DataOps and MLOps – The Power of Integration

As organizations transform digitally, IT is becoming a profit center. AI and data are at the center of this innovation with their prescriptive, cognitive systems.

In this post, we will explore two popular terms or disciplines in the Ops landscape. Going beyond IT, the Ops areas include DataOps, MLOps, VizOps, AIOps, DevOps and TechOps. The base for these areas comes from the application of agile principles for the smooth delivery of applications across various business processes.

What is DataOps?

DataOps, simply put, is the use of agile practices to deliver data products, quickly and cost-effectively. It is a process-oriented methodology used by data teams to improve the quality of data, increase the efficiency of analytics, and reduce the time cycle of data analytics. Used by data architects, data engineers, data analysts, and data scientists, this methodology powers data pipelines and machine learning models to help companies derive value from their data.

What is MLOps?

MLOps or Machine Learning Operations focuses on model cataloging, compute orchestration, version, control, and such disciplines. It simplifies machine learning models for better management and logistics between operation teams and machine learning researchers. MLOps is a part of DataOps. Using MLOps, organizations can improve the quality of production ML, increase automation, and focus on business requirements.

DataOps and MLOps – The Similarities

Both DataOps and MLOps discuss the best practices to help organizations deliver their data projects in a manner that is-

• Faster
• Simpler
• Qualitative

Also, DataOps and MLOps target the common set of data developers, operations personnel, and analytics teams. They both are part of the software cycle in the same stage of Ops. The common steps include-

• Data extraction
• Data preparation
• Data analysis
• Model monitoring
• Model evaluation and training

DataOps and MLOps – The Differences ?

While there is a lot of overlap between DataOps and MLOps, there are some fundamental differences between the two –

• The users of DataOps are data teams (data engineers, architects, analysts, scientists, and operations). The users of MLOps, on the other hand, are data scientists and operations teams.
• DataOps is applicable across the complete lifecycle of data applications. MLOps is primarily for simplification of?management and deployment of machine learning models
• The aim of DataOps is to shorten the development cycles, achieve faster time to market, and release high-quality products. The aim of MLOps is to ease the deployment of machine learning and deep learning models in production environments.

Data-Ops and ML-Ops Risks & Challenges

The key challenges in the development and implementation of DataOps and MLOps can result in the degradation of efficiency. This can also affect the predictive performance of models. The most frequent challenges include-

• ?Possibility of features shifting with external changes. For example, DataOps in the automobile industry can be affected by the oil prices globally.
• In some industries, data may not be audited to meet monitoring tools requirements, which can lead to the problem of validity and correctness.
• Data can change over time and models need to be intelligently equipped to produce accurate results.
• The lack of tools, technologies, and expertise in the existing DataOps and MLOps ecosystem is also a challenge in organizing these processes.

DataOps - Best Practices

To ensure the maximum returns from your DataOps initiatives, -

• Just like DevOps, instead of starting these initiatives at a large scale, start small, realize the benefits, and then expand the scope
• Leverage automation for data-related process
• Democratize data across the enterprise
• Use tools that allow easy data analysis by users across all levels within the enterprise

MLOps - Best Practices

Here are some best practices for organizations to develop and deliver consistent, high-performing machine learning models -

• Align the projects with critical business needs
• Ensure seamless communication and collaboration between data scientists, data analysts, software engineers, and subject matter experts
• Take data validation seriously – ensure data quality, integrity, and security
• Demonstrate the value being delivered through appealing visualizations

Over the past couple of years, DataOps and MLOps have seen massive growth. With the proliferation of data, there is an increased interest in data-driven decision-making in enterprises of all sizes. DataOps helps such organizations deploy and monitor data pipelines – quickly and easily. MLOps helps in the deployment and monitoring of machine learning models – with an objective to improve the efficiency and efficacy of these models.

As all products become data products, organizations need platforms like Rubiscape to extract real value from their data. Connect with us to know more. ?