The Importance of DataOps in Enterprise Data Science and Machine Learning Projects
This blog gives a brief overview of the DataOps methodology, it’s importance and talks about how it’s a crucial component of Data Science and Machine Learning projects in an organization. Through practical scenarios, it attempts to shed light on DataOps’ best practices and benefits.
Importance of DataOps – Secret Of Machine Learning & Data Science Success In An Enterprise
Data is the new oil, and unsurprisingly, modern enterprises are leveraging it in innovative ways to gain a competitive advantage in the marketplace.
Organizations are collecting data to drive multiple cutting-edge, data-heavy applications to boost business revenues and eliminate operational inefficiencies. Large volumes of data flow through the various departments in an organization, and you need a dedicated team to manage this data. This division of an organization that deals with curating and utilizing information are commonly known as the DataOps team.
What Is Dataops?
A non-specialist would define DataOps (short for Data Operations) as an agile framework to quickly develop and deploy data-intensive applications for artificial intelligence, machine learning, data science, and more.
An agile methodology involves breaking up a complex project into smaller stages and getting stakeholders to iterate and improve at every stage independently continuously. Put simply; it helps businesses gain rapid data insights without much human intervention.
The DataOps team manages the entire data pipeline in an organization, making it seamlessly available for the various stakeholders – principal architects, infrastructure engineers, data scientists, programmers, and end-users.
Let us give you a practical example of DataOps in action. Say your own e-commerce business and want to reduce website drop-offs. You could utilize existing customer data to build a recommendation system that suggests relevant products at precisely the right time. This might keep them hooked longer and minimize cart abandonment.
All this can be made possible only if your data science, engineering, and product teams have access to the right data and work in tandem to successfully ship the feature.
Data Challenges in Machine Learning and Data Science Applications
It’s not easy to transform raw data into actionable business insights. It is even harder to assimilate these insights into the business value chain to monetize said data.
Here are some challenges businesses face while integrating data into their ML (Machine Learning) and DS (Data Science) initiatives:
- Losing sight of business goals by overfocusing on the data,
- Multiple departments working in silos, thus making it challenging to create data synergy between the different teams,
- Building and deploying data infrastructure is a tedious process and takes a lot of time,
- Not enough time is spent on core activities like defining models, fine-tuning parameters, prediction, analytics, etc.,
- Even in production, the models need continuous evaluation and iteration to make it more accurate, and redeployment is cumbersome,
- It’s challenging to get new users to adopt the ML and DS projects.
How to overcome Data Challenges in Machine Learning and Data Science Applications?
The solution, if you haven’t guessed already, is efficient DataOps.
Organizations must have a few ideas in mind before starting on large-scale ML and DS projects. There should be a self-serve interface where different stakeholders can access relevant data quickly and intuitively.
The data landscape should be standardized while keeping the data architecture readily accessible.
Scenarios of Tim, Kate, and Bill:
Let us take a look at three scenarios where importance of acceptable DataOps practices can prove to be the difference between project success and failure:
Tim is an ML engineer in charge of building a prediction engine for industrial compressor failure. He’s aware that he needs recurrent neural networks, which are computationally-heavy and require huge bandwidths, fast connectivity, and robust storage. Requisitioning that kind of compute capacity can be time-consuming. He finally gets everything in place in 2.5 months, but his manager has moved on to other projects by that time.
Kate is a data scientist tasked with building a video analytics tool for process control in manufacturing. She employs convolutional neural networks to do lightning-fast image classification. However, due to a shortage of edge compute capacity, she cannot address cloud latency issues, and her project gets shelved.
Bill has developed a robotics model to enable bots to spot anomalies in industrial equipment at gas pipelines. It has been stress-tested and deployed to production. Despite this, the robots fail to identify a show-stopping abnormality a few months later, and just like that, the robotics project comes to a standstill.
The common denominator in all three scenarios? Transparent business KPIs but lack of organizational coordination and data management processes.
Importance of DataOps – How DataOps could have helped Tim, Kate, and Bill:
Agile Data Architecture
An organization should be elastic when it comes to provisioning compute power on an on-demand basis, the inability of which was what led to Tim’s project failure. Data might be scattered across on-premise and cloud platforms, so a synchronized data management and tech deployment strategy are critical.
Data Pipeline Automation
The data pipelines requisitioned by a data engineer should automatically scale infrastructure under-the-hood. Very little human intervention should be sought so that a person like Tim can focus on the more complex work. For instance, pipelines should be codified to scale up efficiently with Spark and Hadoop.
Workflows For Shipping Models
Once they’re ready, data models should be packaged, shipped, and inserted into the appropriate operational workflow. Kate’s inability to deploy the model at the edge of her IoT infrastructure led to its demise. Sometimes these workflows are mission-critical for business processes like manufacturing, customer support, sales, and models that might need to be implemented in the cloud.
Rigorously Testing The Models
Bill learned the hard way what can happen when data models don’t pass through a rigorous QA Service. Before a model is finalized, it should be benchmarked compared to other models across parameters like accuracy, computational and maintenance costs. Remember, even if the data is 100% precise, data science is not and has error margins, which need to be minimized.
Track And Measure
Since ML and DS projects are so iterative by nature, you need to track and measure obsessively relevant success metrics. Model accuracy, computational infrastructure usage, and training speed can be visualized and tracked through many alerting tools and reporting dashboards.
Data Science, Machine Learning, and Artificial Intelligence Development Service are the future of technology. And the future is already here. Coupled with good DataOps, these technologies can be utilized much more effectively at scale, driving the business upward in the process.