skip to Main Content
Comet Launches Kangas, an Open Source Data Analysis, Exploration and Debugging Tool for Machine Learning.

2022 Trends in Machine Learning Model Training

The field of machine learning is relatively young but is changing rapidly. More and more organizations are adopting machine learning to transform their offerings and gain a competitive edge. Whether you’re a data scientist working on a new project, an ML engineer looking for an easy way to deploy models, or a business leader hoping to make their organization more innovative and competitive, understanding trends in machine learning model training will be an invaluable success.

Top 5 Machine Learning Model Training Trends in 2022

Machine learning practitioners spend a lot of time on model training. Keeping up on model training trends can make you an invaluable asset to your organization, make your team more efficient, and provide business value to your organization faster. What trends should enterprises and practitioners be watching in 2022?

1. Multimodal Machine Learning

Multimodal machine learning aims to train models involving different data from multiple modalities. Modality is how something occurs or is experienced. It refers to the sensory modalities that serve as our primary means of communication or perception, such as vision or touch.

The goal of multimodal machine learning is to create models that can relate to and handle data from several modalities. The common modalities utilized in machine learning are:

  • Vocal inputs (laughter, moans, intonations).
  • Visual signals (pictures and videos).
  • Natural language (spoken or written).

Multimodal machine learning enables various applications, like audio-visual speech recognition and image captioning. It’s an emerging field with many applications in healthcare, self-driving cars, and robotics.

2. AutoML

Traditional machine learning model training requires significant time, resources, and knowledge to produce, compare, and optimize models. Every step in a typical machine learning pipeline, such as data preprocessing, hyperparameter optimization, feature engineering, and feature selection, is done manually by a machine learning practitioner. With automated machine learning (AutoML), you can expedite the model training process with ease and efficiency and get production-ready models faster.

As the name suggests, AutoML is the process of automating tedious and repetitive tasks in developing and training ML models. It can be applied to various stages of the ML process, such as data preparation, feature engineering, model selection, hyperparameter optimization, neural architecture search, evaluation metrics selection, and results analysis.

AutoML makes machine learning more user-friendly and accessible for organizations without machine learning experts. Because of this, the adoption of AutoML will only grow bigger as more organizations will see the value of ML.

3. Reinforcement Learning

Reinforcement learning is a machine learning model training method based on a reward/punishment system. It uses an agent (an algorithm based on neural networks) that can perceive and interpret environmental cues and learns through trial and error.

In reinforcement learning, the agent learns from direct experiences with its environment. The environment can use a reward/punishment system to assign value to the observations that the agent sees. Positive values are allocated to desired activities, while undesirable behaviors are assigned negative values. The goal is to train the agent to pursue the maximum reward and complete all necessary tasks to arrive at the best solution. Over time, the agent eventually learns to avoid the negative and seek the positive.

Even though the real-world application of reinforcement learning is limited, there’s growing interest in the ML community. The current applications of reinforcement learning include robotics, game simulation, personalized recommendations, and simulation-based optimization.

4. Unsupervised ML

Unsupervised machine learning is a machine learning training method that uses algorithms to learn from unlabeled data. Unlike supervised machine learning, where the algorithm is presented with example inputs and desired outputs, the algorithm discovers patterns and information in the data in unsupervised machine learning.

Let’s explore the two categories of unsupervised machine learning to get a clearer view of how it works. The first technique is clustering, where the algorithm categorizes similar items. The clustering algorithm finds the commonalities between the data objects and categorizes them based on the existence or lack of those commonalities. On the other hand, association finds the relationship between variables in an extensive database. By discovering this relationship, businesses can develop effective cross-selling strategies and recommendation engines.

ML teams can use unsupervised machine learning algorithms for various applications, such as market segmentation or identifying similar groups of customers. Recommendation engines also use unsupervised machine learning algorithms to analyze customers’ past behavior and make recommendations based on those behaviors. For example, Netflix uses an algorithm called “Collaborative Filtering” that analyzes viewers’ activity history and makes suggestions based on what similar users have watched.

5. TinyML

Tiny Machine Learning or TinyML is the development of machine learning models on small or low-powered devices like microcontrollers. Microcontrollers are small computers inside a larger device charged with one program or task. They typically consume power in microwatts or milliwatts, a thousand times less power consumption than a standard consumer CPU or GPU. Because of this, devices can run on a battery that lasts weeks, months, or even years. TinyML enables low power, low latency, and low bandwidth inference at edge devices.

There are many tools to get started on TinyML. Popular hardware includes the SparkFun Edge boards and the Arduino Nano 33 BLE Sense board. For frameworks, Tensorflow Lite for Microcontrollers is the most popular. To learn more about TinyML, check out The tinyML Foundation, the leading community of researchers, data scientists, engineers, and product managers that provide education on the subject.

Even though TinyML is a relatively new field, tech giants such as Google, Apple, and Amazon have been using it for years. Popular applications of TinyML today are the wake words “OK Google,” “Alexa,” and “Hey Siri.”

Shaping Machine Learning’s Future

Machine learning is critical in developing a company’s products or services. It helps customers get the most out of a product or service by providing them with recommendations and other helpful information that enhances their experience. The five trends mentioned above will impact various industries, and those who adopt them today will have the unique opportunity to shape these trends in real time.

With all of the new developments in machine learning, keeping up with the trends will give your organization a competitive edge in the market. Check out the Comet blog and discover more trending topics in machine learning.

Team Comet

Back To Top