The Growth of MLOps and Predictions for Machine Learning (ML) In 2023
What are the key trends to follow for ML and MLOps? Find out.
Pressured by economic constraints and a limited pool of expert resources, ML experts are developing cutting-edge MLOps tools that will push the boundaries further than ever before, creating more opportunities for advancements in model deployment, explainability, observability, and experimentation, discusses Kilvin Mitchell, technical writer, Wallaroo.AI
Machine Learning (ML) is rapidly developing into a core component in modern business success, with ML-powered industries standing to gain a $1.2 trillion market share over non-ML industries in 2023. As a result, there has been a huge rise in machine learning operations, or MLOps, as businesses focus on integrating ML models in software development and production environments..
As the use of ML continues to increase, the demand for skilled MLOps professionals is also expected to grow. Companies are recognizing the importance of MLOps in helping them to quickly and efficiently deploy machine learning models in production environments, which is driving the growth of this field.
With that in mind, here are five trends and predictions to look for in 2023.
Edge Computing Will Be the Fastest-growing Segment of ML
The increase in ML capabilities’ integration into more edge devices, equipment, and appliances has long demanded a solution that minimizes latency in these autonomous devices. As a result, edge computing will become one of the most important growth areas in ML for 2023 due to the fact that it can be used to improve the performance of systems deployed at the edge by allowing the system to learn from data and adapt to changing conditions. This can be particularly useful in situations where the data being processed is highly variable or where the system needs to operate under a variety of different conditions.
For example, an ML model deployed at the edge of a network might be used to analyze sensor data and make real-time decisions about how to best allocate resources or respond to changing conditions. This could be used, for example, to optimize the performance of an industrial process, improve the accuracy of a predictive maintenance system, or improve the efficiency of a supply chain.
2023 will favor high-performance edge solutions that reduce latency to microseconds and deploy models using large silos of streaming data as businesses work towards enhancing customer experiences.
Advancements in edge computing will not only free servers from intensive processing loads but will allow for the deployment of models in production, even for industries that operate in rural areas with unreliable connectivity. Since MLOps on the edge allows devices to run ML models locally at the data source, it will serve as an invaluable asset for enabling the instant processing of information, reducing transportation/storage costs for telemetry data by only transferring necessary information to the cloud.
See More: How to Overcome Machine Learning Risks
Remote ML Deployment Will Surge in Response to Data Privacy Concerns
The rise in cybercrime and privacy breaches in AI have raised questions about the security implications of ML in data-sensitive industries. 2023 will therefore see a great migration to on-premise computing as a means to restrict unauthorized access to critical data. Companies will be able to disconnect their critical systems from the internet by deploying models offline in air-gapped environments. This will serve as a major resource, particularly for companies and organizations that seek to maximize their information security.
Sustainability Will Be a Top Priority for Enterprise Data Science Teams
Data centers currently account for nearly five percent of global energy consumption. Compute-intensive domains such as conversational analytics, augmented reality, and large language models will drive this figure higher if run-time efficiency is not prioritized as a key consideration in MLOps. In 2023, businesses aiming to scale up their ML operations in production will be on the lookout for sustainable ML solutions that can run more inferences with fewer resources so as to minimize their costs and carbon footprints.
User-friendly ML Will Democratize ML
AI was developed to bring innovative solutions to people in every aspect of their lives. The adoption of AI and ML in the automation of business operations has been a crucial factor in the success and stability of businesses that thrived through the pandemic. Take, for example, Levi Strauss. While store closings were pushing millions of consumers online, the company fast-tracked initiatives that were planned out for months and even years later. Before the pandemic, investing in digital technologies, including AI and predictive analytics, allowed Levi’s to react quickly and decisively as consumers switched to e-commerce channels in droves.
In 2023, there will be a massive stride in making ML accessible to everyday individuals and small business owners through user-friendly interfaces or natural language queries like ChatGPT. The benefits of ML will no longer be limited to enterprises with the best data scientists and programmers. Low coding platforms with drag-and-drop features as well as natural languages that allow for the engagement of a wider audience, will be all the rage in the coming year.
More Data Means More ML
It’s an annual rite to say this year is the year that ML becomes mainstream. While we don’t want to sound like a broken record, we see that all enterprises, not just the digital natives, have been investing in ML capabilities even while they cut back in other areas due to economic uncertainty. In a recent McKinsey survey, 92 percent of respondents thought that their business models would not remain viable at the rates of digitization at that time.
Digitization is bringing in more data than ever about their customers, but actually making use of this volume, variety, and velocity of data requires ML. Going from a prototype in a dev environment to actually integrating ML into the business requires much more understanding around production, like dealing with compliance, scale, and especially drift. The enterprises that will succeed will be the ones that look at production machine learning as more than just deploying a model in a server.
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