MLOps seems to be the missing link between machine learning, data science and data engineering. What is striking, is the way MLOps is emerging to seamlessly unify these functions than ever before. It is enabling professionals and advanced systems to consistently deploy ML algorithms and solutions for improved productivity.
On one hand, new trends in MLOps aim to meet the evolving challenges in scaling ML, whereas on the other, advanced MLOps applications can solve a variety of errors and quality issues based on proven architectural principles.
Given the varied advantages, MLOps is gaining prominence with the industry standing to gain from its 1.2 trillion dollar market share. So what are the trends that are most likely to trigger the development, growth and adoption of ML in 2023? Here’s our take on a few trends:
1. User-friendly ML Will Democratize Use of ML
2023 will see a massive leap in making ML accessible to its everyday users through user-friendly interfaces and natural language query platforms like ChatGPT. ML will continue to single-handedly benefit data scientists, programmers, enterprises through natural languages that will engage with a wider audience at large.
2. Edge Computing Will Grow Fast
With an increase in ML capabilities’ to integrate with more edge devices to reduce latency in these autonomous devices, edge computing is all set to grow leaps and bounds in 2023. When implemented to learn and adapt to changing conditions, edge computing will prove most beneficial where data being processed is highly variable or where systems need to operate under different conditions. For example, an ML model deployed to analyze sensor data can make real time decisions to improve machine accuracy via predictive maintenance or even the efficiency of a supply chain.
3. Auto ML Will Take Center Stage
AutoMl provides various simplex and accessible solutions that do not require a pre-defined ML expertise. One of these tools is Data Labeling. With ML automating much of the data labeling process, the chances of human error are significantly reduced. This brings down personnel expenses, allowing businesses to concentrate more on data analysis.
4. ML Will be Deployed for Data Privacy
A rise in cybercrime in data-sensitive industries is raising questions about the implication of ML in securing private data. 2023 will witness industries migrating to on-premise computing to tackle unauthorized access to critical data with organizations opting to deploy offline models in air-gapped environments. As a useful resource, ML will be extensively used by organizations looking to maximize their information security.
5. Volume, Variety, Velocity Will Merge With ML
Thanks to digitization, more data – in terms of volume, variety and velocity – is generated daily for several organizations. 2023 will be the year several businesses invest in the capabilities of ML including mapping customer data to better match their preferences. ML will play an integral part in understanding user journey from intent, need and consumption and enable businesses to build more sustainable models and target more specific consumers.
6. Data Drift Focus Will Increase
While deploying models, one of the critical challenges that data scientists face is drift. ML enables the use of a practical guided view to mitigate this drift. This year ML will be utilized to identify impactful drift while providing ground truths, bringing forth various methods to identify drift and its consequences on model performance. MLOps in the near future will provide tools for the right root cause analysis for identifying drift and guide data scientists towards the right solutions.
7. Sustainability Will be a Priority for Data Scientists
Data centers account for nearly 5% of global energy consumption and computer-intensive domains tend to drive this figure higher. Domains that focus on conversational analytics and augmented reality must practice run-time efficiency as a key consideration in MLOps. This trend will catch up in 2023, and soon businesses will aim to adopt sustainable ML solutions that can run more inferences with lesser resources.