The machine learning field is relatively new but it’s changing at a rapid pace and the demand for machine learning and artificial intelligence technologies seems to be growing by the day. As ML engineers, we have to seek more efficient and effective ways of preparing data and building models.

Whether you’re an expert or a newbie in machine learning, you must keep an open mind toward the latest developments in the field. Below are some of the newest machine learning techniques. All of them appear to have interesting use cases.

1. Automated Machine Learning (AutoML)

AutoML is a major deal in machine learning. Its first research group was founded in 2013 by Prof. Frank Hutter at the University of Freiburg.

Automated Machine Learning (AutoML) is the process of automating time-consuming and repetitive tasks involved in machine learning model development. With AutoML, you can design effective and sustainable models that can help to improve efficiency and productivity.

Traditional machine learning encompasses several tasks including cleaning data, selecting the appropriate features, specifying the model family, optimizing model hyperparameters, designing the topology of neural networks, processing models, and analyzing the results.

These tasks are time-consuming and require a great deal of expertise in machine learning. However, AutoML has introduced off-the-shelf machine learning methods to help automate the entire process. It is particularly useful when dealing with large amounts of data.

Google AutoML code sample:

from google.cloud import automl# TODO(developer): Uncomment and set the following variables
# project_id = “YOUR_PROJECT_ID”
# dataset_id = “YOUR_DATASET_ID”
# display_name = “YOUR_MODEL_NAME”client = automl.AutoMlClient()# A resource that represents Google Cloud Platform location.
project_location = f”projects/{project_id}/locations/us-central1"
# Leave model unset to use the default base model provided by Google
metadata = automl.TextClassificationModelMetadata()
model = automl.Model(
 display_name=display_name,
 dataset_id=dataset_id,
 text_classification_model_metadata=metadata,
)# Create a model with the model metadata in the region.
response = client.create_model(parent=project_location, model=model)print(“Training operation name: {}”.format(response.operation.name))
print(“Training started…”)

Automated ML makes machine learning more user-friendly and empowers those without extensive programming language to implement ML solutions. It allows for faster and more accurate outputs, agile problem-solving, and also leverages data science best practices.

There are several AutoML software platforms for you to work with. But we’ll mention the ones adopted by most organizations and ML Engineers today. They include: Google AutoML, Azure Automated Machine Learning, Auto Keras, and Auto-sklearn.

2. Machine Learning Operationalization Management (MLOps)

Inspired by traditional DevOps, MLOps seeks to remove silos of traditional software development.

MLOps is a function of machine learning that aims to streamline the production, maintenance, and monitoring of machine learning models. It makes it possible for data scientists and machine learning engineers to collaborate and improve model development and production.

MLOps ensures that continuous integration and deployment (CI/CD) practices are implemented throughout the machine learning lifecycle. It’s useful for every phase including data gathering and analysis, model training and development, model monitoring and retraining, and more.

MLOps Architecture Design:

Machine learning development is also known to have issues with team communication, the construction of proper ML pipelines, scalability, and the management of sensitive data at scale. But, an MLOps approach can help to facilitate the management process and automate the deployment of machine learning applications.

MLOps principles for reproducibility:

There are a number of tools for implementing MLOps in machine learning projects. You can choose from open-source, proprietary software, Saas, and on-premise MLOps tools. Some of the best MLOps tools include Comet, Amazon SageMaker, Azure Machine Learning, and Google Cloud AI Platform.

Innovation and academia go hand-in-hand. Listen to our own CEO Gideon Mendels chat with the Stanford MLSys Seminar Series team about the future of MLOps and give the Comet platform a try for free!

3. TinyML

TinyML was popularized by Pete Warden, the “founding father” of TinyML, and Daniel Situnayake, an early tinyML engineer.

TinyML is concerned with the development of machine learning algorithms that can operate on small or low-powered devices such as microcontrollers. Machine learning models at edge devices enable low latency, low power, low bandwidth, and also ensure user privacy.

TinyML allows IoT devices to analyze data using limited energy and computing power as well as collect only useful data. This is a useful solution for high-energy consumption and the collection of useless data on local devices. It helps to integrate machine learning and IoT.

ML frameworks for IoT edge devices:

Microcontrollers are great for bringing machine learning to edge devices. They are inexpensive and consume very little power. They also allow us to collect and analyze large amounts of data for very cheap. However, they require the development of ML algorithms that can operate with little local memory or computing power.

Some edge devices and their hardware specs:

Wake words such as “Ok Google,” “Alexa,” and “Hey Siri” are a few examples of TinyML. Experts believe that more ML models will be trained at edge devices in a few years to come. The Arduino Nano 33 BLE Sense and TensorFlow Lite Micro are some of the most popular hardware and frameworks being used for deploying ML models on edge.

4. General Adversarial Networks

GANs were introduced by Ian Goodfellow and his colleagues in 2014 at the University of Montreal.

General Adversarial Networks (GANs) are new machine learning trends that produce samples to be checked by discriminative networks that can delete unwanted content. Just like the branches of government, GANs offer checks and balances to ensure accuracy and reliability.

GANs are an approach to generative modeling (an unsupervised learning approach where the model learns to discover patterns in input data). They are known for using convolutional neural networks, a deep learning method, to recognize, analyze, and classify visual images.

The GAN structure:

Generative Adversarial Networks help to train a generative model by presenting the problem as a supervised learning problem with two sub-models. Here’s how they work:

• The Generator Model

This is trained to generate new datasets e.g. producing output that resembles the real data.

Backpropagation in Generator training:

• The Discriminator Model

This is trained to compare and distinguish the generated data from the real data.

Backpropagation in Discriminator training

These two sub-models are usually trained together in an adversarial zero-sum game until the discriminator model is fooled roughly half of the time, showing that the generator model is generating convincing examples.

GANs are rapidly evolving and are currently being used for many applications because of their ability to understand and recreate visual content. They can be used for generating a realistic image from text, creating deep fake videos, filling in images from an outline, etc.

5. Reinforcement Learning

Richard S. Sutton is referred to as the “Father of Reinforcement Learning.”

Reinforcement learning is an ML method of rewarding desired behaviors and punishing negative behaviors. It employs the use of a reinforcement learning agent — an algorithm based on neural networks. But the agent can learn through trial and error as well as perceive and interpret its environment.

In reinforcement learning, positive values are assigned to desired actions and negative values are assigned to undesired behaviors. It programs the agent to seek maximum reward and accomplish all the necessary goals. This can be operated in any machine learning system as long as rewards are applied.

A rough framework of reinforcement learning:

Although reinforcement learning has garnered much interest, its real-world adoption and application are scarce. However, some current use cases of reinforcement learning include game simulation, resource management, personalized recommendations, robotics, simulation-based optimization, and others.

AWS DeepRacer

Reinforcement learning can be tricky to deploy because it relies on the exploration of the environment for learning. This can pose a problem in real-world environments because of how complex and dynamic things can be. Some reinforcement learning algorithms include Q-learning, Deep Q-Networks, and State-action-reward-state-action (SARSA).

Conclusion

Machine learning applications are growing and so are the machine learning methods and tools. The top 5 machine learning trends discussed above should let you understand where the field could be headed. There are endless possibilities in data science and machine learning. And that makes it more fun and exciting for us!