To transform their businesses, outperform competitors, and drive significant value, organizations need machine learning (ML) and artificial intelligence (AI) solutions now, more than ever. They are well aware about the unfortunate reality of several ML models not making it to production, or when they do, their deployment is cumbersome and time-intensive. The question that arises is how do they achieve faster ML model deployment and maintain seamless operations while managing its complexities?
This is where MLOps comes to the rescue. Implementing MLOps streamlines the development and deployment of ML models, which corresponds to DevOps in software engineering. Besides DevOps principles, observability, and operations, ML models need
- Automating model training
- Monitoring quality of the data
- Original datasets with relevant configuration variables
How to Accelerate ML Model Deployment to Production
It is commonly observed that lack of automated tasks concludes at ML models having deployment environments and data pipelines each to their own. Because there is no consistency in methodologies adopted, development is sluggish, making system improvement even more expensive. Organizations must carefully consider storing and versioning all components of ML models, including variables and datasets, all along the development lifecycle for ensuring coherence.
The logical methodology for accelerated ML model deployment is MLOps, which automates the processes of data science and creates a unified environment to develop ML models continuously and deploy them faster. MLOps supports accelerated ML model deployments by
- Making model deployments an easy row to hoe through an open-source language
- Exposing REST APIs in production to predictive analytics
- Having a central pipeline in place to prepare the data to be compatible with coherent workflows, where advancements can be made for future model deployments
- Incorporating a management tool for comparing new ML models across variables
- Establishing workflows with CI/CD for applications and the data to support all frameworks
The Impact of Low Code Platforms
Close to 36% of respondents, according to a McKinsey survey, have deployed ML algorithms far off the pilot stage. The reason behind low adoption is that there is little guidance among organizations to pave the way for their teams to adopt ML algorithms. For data scientists aiming for bolstering the ML model deployment or experimenting with many algorithms, low code platforms can be highly advantageous.
Low code platforms equip organizations to deploy ML models faster and cost-effectively, with pre-built components that speed up the development process. While the infrastructure set up and management is taken care of beforehand, elimination of manual coding and provision of pre-built, deep training models enables organizations to achieve results in seconds. Moreover, low code platforms allow developers to spend less time on coding tasks and integrate custom models, thereby optimizing functionality of the application.
It will be an overstatement to say that low code will completely substitute the algorithms coded manually. However, pre-built components of low code platforms alleviate the need for skilled data scientists to debug and maintain exhaustive codes. They are better suited for custom ML model deployments in data-intensive processes.
The End of the Line
What’s important for organizations is to boost their performance, cutting down the cost of deployment, accelerating time-to-market, and mitigating application lags for delivering better user experience. The highly skilled data science team at Blazeclan, with their expertise in open source and enterprise ML tools, have been contributing to organizations’ need for ML accelerators and MLOps frameworks across various cloud platforms. For example, a leading alliance of telecom operators leveraged Blazeclan’s ML/AI solution to automate workflows of their invoicing system and improve the accuracy of records categorization by 99%.
