{"id":6424,"date":"2023-06-19T18:52:09","date_gmt":"2023-06-20T02:52:09","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=6424"},"modified":"2025-04-24T17:15:22","modified_gmt":"2025-04-24T17:15:22","slug":"top-5-machine-learning-trends-for-2022","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/top-5-machine-learning-trends-for-2022\/","title":{"rendered":"Top 5 Machine Learning Trends For 2022"},"content":{"rendered":"\n\n\n\n\n
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The machine learning field is relatively new but it\u2019s 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.<\/p>\n

Whether you\u2019re 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.<\/p>\n

1. Automated Machine Learning (AutoML)<\/strong><\/h1>\n

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.<\/p><\/blockquote>\n

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.<\/mark><\/p>\n

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.<\/p>\n

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.<\/mark><\/p>\n

Google AutoML<\/strong><\/a> code sample:<\/strong><\/p>\n

from google.cloud import automl<\/span># TODO(developer): Uncomment and set the following variables\n# project_id = \u201cYOUR_PROJECT_ID\u201d\n# dataset_id = \u201cYOUR_DATASET_ID\u201d\n# display_name = \u201cYOUR_MODEL_NAME\u201d<\/span>client = automl.AutoMlClient()<\/span># A resource that represents Google Cloud Platform location.\nproject_location = f\u201dprojects\/{project_id}\/locations\/us-central1\"\n# Leave model unset to use the default base model provided by Google\nmetadata = automl.TextClassificationModelMetadata()\nmodel = automl.Model(\n display_name=display_name,\n dataset_id=dataset_id,\n text_classification_model_metadata=metadata,\n)<\/span># Create a model with the model metadata in the region.\nresponse = client.create_model(parent=project_location, model=model)<\/span>print(\u201cTraining operation name: {}\u201d.format(response.operation.name))\nprint(\u201cTraining started\u2026\u201d)<\/span><\/pre>\n
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.<\/p>\n
2. Machine Learning Operationalization Management (MLOps)<\/strong><\/h1>\n
Inspired by traditional DevOps, MLOps seeks to remove silos of traditional software development.<\/p><\/blockquote>\n
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.<\/p>\n
MLOps ensures that continuous integration and deployment (CI\/CD) practices are implemented throughout the machine learning lifecycle. It\u2019s useful for every phase including data gathering and analysis, model training and development, model monitoring and retraining, and more.<\/p>\n
MLOps Architecture Design:<\/strong><\/p>\n
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Image from: https:\/\/content.cloud.redhat.com\/<\/a><\/figcaption>\n<\/figure>\n
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.<\/p>\n
MLOps principles for reproducibility:<\/strong><\/p>\n
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Image from: ml-ops.org<\/a><\/figcaption>\n<\/figure>\n
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<\/a>, Amazon SageMaker<\/a>, Azure Machine Learning<\/a>, and Google Cloud AI Platform<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Innovation and academia go hand-in-hand. Listen to our own CEO Gideon Mendels chat with the Stanford MLSys Seminar Series team<\/a> about the future of MLOps and give the Comet platform a try for free!<\/a><\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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3. TinyML<\/strong><\/h1>\n
TinyML was popularized by Pete Warden, the \u201cfounding father\u201d of TinyML, and Daniel Situnayake, an early tinyML engineer.<\/p><\/blockquote>\n
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.<\/p>\n
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.<\/p>\n
ML frameworks for IoT edge devices:<\/strong><\/p>\n
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Image from: therobotreport.com<\/a><\/figcaption>\n<\/figure>\n
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.<\/p>\n
Some edge devices and their hardware specs:<\/strong><\/p>\n
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Image from: therobotreport.com<\/a><\/figcaption>\n<\/figure>\n
Wake words such as \u201cOk Google,\u201d \u201cAlexa,\u201d and \u201cHey Siri\u201d 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<\/a> and TensorFlow Lite Micro<\/a> are some of the most popular hardware and frameworks being used for deploying ML models on edge.<\/p>\n
4. General Adversarial Networks<\/strong><\/h1>\n
GANs were introduced by Ian Goodfellow and his colleagues in 2014 at the University of Montreal.<\/p><\/blockquote>\n
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.<\/p>\n
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.<\/p>\n
The GAN structure:<\/strong><\/p>\n
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Image from: developers.google.com<\/a><\/figcaption>\n<\/figure>\n
Generative Adversarial Networks help to train a generative model by presenting the problem as a supervised learning problem with two sub-models. Here\u2019s how they work:<\/p>\n
There are several AutoML software platforms for you to work with. But we\u2019ll mention the ones adopted by most organizations and ML Engineers today. They include: Google AutoML<\/a>, Azure Automated Machine Learning<\/a>, Auto Keras<\/a>, and Auto-sklearn<\/a>.<\/p>\n