In this article, we’ll look at some of the most important factors and trends that, according to different experts and Think Tanks, will be driving enterprise ML development in 2022 (and most likely beyond).
Companies looking to leverage ML for their end-users or internal needs must step into 2022 with a clear roadmap of what they want to achieve in the first place in terms of measurable results. As per the Crowd Expert, this year, AI adopting companies will be focused on:
And of course, unique business use case creation will remain a strong focus among AI/ML adopting organizations in 2022 and beyond.
On top of that, businesses need a much faster return on investment in ML. It means that both ML platform vendors and custom ML development providers will need to quantify their value with greater accuracy and real-time insight to retain customers and attract new ones.
Proper use of the core ML elements improves decision-making accuracy, speed, and quality.
This year, ML strategies will gain momentum across organizations, and the following are trends that businesses will have to incorporate into their ML roadmaps for 2022:
Adaptive machine learning demonstrates the potential for improving and fine-tuning cybersecurity, remote site security, manufacturing quality management, and industrial robotics systems.
Adaptive ML is expected to find a wider spread across a spectrum of use cases defined by how quickly their context data, conditions, and actions change.
For instance, in manufacturing, combining telemetry data from visual IoT sensors with adaptive ML applications can immediately identify defective products and take them off the production line.
Eliminating the hassle of returning defective products to customers can increase customer loyalty while lowering costs. Given chronic labor shortages, combining adaptive ML with robotics can help manufacturers meet customer product needs consistently. Adaptive ML is also the foundation of autonomous self-driving vehicle systems and intelligent collaboration robots that quickly learn to collaborate on simple tasks through iteration.
Cogitai, Google, Guavus, IBM, Microsoft, SAS, Tazi, and other DS and ML platforms can be used to streamline and automate manufacturing processes and operations, improve risks assessment, and increase bottom-line savings.
ML platforms that are not designed to be flexible and adapt collaboration workflows to the most sophisticated user needs can cost weeks of model development time and overblown budgets. Collaboration tools and workflows need to go beyond simple Q&A forums and provide more efficient cross-modal data and code storages that everyone can safely use across the enterprise. There should also be support for data and model visualization and models export features.
The essential elements of collaboration to meet the demands of the data scientists include information exchange and code sharing at every stage of the modeling process, tracking data and data lineages, and version control and model lineage analysis. Domino, Dataiku, Google, Microsoft, SAS, TIBCO, RapidMiner offer collaborative workflow support.
MLOps will have a breakthrough year as organizations gain more experience scaling models for faster deployments and better tracking business results.
Reducing cycle times for creating and launching new models is one of the key metrics for evaluating ML projects in enterprises today. Each ML vendor offers a different version of MLOps support. Enterprises considering an ML strategy need to analyze how each platform of interest handles model creation, management, maintenance, model and code reuse, updates, and management.
A software development partner specializing in custom ML models training and solutions development can help build and fine-tune the MLOps function to provide greater model scalability and security in 2022. Any business must understand MLOps differentiators, including model taxonomy, version control, model maintenance, monitoring, and code and model reuse. It’s also essential to ensure that MLOps workflows are measurable using metrics and KPIs critical to financial decision-makers and business owners. It can be challenging for internal IT teams with little ML experience.
Some time ago, a global online survey and insights pure play company reached out to us for help building an AI-based solution that would enable them to identify and track human behaviors inside a retail store without breaching confidentiality.
We built a three-person team to build and train a custom ML model that can detect and recognize everyone in the store and create standalone videos from the surveillence footage based on the person, location, tag, and other parameters.
On the one hand, our custom ML solution helped the Client reduce 10K hours of video surveillence to just 2 hours of highly targeted footage. On the other hand, it helped the Client’s team to save 90% of time needed to codify video for individual behavior tracking.
You can learn more about this project here.
The current and next generations of connected devices with built-in sensors for collecting biometric data are some of the most sophisticated ML models you can create today.
Machine learning is an ever-evolving process that requires large, varied, and carefully labeled datasets to train ML algorithms. But collecting and labeling datasets with millions of items taken from the real world is time-consuming and expensive. This has drawn attention to synthetic data as the preferred training tool.
Synthetic data is information generated by computer simulations, not collected or measured in the real world. Although artificial, it should reflect real-world data and have the same mathematical and statistical properties. Unlike real-world data, ML specialists have complete control over a synthetic dataset. This allows them to control the degree of marking, sample size, and noise level. It also helps address privacy and security concerns when real-world data use is associated with confidential and personal information.
Synthetic data makes it much easier for ML practitioners to publish, share and analyze synthetic datasets with the broader ML community without worrying about personal information disclosure and the wrath of data protection authorities. Synthetic data is widely used in self-driving vehicles, robotics, healthcare, cybersecurity, and fraud protection.
Google and Uber use it extensively to improve autonomy in their self-driving cars. Similarly, Amazon reportedly uses synthetic data to train its Alexa language tool.
According to Gartner, synthetic data can:
Machine learning professionals need to keep a few things in mind to get the most out of synthetic data. First, they need to make sure that the dataset adequately emulates their use case and adequately represents the production environment in terms of the examples’ complexity and completeness.
They also need to make sure the data is clean. But what’s more important –ML professionals need to understand that applying synthetic data may not work in their particular case. Our rinf.tech AI developers stress that determining if synthetic data can potentially solve the problem is paramount. This assessment should be done before launching an ML project and should never be an afterthought.
Transfer learning is an ML research problem that focuses on retaining knowledge gained from solving one problem and applying it to another, related problem. For example, the knowledge gained from learning to recognize cars can be applied when trying to recognize trucks.
The essence of transfer learning is to reuse existing trained ML models to take advantage of the development of new models. This is especially useful for data science teams working with supervised ML algorithms who need labeled datasets for accurate analysis. Rather than starting a new supervised ML model, data scientists can use transmission alignment to tune models for a given business goal quickly.
Additionally, transfer learning modules are becoming increasingly relevant in process-oriented industries that rely on computer vision because of their scale for labeled data. ML platforms offering transfer learning include Alteryx, Google, IBM, SAS, TIBCO, and others.
Organizations must focus primarily on use cases and metrics and understand that extreme model accuracy may not deliver business value.
One of the most common problems when building supervised machine learning models, especially when there is a lot of telemetry from sensors and endpoints, is the tendency to constantly tune the models to get another degree of accuracy. Factory floor telemetry data can be sporadic and vary depending on the number of cycles, the frequency and speed of a particular machine, and many other factors.
It’s easy to get carried away by what the real-time telemetry data from the manufacturing floor says about the machines. Still, backtracking to see what the data says about factory floor productivity and its impact on profit needs to remain the focus as a primary target.
Machine learning went through its testing phase a couple of years ago and is now used in most industries. ML has so many different learning models that make it versatile enough to be used in many fields. However, the problem arises when you have many models to focus on or insufficient and poor-quality data to use for ML models training.
To do this, you need help managing your model. In addition, you need help defining the right ML platforms and tools whose capabilities will be best tailored to your business goals and needs.
Hiring an external team of AI consultants or joining forces with an ML-specialized custom software development provider can be a more cost-effective and efficient option than developing and training models in-house. Besides money, you can save time due to learning curve elimination, the provider’s access to synthetic datasets and pre-trained models, and a pool of top DS and AI dev talent that your in-house recruitment team or local agency may have difficulty finding and hiring fast enough to ensure short time to market.
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