Seamless Handling of Data ops and ML ops

Agile methodology is an approach to software development and IT processes that accelerates deployments, streamlines collaborations and promotes real-time decision-making. Agile principles create a foundation for DevOps and especially DataOps because they promote cross-stack integration and simplify data use in dynamic business environments. The same can be said of machine learning operations (MLOps), which fosters increased automation and eases data model training within an organization. While DevOps focuses primarily on IT processes and software development, DataOps and MLOps approaches can apply to the entire organization to improve IT and business collaborations, as well as overall data use.

The BDB Data Pipeline is an Enterprise event-based data orchestration and transformation tool, that allows you to seamlessly design and deploy your DataOPs or MLOPs workflow.

Let's explore key concepts behind both methodologies, look at the operational requirements and consider the technical benefits of one approach over the other.

Key requirements to deploy DataOps

In general, DevOps methodologies ensure greater collaborations across development, engineering, and operations teams. DataOps help to extend these capabilities and integrate platforms, analytics tools, and automation to eliminate data silos across an organization. DataOps also democratize data use through self-service portals. DataOps create an infrastructure to make information more accessible for both data scientists and business-side end users.

Within an organization, DataOps rely on automation across the entire IT infrastructure to offset manual IT operations such as quality assurance testing and pipeline monitoring. Companies also gain general productivity improvements via the ability to use microservices and achieve higher degrees of self-sufficiency for IT and business teams.

Data Ops

Traditional data transformation operations are more like a sequential process where the developer design, develop, test, and deploy the logic. BDB Data Pipeline allows the user to adopt the agile and non-linear approach which reduces the time to market by 50 to 60 %.

MLOps adoption

BDB Data Pipeline allows you to operationalize your AI/ML Models in a few minutes. The Models can be attached to any pipeline to get the inferences in real-time. The inferences can either be used in any other process or instantly get shared with the users.

ML Ops process in Data Pipeline

There are several stages to MLOps and aid the machine learning model lifecycle. These involve IT and business goal identification, data collection & annotation, model development & training, and final deployment as well as maintenance.

MLOps involve executing and monitoring data flows via multiple pipelines to properly train data models. It represents the next level in organizing data and model-based processes. MLOps entail tasks similar to those involved with extracting, transforming, loading, and mastering data management systems.

The key objectives of MLOps that align with the goals of DataOps are to streamline project deployments and improve data quality.

The Tools and Technologies

Now to move on to the third critical element of DataOps, i.e., technology, it has to be emphasized that, much like DevOps, DataOps is indeed more about the process and not technology. But we must also realize that without the power that the Data Pipelines technology brings to support DataOps, the business returns of the Data Management initiatives in question would be impossible to realize.

In addition to all the goodies from the Open-Source Software (OSS) communities,

Many vendors have joined the DataOps movement providing substantial influence. These include new/niche players, established software giants who for decades have specialized in enterprise data management, and Cloud hyper scalers – all jumping on the bandwagon with very compelling offerings.

The goals of DataOps vs. MLOps

When deciding on one approach versus another, it's useful to consider what they have in common: Both IT processes revolve around making data work better. On one hand, DataOps is designed to manage and improve data flow at scale. Applying methodologies similar to DevOps, DataOps manage pipeline deployment and ensures that diverse information streams are usable and conform to specification.

On the other hand, MLOps is dedicated to ensuring that machine learning algorithms and AI systems are perfectly aligned and in sync. MLOps seamlessly integrates the amount and diversity of data to ensure that machine learning models perform as intended. A key goal of MLOps is to increase data science's effectiveness for insight-driven decisions within an organization. As these two methodologies overlap and complement one another, it's useful to consider a new and emerging approach that might help simplify the choice for smaller companies and enterprises.

Service orchestration and automation platforms offer an as-a-service approach to help IT operations teams orchestrate the automated processes that comprise end-to-end data pipelines. This platform approach provides management and observability across an entire network of data pipelines. Organizations are then equipped to handle the coordination, scheduling, and management of their data operations through a subscription service.

Similarities and Differences between MLOps and DataOps

Both MLOps and DataOps involve

Collaboration for workflow: The operating philosophy of DataOps and MLOps is to achieve harmony and speed by encouraging different departments to work together.

Automation: Both of them work towards automating all processes in their pipelines. DataOps automates the entire process from data preparation to reporting, and MLOps automates the entire process from model creation to deployment and monitoring.

Standardization: While DataOps standardize the data pipelines for all stakeholders, MLOps standardize the ML workflows and create a common language for all stakeholders.

Differences between MLOps and DataOps

They deal with a different set of questions and objectives in the machine learning lifecycle and require different types of expertise and tools.

You can have DataOps without MLOps because you can have data extraction and transformation without machine learning. The contrary is barely true.

DataOps is applicable across the complete lifecycle of data applications. MLOps is primarily for simplification of management and deployment of machine learning models.

The goal of DataOps is to streamline the data management cycles, achieve a faster time to market, and produce high-quality outputs. MLOps aims to facilitate the deployment of ML models in production environments.

Use Cases

Seamless handling of Data and ML Ops

Link:: https://youtu.be/tpBTQ7Vitu0

OIL & GAS part

Link:https://youtu.be/qSYxAFdIvUY

Hyper automation & Analytics use case for Online Retail

Link: https://www.youtube.com/watch?v=2pjY3gJLHJ0&list=PLp0jqorSKLM7VwiQ29gnA0cowhm-NTSSR&index=4