The Impact of ML DataOps on Different Sectors

ML DataOps

Know about how ML DataOps is creating an effect on multiple sectors worldwide

Commercial machine learning (ML) applications have progressed from conceptualization to testing to deployment over the past decade. The need for efficient and scalable operations has led to the establishment of MLOps as a vital function within firms developing artificial intelligence (AI) as the industry has progressed through this cycle. As a result, it is critical to understand what ML DataOps is and how it affects various sectors.

What is ML DataOps?

ML relies heavily on the collection, analysis, and creation of data. Over the past year, the AI ​​ecosystem has witnessed a push to move to a more data-centric approach from the current model-centric one. And data is the single biggest differentiation in ensuring the success of ML models in the real world.

In the wake of this development, ML DataOps is in the spotlight as it allows us, if correctly structured, to handle data at scale as it flows through the cyclical journey of AI training and deployment. This becomes highly important to ensure the sustainability of the resulting AI solutions as there is a need for a transition from testing to production, which must be tackled through repeatable and scalable processes. Moreover, various insights can be derived from the data that can help customers accelerate the process of developing production-grade ML models.

Companies focus on different aspects of the data pipeline within the ML DataOps ecosystem. However, the solutions provided broadly fall under the following categories:

1. People: The role of a skilled human in ML development is crucial. While technology has made radical advancements, it cannot tackle all problems. Thus, humans have a crucial role in providing complex solutions that technology can’t.

2. Technology and tooling: Technology advancements result in improved human efficiency input within technical support and automation. Similarly, effective tools development streamlines different functions that take place within the ML DataOps pipeline.

3. End-to-end processes: Efficient, insight-driven processing can be a big saver of cost and time when dealing with enterprise-grade data pipelines. Thus, some companies focus heavily on end-to-end solutions for such streamlined processes.

2022 is the year of ML DataOps

So far, 2022 has been a year of remarkable growth. Here’s why this year will see further investment and development.

  • First, AI products are going into production and this is huge. Industries like finance and retail are taking cutting-edge models to production, which will provide feedback loops once released. A feedback loop of results will force enterprises to adapt their ML data operations to meet the evolving demands of their models. Algorithms in the field will come back with edge cases, which data operations will work to resolve before the algorithm is redeployed.
  • Second, data pipelines require scale and experts-in-loop. Scaling for efficiency, enterprises will need to ensure that annotators understand the domain and product requirements.. This will, in turn, result in faster market releases as they continue to improve the performance of their models.
  • Lastly, end-to-end AI data solutions are coming to the market. As AI advances, so does the technology in the background. The combination of technology and human-in-the-loop expertise gives enterprises a true end-to-end solution as they move to deploy their models in the field. By bringing together the right expertise, judgment, and technology, the highest quality data possible will be generated.
Using the right processes

Technology is only as good as your ability to use it properly, which is why enterprises building AI applications must leverage the right processes across their ML DataOps. Leaning on AI data solutions providers like iMerit gives companies access to domain experts who can guide every phase of a company’s ML DataOps process, including requirements definition, workflow engineering, technology and tool selection, domain skill identification, execution, evaluation and refinement, and analytics .

Impact across various sectors

Healthcare: Since the onset of the COVID-19 pandemic, healthcare has taken center stage across the globe. There are several challenges we need to tackle to make it accessible and impactful.

Intelligent, data-driven insights enable organizations to predict the right clinician mix needed for a specific department. It can also aid in creating a value-based ecosystem by automating clinical operations such as investments in physician recruiting, clinical staff scheduling, and clinical systems.

DataOps can assist in creating patient-centric systems to deliver enhanced operating processes and better customer engagement. Such DataOps-led architecture can help assess tools and capabilities to identify and recommend patient-centric approaches to improve connectivity, engagement, and collaboration with patients.

Finance and Insurance: The sheer amount of data collected by financial services has prompted the industry to adopt technology-driven solutions to achieve a competitive edge.

Employing innovative data and analytics capabilities can have a huge impact on the financial services sector, from decision-making to innovation. These smart tools enable financial service providers to optimize data analysis and enable companies to combine human expertise and machine intelligence to build a credible ecosystem. For example, data analytics can empower banks to gather customers’ insights and channel this into strategic decisions for introducing new products and improvising current business models.

The use of AI and data-driven tools can also lower risk for banks with more effective evaluations and judgments based on risk profiles during credit applications, by considering more targeted details about an individual or business who is applying.

Automobile: Countries are taking note of the rising need for and potential of autonomous vehicle (AV) technology and building initiatives to nurture its growth. For example, the US rolled out a $1 trillion infrastructure bill that makes numerous suggestions for modernizing infrastructure to facilitate the widespread adoption of AVs and mobility. However, manufacturers and innovators still need to master the art of creating AI models to perform on any road.

With modern transportation at an all-time high, one of the biggest challenges we face in the 21st century is reducing the number of road accidents and safety breaches. AI-led solutions have the potential to significantly assist human drivers and enable driverless mobility. It’s not surprising that the sector has attracted many global leaders in AI, software development, and device engineering.

Retail: The industry collects great volumes of data, from product catalogs and customer information to customer queries and complaints. This data could be overwhelming for decision-makers trying to solve a problem. Moreover, retail is one sector that appeals to all human senses, be it touch, smell, hearing, or sight. We need data operations to make sense of the information collected in any format – audio, video, or text. However, especially in retail, we also need human abilities to dive deep into the intricacies of consumer behavior and derive insights for effective decision-making. Data-driven solutions not only help retail businesses analyze the enormous volume of data but also accelerate decision-making for this dynamic industry.

The eventual goal of industries adopting AI and data solutions is to build an ecosystem that can independently learn and develop to aid in decision making. This, along with human-in-the-loop processes, provides the right blend of technological innovation and human intelligence at work to drive business goals and problem-solving.


Sudeep George, VP of Engineering, iMerit


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