learning relationships among independent(input) variables and predicting target(output) variables. Machine Learning projects involve different roles and responsibilities starting from the Data Engineering team collecting, processing, and transforming data, Data Scientists experimenting with algorithms and datasets, and the MLOps team focusing on moving the trained models to production. Machine Learning Lifecycle represents the complete end-to-end lifecycle of machine learning projects from research mode to production.
We live in an unbelievably rich ecosystem for creating present day web applications. The tools for delivering applications to production, monitoring performances, and deploying in real-time are countless. These tools are so indispensable that modern web application development would be almost impossible without them. By contrast, modern Machine Learning doesn't yet have that same ecosystem. This anomaly arises due to a number of reasons: standardized practices are yet to be established, constant evolution of development tools, and modern Deep learning has been around only for a really miniscule amount of time in the grand scheme of things.