The introduction of MLOps stands to dramatically shift the current data analytic landscape as far as deploying and maintaining AI-ML models are concerned. This rapidly growing field has already begun to build momentum and is becoming invaluable to data-driven organizations.
Our team is here to catch you up to speed and tell you what’s-what with all things MLOps…
What is MLOps, anyway?
“MLOps” is synonymous with the word “continuous.” It is a delivery and deployment practice that aims to homogenize and streamline data for the use of high-functioning performance models. In less cryptic speak, it allows users to take copious amounts of data and then automate and embed them in a wide array of applications and systems aligning with business needs. It can be extremely helpful with defining business objectives using KPIs, data preparation, data acquisition, deployment setup, developing models, monitoring Data Scientist project progress, and so much more.
Application of MLOps in a business context
MLOps can be instrumental in the creation and integration of automated processes and elevated analytic capabilities. It can be used to identify patterns or hazards within your data set, and even regulate quality errors and common keys within your data infrastructure. MLOps/AutoMLOps can take on the heavy lifting of “data” so that users can focus on tasks that require a human touch: problem solving and growth initiatives, among other functions. It allows for the seamless integration of existing products and applications, streaming analytic insights directly into existing software applications. This is extremely powerful, considering that AI/ML projects are becoming a mainstream business need to solve everyday problems.
Currently, Dopplr has an MLOps model under development. This solution could help bridge this disconnect through the introduction of DevOps into AI/ML practices (CI/CD). The goal would be to create a seamless, continuous delivery flow of ML intensive applications. This technology would allow users to remain production-ready, automating a great amount of the packaging and tasks throughout the user’s data journey (Dopplr).
Kubernetes and all their glory
When coupled with MLOps/AutoMLOps technology, Kubernetes can be used to orchestrate deep learning AI/ML models at scale. Kubernetes abstracts some of the infrastructure layer. In turn, ML workloads to take advantage of containerized GPUs (Graphical Processing Unit), and standardized data source ingestion and ensures workloads run smoothly across all microservices.
Systech’s solution — Dopplr — employs the use of MLOps and AutoMLOps to ease the process of producing a high-quality model from a dataset. The user simply uploads his dataset of choice and Dopplr gives out a model with zero coding. The simplicity of platforms such as Dopplr turbocharges your analytics and business processes reducing the time, skills, and grunt work required to produce effective ML models.
Key takeaways from MLOps and AutoMLOps technologies
MLOps/AutoMLOps can be remarkably helpful with organizations that concerned with data consistency, traceability, governance, security, reliability, and observability. Although AI/ML projects are emerging as a mainstream business need to solve complex problems, IT leaders are discovering just how hard it is to go from data science to business value; due to gap in skill, lack of a proper native governance structure pertaining to Machine Learning models, ever changing data patterns, lack of relevant ML management tools and processes, and more (Dopplr).
All in all…
The overarching goal of MLOps/Auto MLOps is to accelerate an organization’s speed to value through the seamless and continuous deployment of automated packaging and production tasks throughout the data lifecycle. MLOps is intended to make existing and future models more impactful and actionable for all institutions, alike.
As an organization in 2022, it is time to get on board, or be at risk of getting left behind.
The Systech Solutions, Inc. Blog Series is designed to showcase ongoing innovations in the data and analytics space. If you have any suggestions for an upcoming article, or would like to volunteer to be interviewed, please contact Olivia Klayman at firstname.lastname@example.org.
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