Machine learning (ML) becomes effective once models are in production. Organizations, on the other hand, usually underestimate the complexity and challenges of implementing machine learning in production, devoting the majority of their resources to ML development and considering machine learning as ordinary software.

The final outcome is machine learning programs fail to produce results, resulting in lost money, squandered resources, and to overcome this, the concept of MLOps was developed.

What is MLOps?

MLOps is a collection of procedures that aim to implement and maintain machine learning (ML) models that are in production, consistently and efficiently. The word itself is a mixture that refers to “Machine Learning” and the continual development process of “DevOps” in the field of software.

Models for machine learning are evaluated and refined in isolated experiment systems. Once algorithms are ready for launch, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm into the production systems.

Benefits of MLOps

MLOps can help enterprises in a variety of ways, and is becoming increasingly popular among businesses as a way to boost productivity and generate reliable, enterprise-grade models. Teams are using MLOps to make a difference in various sectors, from innovative new firms to large-scale public transportation divisions. Major benefits which a developer can achieve through MLOps are mentioned below.

Less time on data collection and preparation — Data scientists, systems integrators, and solution engineers had to spend a lot of time on repetitive data gathering or data preparation activities before they could get their hands on the model and apply our use cases. These efforts were painstaking and costly, however, because many highly qualified employees were allocated before the model was built. By minimizing operational chores, MLOps can help data scientists and software engineers save time and money.

Scalability: MLOps also allows for massive scalability and management, allowing for the oversight, control, management, and monitoring of thousands of models for continuous integration(CI), continuous delivery(CD), and deployment.

Risk reduction: Throughout the model lifecycle, MLOps delivers comprehensive monitoring tools, data drift visualizations, and data metrics detection to maintain high accuracy. It uses analytics and alerting methods to detect anomalies in machine learning development, allowing engineers to immediately assess the severity of the problem and take appropriate action.

Reducing Bias: MLOps can help to avoid development biases, which can lead to misrepresentation of customer needs or legal scrutiny of the organization. MLOps technologies ensure that data reports do not contain inaccurate data. MLOps enable the development of dynamic systems that are not pigeonholed in terms of reporting.

Easy deployment of high precision models — The MLOPs technology allows users to easily and confidently deploy high precision models. It takes advantage of autonomous scalability, managed CPU and GPU clusters, and cloud-based distributed learning. Pack models rapidly, guaranteeing excellent quality at every stage with profiling and model validation, and move models to the production environment with managed deployment.

Big teams rely on big ideas. Learn how experts at Uber, WorkFusion, and The RealReal use Comet to scale out their ML models and ensure visibility and collaboration company-wide.

MLOps Stages

Data Preparation — As we know there is no Machine Learning without data. Prior to anything else, ML teams require access to historical or online data from a variety of sources, as well as the ability to store and organize the data in a way that enables quick and easy analysis. This stage involves collaboration between a data engineer and data scientists from various sources to collect data and prepare data for modeling.

Machine-learning Development—While constructing models, data scientists typically follow the steps: Extract data from an external source and then do data labeling to identify the potential pattern. Next will go for Model training and validation. At this stage, Machine learning teams use MLOps to create machine learning pipelines that collect and prepare data automatically, pick optimal features, train models using multiple parameter sets or algorithms, evaluate models, and conduct various model and system tests.

Production deployment —To connect with real-world as well as the business application or front-end services. The entire ML application must be deployed without causing service disruption. If the machine learning components aren’t considered as an integrated part of the application or production pipeline, deployment might be extremely difficult. This stage ensures a secure and seamless transition to the production server of your choosing, whether it’s a public cloud or hybrid.

Monitoring — Artificial intelligence (AI) services and apps are quickly becoming an integral aspect of any company’s operations. ML teams must add data, code, and monitor data for quality issues, check models for concept drift and enhance model accuracy using AutoML approaches and ensembles, among other things. This step includes monitoring of the model and monitoring of the infrastructure.

MLOps Architecture Design

The end-to-end reference design for MLOps is shown below. It’s critical to note that the MLOps lifecycle is an iterative, rather than a linear process. A failing test or a compilation issue are examples of conditions for reverting back to an earlier stage in DevOps. MLOps inherits DevOps conditions and adds new ones, such as offline model validation and model drift, that are unique to machine learning.

Type of MLOps Tools

As you look for a solution that will fit your goals and assist you in implementing MLOps, you’ll see that there are a variety of possibilities. You’ll need to consider open-source vs. proprietary software, as well as SaaS vs. on-premise solutions.

Open-source vs proprietary MLOps tools — Open-source software users are free to read, modify, and distribute the source code for their own purposes. The source code for proprietary software is not available to the general public. Only the firms that generate this software have the ability to change it.

SaaS vs on-premise MLOps tools — Access to programs is provided through software as a service (SaaS). Through the web, users engage with a software interface. In-house hosting is used for on-premise software solutions. This is normally more secure, but the expenses of administering and maintaining the necessary infrastructure are higher.

MLOps have access to all of these possibilities. Your decision should be based on your individual objectives, internal knowledge, and financial constraints.

The next article will discuss in detail the various MLOps tools, How to download and work? How to select the right MLOps tool as per your requirement.

Conclusion

This article explained to you regarding MLOps and its benefit. Even you got familiar with MLOps architectural design and the various stages from Data preparation to monitoring.

The next part will discuss various MLOps tools in detail with their use cases. A different tool is developed for different purposes, so you will get a chance to explore multiple tools in a single place.