MLOps: The Next Big Thing in the Technology Sector

The technology sector is, unsurprisingly, ahead of the curve when it comes to adopting the latest technological solutions and enjoying the benefits they bring. For example, machine learning operations (MLOps) are enabling and governing AI solutions that add value to technology organizations, maintain performance, and improve outcomes. But is the sector living up to its name and realizing the full potential of the technology it uses? And is there room for technology organizations to benefit further from these advanced solutions?

AI Reaches Maturity

As technologies like AI reach maturity, organizations are beginning to understand the opportunities it offers for business outcomes in terms of churn, upselling and next best actions. However, they are also discovering the obstacles that they need to overcome, including finding the right balance for AI. Overusing AI just because it seems to be the latest trend sees organizations disappointed by the outcomes. Oppositely, underusing it sees organizations running the risk of missing out on the latest innovation and ultimately hurting their business.

When the use of AI has been balanced and put into production, it can start to create value at scale, augmenting intelligence, and taking over certain decisions and risks. At this point organizations need to be careful that they can still control AI and its outcomes. In other words, are AI models running correctly and delivering results that are transparent, trustworthy, unbiased, ethical, and aligned with your organization’s culture, vision and mission? This is where MLOps come into the picture, facilitating and managing communication and collaboration between data scientists and operations professionals to increase automation while improving production machine learning quality.

Why Have MLOps Grown in Importance?

Even though AI was defined back in the 1940s, it has taken time to break down the technological limitations surrounding AI for it to become reality. Associated technologies like cloud, deep learning, multiple sources of digital data and lower costs for communication and data exchanges has helped us overcome these limitations. MLOps have followed a similar evolution.

The current push towards AI and MLOps within the technology industry is the result of several different trends.

• Increased Need for Productivity
  While GDP continues to grow, the pace of productivity is actually declining, leading organizations to look for ways to increase their productivity.

• Customer Centricity
  Organizations want to reach each individual customer in a unique way to maximize the customer lifetime value. This means finding ways to leverage their data sources to fully understand individual customers so they can tweak their products and services to deliver exactly what each customer wants, when they want it.
  
  
• Product Personalization
  Customer expectations for personalized products and services have been accelerated due to their access to digital tools. Plus, when faced with a multitude of options, customers now expect organizations to help them to select what they want.
  
  
• Cultural Acceptance
  People have been introduced to different technologies through the media and their personal usage, resulting in increased acceptance of advanced solutions when they are discussed in organizations.
  
  
• Skill Shortage
  Organizations are finding it difficult to find people with the right skills for the right roles. Technology is stepping into this gap to meet this need.
  
  
• Edge Computing
  The rise of edge computing for MLOps is being pushed by customer expectations for increased security, robustness, and sustainability. It also answers organizational demands for a decentralized approach to decision making that doesn’t rely on your organization’s cloud connection.

What Do MLOps Mean for Your Organization?

MLOps have the potential to have a serious impact on your organization and its operations. In addition to AI and MLOps being leveraged to expand the size of your organization, it can also be leveraged in the products and services your organization brings to market and help your organization manage its digital threats while improving the customer experience and being ethical and transparent.

Leveraging AI in the Products and Services Your Organization Sells

One major advantage of being the first to develop specific AI and MLOps applications is being able to sell these solutions to the market. However, this requires that the technology is mature and constantly monitored so it always operates at a high level of quality. 

The Risk of Digital Threats

When it comes to digital threats, it is important not to underestimate the risk. Cybersecurity threats are unavoidable in today’s business climate. AI and MLOps can be used to detect threats and problems on the organization’s network and ecosystem to minimize the risk, but these threats are impossible to eliminate entirely.

With the introduction of the network and information security directive (NIS Directive), the first piece of EU-wide legislation on cybersecurity, regulations are being introduced to try and reduce the risk associated with these threats. If an organization does not comply, they run the risk of being liable if there is a violation. This need for compliance will increase the level of professionalism in the market as organizations realize that they cannot afford to continue using experimental AI and MLOps systems.

Being Ethical and Transparent

Organizations need to ensure that the answers delivered by their AI and MLOps technology remain trustworthy, ethical and transparent. This will enable customers to rely on it when they make decisions even though they don’t understand the complexity of the algorithms behind the technology. A further step is to build in automation that corresponds to your ethics and culture to automatically take an action in certain situations and inform you of the outcome, for example, in response to a warning or error message.

Using Data to Improve the Customer Experience

After gathering a lot of data from different sources, organizations need to ask themselves what they should do with this data. While being able to work from a large, qualitative data lake is an advantage, organizations should not lose focus of their customers and the customer experience to realize that even though the possibilities have increased, they still need to make the right choice for their customers.

Gathering Data

When following GDPR and other regulations, it can be challenging for some technology organizations to access the data they require to learn about their customers, especially as larger technology organizations frequently have exclusive access to larger data sources making them inaccessible for smaller organizations. Without access to this data in real-time, it can be difficult for organizations to know how to adapt their product and service offerings to meet customer needs and expectations.

What Do MLOps Mean for Your Customers?

Successfully implementing MLOps in production also impacts your customers in three main ways:

1.     Predictive Personalization

Customers are increasingly expecting organizations to be able to predict their preferences by offering them a multitude of options and helping them select what they want. Organizations will need to combine AI, MLOps and hyper-automation to achieve this.

2.     Virtual Assistants

Technology is increasingly replacing employees when it comes to the customer service experience. Virtual assistants and chatbots are using AI and MLOps to automate and personalize customer service.

3.     Customer Analytics

The exponential rise in the volume of data gathered by organizations enables AI and MLOps to analyze the behaviors of individual customers and adapt touchpoints to the specific preferences of each customer, increasing the efficiency of applications.

Looking to the Future of MLOps in Your Organization

Even though the technology sector is more data-driven than other sectors, there is still room for improvement. Examples include: fully understanding their customers as individuals rather than taking shortcuts when researching customer behavior, and not assuming that all customers are linear. In other words, just because a group of customers like one specific option, it doesn’t mean that they will all act or respond in the same way.

Is your organization ready to fully benefit from MLOps? Learn more about how MLOps can play an important role in staying relevant and seizing business opportunities in the future.