The Benefits Of DataOps In Enterprise Data Science and Machine Learning Projects

What is DataOps methodology? What are the benefits of DataOps in Data Science and Machine Learning projects in an organization. Through practical scenarios, it attempts to shed light on benefits of DataOps and best practices of it.

What Is DataOps?

A non-expert would define DataOps as an agile framework. It is for quickly making and using data-heavy applications for AI, machine learning, data science, and more. Agile methodologies break up complex projects into small stages. Stakeholders iterate and improve at each stage. They do so independently and continuously. Put simply; DataOps benefits businesses gain rapid data insights without much human intervention.

The DataOps team manages the whole data pipeline in an organization. They make it easy for many types of stakeholders to use. These include: 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 use existing customer data. You could use it to build a system that recommends relevant products at the perfect time. This might keep them hooked longer and minimize cart abandonment. This can happen only if your data science, engineering, and product teams can access the right data. They must work together to ship the feature.

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Benefits of DataOps – Secret Of Machine Learning & Data Science Success In An Enterprise

Data is the new oil. Unsurprisingly, modern businesses are using it with DataOps in new ways. They do this to gain competitive benefits in the market. So, organizations are collecting data to drive many cutting-edge, data-heavy apps. These apps boost business revenues and slash inefficiencies.

If lots of data flow through an organization’s departments, you need a team to manage it. This is a division of an organization that deals with curating and using information. They are commonly known as the DataOps team.

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Data Challenges in Machine Learning and Data Science Applications

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It’s not easy to transform raw data into actionable business insights. It is also 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.

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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. You must standardize the data landscape while keeping the data architecture readily accessible.

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Scenarios of Tim, Kate, and Bill:

Let us look at three scenarios. In these, the value of good DataOps can be the difference between project success and failure.

Scenario 1

Tim is an ML engineer in charge of building a prediction engine for industrial compressor failure. He knows he needs recurrent neural networks. They are computationally-heavy and need huge bandwidth, 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.

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Scenario 2

Kate is a data scientist tasked with building a video analytics tool for process control in manufacturing. So, 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.

Scenario 3

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 spot a show-stopping issue a few months later. So, 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.

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Benefits of DataOps – How DataOps could have helped Tim, Kate, and Bill

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Agile Data Architecture

An organization should be flexible in adding compute power as needed. So, the lack of this flexibility led to Tim’s project failure. Data might be scattered across on-premise and cloud platforms. So, a synced data management and tech strategy are key.

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, it is crucial to package and ship them. So, insert the data models into the right workflow. Kate’s inability to deploy the model at the edge of her IoT infrastructure led to its demise. Sometimes these workflows are also mission-critical. They are vital for business processes like manufacturing, customer support, and sales. They are for models that might need to be in the cloud.

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Rigorously Testing The Models

Bill learned the hard way what can happen when data models don’t pass through a rigorous QA Service. Before finalizing a model, benchmarking is crucial more than 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. You need to minimize that.

Track And Measure

ML and DS projects are so iterative. You need to obsessively track and measure relevant success metrics. Also, you can see the model’s accuracy, infrastructure use, and training speed. You can do so through 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. With good DataOps, you can use these technologies better at scale. This will drive the business upward.

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