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6 Machine Learning Trends By AWS That Will Drive Adoption & Innovation

Machine Learning Trends in 2023 are more than the buzzword, as ML technology can revolutionize how business operations are performed. Machine Learning (ML) and Artificial Intelligence (AI) are emerging technologies that can transform our lives and business beyond imagination.

In 2023, creative AI, distributed enterprise management, autonomous systems, and cyber security will be a few technical segments that will witness the increasing use of ML. Businesses that leverage the power of machine learning technologies will have the ability to stay ahead in the competitive market. 

Given the rapid transformation that Machine Learning has undergone, McKinsey’s recent report reveals that industrializing ML and applied AI are the two top trends of the year. Read this article to understand the new trends that will shape the future. Adopting the trends will help enterprises scale, expand, and innovate to achieve goals in 2023.

Top Machine Learning Technology Trends

The Machine Learning industry is evolving rapidly, and businesses are improving their in-house operations using advanced tech. AWS, the leading provider of cloud, outlines the six key Machine Learning trends that can help drive ML innovation and adoption in the upcoming years.

Growth Of Model Sophistication

There has been an exponential boost in ML solutions and model sophistication in recent years. The state-of-the-art ML models have grown from 300 million to 500 billion from 2019 to 2022. The 1600 times increase in ML sophistication models in the past three years proves that Machine Learning has a bright future.

Commonly known as foundation models, these massive ML models can be trained with large datasets. They can then be reused and tuned for different tasks. Hence, the easier-to-adopt approach reduces ML deployment’s cost and effort. It will also help enterprises leverage the benefit of increased sophistication to maximize business productivity and improve the efficiency.

Data Growth

The second key trend identified by Amazon Web Services about Machine Learning is the rising volumes and different types of data. With the increasing power, innovation, and technology adoption, enterprises can train and build models for structured data sources like text and unstructured data sources like audio and video.

When enterprises can effortlessly get different data types into ML models, it leads to an increase in the deployment of multiple services at AWS that assist in model training. For example, AWS’s SageMaker Data Wrangler is a practical ML training solution that helps users process unstructured data using a defined approach.

Machine Learning Industrialization

Another emerging technology that enterprises need to catch up with includes the industrialization of Machine Learning. The growth of ML industrialization enables organizations to build applications quickly. In addition, enterprises can automate deployment and make it reliable with the help of ML industrialization.

The critical approach followed by industries results in building and deploying more ML models in less time, all thanks to the new tech, libraries, and frameworks. One of the best examples of ML industrialization is AWS SageMaker, which can train Alexa speech models. In 2023, we can see more adoption of ML solutions throughout the industries that help them rise in the competitive market.

ML-Powered Purpose-Built Apps

Machine Learning (ML) is growing in popularity due to the development of purpose-built applications that serve specific use cases. Using the cloud services like AWS, enterprises can automate common ML use cases. Services such as translation, voice transcription, text-to-speech, anomaly detection, etc., help teams working on machine learning projects to automate half of the mundane tasks.

Furthermore, Amazon Transcribe service, one of the latest Machine Learning and Artificial Intelligence trends, can support real-time call analytics. In addition, the user speech recognition feature of the service helps enterprises understand customer sentiment and improve business operations. With these easy-to-develop and deploy purpose-built applications, enterprises can save time and resources to stay ahead of the market.

Responsible AI

Even though the two technologies, ML and AI, are increasing in popularity, enterprises need to use them responsibly. That said, another Machine Learning trend booming in the market is responsible AI. For this, an AI system must be fair, regardless of gender, religion, user attributes, or race.

In addition, there should be explainable Machine Learning systems that help teams understand how a model operates in a specific environment. Finally, enterprises need to focus on the need for a governance mechanism and ensure AI is practiced responsibly. In the upcoming years, we can see a rise in solutions promoting the responsible use of trending technologies.

ML Democratization

The next key Machine Learning trend in our list revealed by the cloud computing platform is ML democratization. According to this trend, technology will be democratized, making skills and tools accessible to more people. In addition to the use-case-driven tool, the no-code and low-code applications simplify the machine learning process. It will solve the problems and challenges of democratization using deep learning models.

This low-code and no-code machine learning solutions will help non-tech employees build applications faster. It helps you to reduce time-to-delivery and eradicate high development costs. According to recent data, around 60% of the world’s corporate data is reserved in the cloud. That said, 2023 will see increased investment in resilience and cloud security to meet the ever-evolving demands of the tech industry.

A skilled team of experts will help you differentiate your business and utilize the power of technology to reduce human error. Inferenz AI and ML engineers help organizations understand the ins and outs of the technology better and improve in-house business operations.

machine learning trends

Top Technological Segments For ML in 2023

Some technological segments that will have the most usage of Machine Learning models include:

  • Distributed Enterprise Workforce: As remote work is the new normal of 2022, enterprises are bound to look for new ways that help them manage the workforce. Machine Learning is the tech that will help distributed companies grow by assisting teams to come on the same page.
  • Automation: From banking to security, many industries are integrating autonomous software systems. The aim of automation is to ease complicated tasks for data scientists working on machine learning applications. New innovations will help enterprises with smart automation to quickly adapt to recent changes.
  • Cybersecurity: With the growth of digitization in different fields, the need to protect sensitive information is rising. AI and Machine Learning are smart technologies that help organizations protect their private data and secure business.

machine learning trends

Future Of Machine Learning Development 

Artificial Intelligence and Machine Learning technologies drive innovations across different industries. According to the Artificial Intelligence market report, the market is expected to reach $500 billion in 2023 and $1597.1 billion in 2030. This indicates that emerging machine learning and AI technologies will continue to be in high demand in the upcoming years.

Organizations that wish to leverage the power and benefit of technologies need to focus on innovation. Data teams should focus on adopting the latest machine learning algorithms. Partnering with a certified team of ML/AI engineers can help you adopt deep learning solutions and achieve goals. If you’re apprehensive about how to catch up with the Machine Learning trends, contact the expert team of Inferenz.