MLOps: Enabling Operationalization of ML at Scale


You’ve probably heard of machine learning if you work in an IT or data department at a growth company.


Although ML has been around since the 1950s, many people were unaware of its potential until recently—specifically, in 2015. However, with the flood of data science innovations and advances in AI and compute capacity, autonomous systems learning has expanded leaps and bounds to become an indispensable aspect of operations.


As explained by Run.AI:

“Today, machine learning has a significant impact across various industries, including financial services, telecommunications, healthcare, retail, education, and manufacturing. ML is enabling faster and better decisions in business-critical use cases across all of the sectors.”

ML has expanded the boundaries with endless possibilities, and thus many businesses are investing in this new technology.


What exactly is MLOps?

MLOps is defined as “a practice for data scientists and operations experts to collaborate and communicate to help manage the production ML (or deep learning) lifecycle.” MLOps aims to boost automation and improve the quality of production machine learning while simultaneously concentrating on business and regulatory needs, similar to DevOps or DataOps.


In a nutshell, MLOps is a collection of technical components that work together to deploy, run, and train AI models. 

MLOps originated as basic workflows and processes to deploy during deployments to manage the issues experienced with machine learning, beginning with the development of techniques used to enable data scientists and DevOps teams to communicate better using machine learning.


MLOps is now responsible for 25% of GitHub’s fastest-growing projects, leaps and bounds ahead of where it was just a few years ago. The advantages of trustworthy ML system deployments and maintenance in production are tremendous. Now there is full-fledged benchmarking and systemization, not simply simple routines and processes.


The MLOps Challenges

Most companies fail to deliver AI-based applications because they convert data science models into interactive applications. Data science and model development must be an integral element of any modern application, according to experts at McKinsey’s Data-Driven Institute.


Stages of MLOps
MLOps: Enabling AI Application Continuous Delivery

MLOps is focused on providing CI/CD of data and ML-related or required apps. It combines AI/ML principles with DevOps practices. MLOps isn’t about putting an ML model behind an API endpoint or running notebooks in production situations.


MLOps Stage 0: Data Collection and Preparation

Machine learning (ML) teams need access to historical and online data from multiple sources. They must catalog and organize the data in a way that allows for fast and straightforward analysis. 80% of data today is unstructured, so an essential part of building operational data pipelines is to convert it into machine learning-friendly data.


MLOps solutions should include a feature store that defines the data collection and transformations for batch and real-time scenarios. Feature stores must also extend beyond traditional analytics to enable advanced modifications on unstructured data and complex layouts, say experts at Accenture.


MLOps Stage 1: Automated Model Development Pipeline

With MLOps, teams can build machine learning pipelines that automatically collect and prepare data, select optimal features, run training using different parameters or algorithms, evaluate models, and run various model and system tests. All executions, data, metadata, code, and results must be versioned and logged, providing quick results visualization.

ML pipelines are built using microservices (containers or serverless functions), usually over Kubernetes. They implement versioning for data and artifacts used in the pipeline. This way, jobs complete faster, and computation resources are freed up once they do.


MLOps Stage 2: Developing Online Machine Learning Services

An ML model needs to be integrated with real-world data and the business application or front-end services. Its deployment can be ineffective if the related components fail to become a part of the production pipeline or application. Graphs are required to depict a shift in these dependencies.

Flexibility in building mechanisms and pipeline graph is essential. Production pipelines should be elastic to address traffic and demand fluctuations. They should also allow non-disruptive upgrades to one or more elements of the pipeline. To address these requirements, one must deploy various serverless Technology.


MLOps Stage 3: Constant Monitoring, Governance, and Retraining

MLOps monitor the components of ML, to keep deployed models current and predicting with the utmost accuracy and ensure they deliver value long-term. In these turbulent times of massive global change, ML teams need to react quickly to adapt to constantly changing patterns in real-world data.


ML teams need to react quickly to constantly changing patterns in real-world data. The constant monitoring and assessment in a model is an integral part of MLOps. Pipelines should be executed over scalable services or functions, which can span over multiple servers or containers. Use Continuous Integration (CI) techniques to automate the pipeline initiation, test automation, review, and approval process.

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