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MLOps to deploy Machine Learning Pipeline
MLOps (Machine Learning Operations) offers a set of standardized processes and technological capabilities to quickly and reliably develop, deploy, and operationalize ML systems. Data scientists, ML engineers, and DevOps engineers collaboratively work together to provide great results with MLOps.
It would sometimes happens that machine learning products fail in the manufacturing process but MLOps makes it possible for many teams to collaborate by speeding up the development and release of machine learning pipelines. Many businesses are placing an increasing amount of emphasis on deploying pipelines and controlling entire processes using MLOps best practices.
What is Pipeline?
The workflow required to create a machine-learning model can be managed and automated using a machine-learning pipeline. The pipelines are made up of several sequential processes that handle everything from model training and deployment to data extraction and preprocessing.
Iterative machine learning pipelines repeat each step to increase the model's accuracy and reach the desired outcome. An MLOps pipeline's objective is to effectively and costefficiently apply a machine learning model to incoming data at scale. By offering standard utilities for deployment, it aims to reduce the time ML engineers spend operationalizing each new model.
Four Pillars of Machine Learning Pipeline
The following are the four pillars of an ML pipeline −
Tracking − Keeping track of all the code, data, and models is essential while developing systems. It's crucial for auditing to record which models have been applied to which sets of data.
Automation − ML professionals can deliver ML models faster and with higher quality by using Continuous Integration / Continuous Deployment (CI/CD). Unit tests, stress tests, integration tests, and regression tests should all be automated as part of your CI/CD.
Monitoring − MLOps need to keep a close eye on the ML pipelines through effective logging and alerting. By routinely tracking the effectiveness of your ML pipelines, you can spot issues before they become serious. To ensure that the model is operating as anticipated, the MLOps pipeline should keep an eye out for data drift and erroneous predictions.
Reliability − A dependable ML pipeline will perform as intended and consistently provide value to the business.
Considerations for Machine Learning pipeline
Take into account every step involved in creating your machine-learning model. Work your way up by starting with the collection and preprocessing of the data.
Testing ought to be seen as an essential component of the pipeline. With a pipeline, you have the chance to test much, much more thoroughly because you won't have to do it manually every time.
The orchestration of a machine learning pipeline can be done in a variety of ways, but the fundamentals always remain the same. You specify the pipeline's inputs and outputs as well as the components' execution orders.
Manual ML pipeline Vs Automated ML Pipeline
Following is the difference between Manual ML pipeline Vs Automated ML Pipeline –
In this pipeline, model is considered as the product.
In this pipeline, pipeline is considered as a product.
It has a slow iterative cycle
It has a fast iterative cycle
It is a script-driven process
It is an automated process.
Data scientists and ML engineers dont interact much.
There is a good communication between data scientists and ML engineers.
It does not include version control.
It includes version control.
Steps to build Machine Learning Pipeline
Following are the steps involved in building the Machine Learning pipeline –
Data collection − It is a crucial phase that enables the gathering of crucial data for the ML models to successfully meet the KPIs (Key Performance Indicators). Depending on whether we are trying to solve a classification problem or a regression task, these signs change.
Data Cleaning − The practice of correcting or deleting inaccurate, damaged, improperly formatted, duplicate, or incomplete data from a dataset is known as data cleaning.
Data visualization − It is now time to investigate whether there is any association between data features and the output variable after obtaining the pertinent data that is necessary for forecasts. Utilizing helpful visuals, such as bar plots, scatterplots, and count plots, greatly facilitates comprehension and analysis of the data, allowing for clear communication with stakeholders. Tools: MATLAB, R, and Python.
Using data modeling to provide predictions − Any of the following methods can be used to accomplish this: reinforcement learning, unsupervised learning, semisupervised learning, and supervised learning.
Model deployment − It's time to deploy the models in real time and evaluate their performance now that we have trained a variety of models and tuned the hyperparameters.
Model monitoring − The workflow's final stage would be to continuously check the model's performance to assess how well it is doing and whether it is living up to expectations based on KPIs.
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