Machine learning operations (MLOps) is an emerging technology in artificial intelligence proven to be very impactful on e-commerce businesses. MLOps incorporates machine learning for real-time prediction of e-commerce business sales. MLOps also helps businesses with automating redundant tasks. This blog post discusses the MLOps, its processes and tools, and how to use MLOps to magnify your e-commerce business.
What is MLOps?
Machine learning operations (MLOps) is the continuous delivery of machine learning and deep learning models by the collaboration of data science teams and development teams for efficient workflow and automated deployments and monitoring. MLOps enables model management by continuous integration, testing, and deployment. Models can then be stored in model hubs or as a pre-trained concept model. MLOps and DevOps both involve continuous delivery and integration, and MLOps also integrates some part of DevOps as part of its operations, but MLOps is more specific to machine learning models and ML systems usually built by machine learning engineers and data scientists, while DevOps is generic and it involves software engineers and DevOps engineers.
What is the MLOps process?
This is the process used during MLOps by data scientists and ML engineers to deploy ML models to ML production. It also explains the ML lifecycle and model ops
The MLOps process can be divided into three stages which are:
- Data and business understanding stage
- Modeling stage
- Monitoring and versioning stage
- Data and business understanding stage
The following processes are used during the data and business understanding stage of MLOps
- Business understanding: this is a crucial process because the machine learning model is to be developed based on business needs.
- Data extraction and gathering: dealing with data is very important in the MLOps process, data needs to be first extracted and then gathered into data lakes and data warehouses, where it can be used and accessed.
- Data preprocessing and analysis: this data needs to be converted from the raw data into usable data on which data analysis is then performed.
This MLOps stage deals with the machine learning development processes which are:
- Model training and development. After the data stage the model training and development is next, which involves the application of ML algorithms to the data to understand its pattern, training it, and saving the trained ML model.
- Model serving and pipelining: this model can be served via pipeline tools.
- Model deployment: in real life for usage.
Continuous integration and versioning stage
This stage is very important and deals with the monitoring, versioning, and continuous integration of ML models, comprising of the following processes:
- Model re-training and version models: The model needs to continue learning from data, so a new version model may be developed to suit business needs
- Model monitoring: continuous model monitoring is necessary.
- Continuous delivery: this ensures the ML model is continuously working.
What are the tools in MLOps?
MLOps tools enable e-commerce businesses to easily adopt machine learning operations into their daily business activities. The data scientists can use these tools to build ML models and simplify machine learning and model deployment, thereby saving time.
Features and capabilities of MLOps tools
There are some key MLOps features that these tools should have, which are:
- Simple user interface: a simple interface makes the MLOps tool easy to use for anyone.
- ML model training, monitoring, and versioning capabilities should be integrated into MLOps tools
- It should have visualization capabilities: MLOps tools should provide graphs to monitor ML models during training.
- It should support collaboration: MLOps tools should provide collaboration accessories.
- It should work seamlessly with continuous integration and development tools.
- It should provide security of data.
How to use MLOps to magnify or increase your e-commerce business
E-commerce businesses can adopt MLOps to increase sales, improve collaboration, and other benefits. MLOps processes improve e-commerce businesses and give business owners an edge over other businesses. MLOps can be used to magnify e-commerce business in the following ways:
- MLOps can be used to increase e-commerce business online presence: this is done during the data extraction process, which allows websites with eCommerce and e-commerce platforms to become aware of how much their presence is felt online because during this process the e-commerce business discovers where all data is located.
- MLOps can be used to speed up e-commerce business time: team collaboration in MLOps leads to faster deployment of services in the businesses.
- It can be used for automation of tasks: MLOps automates tasks in e-commerce business. For example, it can help develop a machine learning model to predict sales of a particular product for the coming years, and this can be done automatically by the model instead of manual price estimation.
- It can be used to promote communication and collaboration: the MLOps process tends to improve communication in business and also promote collaboration between business employees and data scientists.
- MLOps results in an increase of sales: applying MLOps to your e-commerce business will increase the sales in that business. For example, a marketing business can predict which regions will have interest in a product by building an ML model from data, which can lead to an increase in sales.
- Improved workflow: the workflow in e-commerce marketing is improved because of the seamless communication that MLOps provides. Also during MLOps, real time monitoring is done which improves workflow.
- MLOps keeps an e-commerce business ahead of the competition: it does this by generating more sales for the e-commerce business and providing state-of-the-art machine learning models for the e-commerce business.
- MLOps helps organize business data and provide data security: when an e-commerce business data is organized, this increases or magnifies an ecommerce business’s sales.
- MLOps can help identify customer base for e-commerce business: data analysis provides insights into the ecommerce business making it easier to identify customers and know where the customer base is.
Platforms and types of e-commerce business that can adopt MLOps to magnify business operations
Types of ecommerce businesses that can adopt MLOps are:
- Business to customer: where business sells products to customers directly
- Business to business (b2b e-commerce): business sells to businesses.
- Customer to business: allows individuals to sell services to businesses.
- Customer to customer: involves the selling of goods among individuals.
Examples of e-commerce platforms popularly used are:
- Magento e-commerce is a popular ecommerce platform that provides marketing, SEO optimization, and a shopping cart system, and flexible control over its online store. It uses various plugins and tools.
- Shopify: fast loading and easy to use and provides other services like marketing, payment, etc.
Other e-commerce platforms are Wix, Volusion, shift4shop, Weebly, Prestashop, to name a few.
MLOps provides a lot of benefits when adopted by e-commerce businesses ranging from promoting communication, to an increase in sales and customer identification, amongst others. Therefore MLOps processes should also be adopted in an e-commerce business, and suitable MLOps tools can be used.
- MLOps: Machine learning operations
- MLOps: What It Is, Why it Matters, and How To Implement It
- MLOps vs. DevOps: What is the Difference?
- What Is MLOps? Machine Learning Operations Explained
- MLOps Tools Overview
- The Best MLOps Tools and How to Evaluate Them
- 5 Common Uses Of MLOps In A Business
- How Can An MLOps Platform Help Your Business
- Industries likely to transform with machine learning