Recent innovations in machine learning (ML) have made many tasks more feasible, efficient and precise than ever before. It is being incorporated in several companies, including tech giants like Google, Apple, Facebook, Netflix, and eBay.
With the surge in demand and interest in ML technology, various new patterns are ascending. For example, Google’s BERT transformer neural network is a new algorithm that promises to revolutionise natural language processing. Understanding the possibilities and recent innovations of ML technology is essential for businesses to see what’s next inside the space of ML.
For the next two years, these are the significant trends and developments we can expect in the field of ML.
Trend #1: TinyML
In a world increasingly driven by IoT solutions, TinyML makes its way into the mix. While large scale machine learning applications exist, their usability is fairly limited. Smaller scale applications are often necessary. It can take time for a web request to send data to a large server to be processed by a machine learning algorithm and then sent back. Instead, a more desirable approach might be to use ML programs on edge devices.
By running smaller scale ML programs on IoT edge devices, we can achieve lower latency, lower power consumption, lower required bandwidth, and ensure user privacy. Latency, bandwidth, and power consumption are significantly reduced since the data doesn’t need to be sent to a data processing centre. Privacy is also maintained since the computations are made entirely locally.
This trending innovation has many applications in sectors like predictive maintenance for industrial centres, healthcare industries, agriculture, and more. These industries utilise IoT devices with TinyML algorithms to track and make predictions on collected data. For example, Solar Scare Mosquito is an IoT project which uses TinyML to measure the presence of mosquitos in real time. This can generate early warning systems for disease epidemics from mosquitos, for example.
Also Read: Which Tech Trends Will Impact Your Business?
Trend #2: AutoML
AutoML aims to make building machine learning applications more accessible for developers. Since machine learning has become increasingly useful in various industries, off-the-shelf solutions have been in high demand. Auto-ML aims to bridge the gap by providing an accessible and straightforward solution that does not rely on the ML-experts.
Data scientists working on machine learning projects have to focus on preprocessing the data, developing features, modelling, designing neural networks if deep learning is involved in the project, post processing, and result in analysis. Since these tasks are very complex, AutoML provides simplification through the use of templates.
An example of this is AutoGluon, an off-the-shelf solution for text, image, and tabular data. This allows developers to quickly prototype deep learning solutions and get predictions without data science experts.
AutoML brings improved data labelling tools to the table and enables automatic tuning of neural network architectures. Traditionally, data labelling has been done manually by outsourced labour. This brings in a great deal of risk due to human error. Since AutoML aptly automates much of the labelling process, the risk of human error is much lower. This also reduces labour costs, allowing companies to focus much more strongly on data analysis. Since AutoML reduces these kinds of costs, data analysis, artificial intelligence, and other solutions will become cheaper and more accessible to companies.
Trend #3: Machine Learning Operationalization Management (MLOps)
MLOps is a practice of developing machine learning software solutions with a focus on reliability and efficiency. This is a novel way of improving how machine learning solutions are developed to make them more useful for businesses.
Machine learning and AI can be developed with traditional development disciplines, but the unique traits of this technology mean that it may be better suited for a different strategy. MLOps provides a new formula that combines ML systems development and ML systems deployment into a single consistent method.
One of the reasons why MLOps is necessary is that we are dealing with more and more data on larger scales which requires more significant degrees of automation. One of the advantages of MLOps is that it can easily address systems of hierarchy. It’s challenging to deal with these problems at larger scales because of small data science teams, gaps in internal communication between teams, changing objectives, and more.
When we utilise business objective-first design, we can better collect data and implement ML solutions throughout the entire process. These solutions need to pay close attention to data relevancy, feature creation, cleaning, finding appropriate cloud service hosts, and ease of model training after deployment to a production environment.
MLOps can be a great solution for enterprises at scale by reducing variability and ensuring consistency and reliability.
Trend #4: Full-stack Deep Learning
Wide spreading of deep learning frameworks and business needs to be able to include deep learning solutions into products led to the emergence of a large demand for “full-stack deep learning”.
What is full-stack deep learning? Let’s imagine you have highly qualified deep learning engineers that have already created some fancy deep learning model for you. But right after creating the deep learning model, it is just a few files that are not connected to the outer world where your users live.
As the next step, engineers have to wrap the deep learning model into some infrastructure:
- Backend on a cloud
- Mobile application
- Some edge devices (Raspberry Pi, NVIDIA Jetson Nano, etc.)
- Full-stack Deep Learning
The demand for full-stack deep learning results in creating libraries and frameworks that help engineers automate some shipment tasks and education courses that help engineers quickly adapt to new business needs.
Also Read: Top 5 Trends In Data And Analytics
Trend #5: General Adversarial Networks (GAN)
GAN technology is a way of producing stronger solutions for implementations, such as differentiating between different kinds of images. GAN produces samples that must be checked by discriminative networks, which toss out unwanted generated content. Similar to branches of government, it offers checks and balances to the process and increases accuracy and reliability.
It’s important to remember that a discriminative model cannot describe the categories that it is given. It can only use conditional probability to differentiate samples between two or more categories. Generative models focus on what these categories are and distribute joint probability.
A valuable application of this technology is for identifying groups of images. With this in mind, large scale tasks such as image removal, similar image search, and more are possible.
*MobiDev, a machine learning software development company, has listed the latest innovations in machine learning to benefit businesses in 2021-2022.
