Over the next six years, the global ML market size is expected to grow from $21.17 billion in 2022 to $209.91 billion in 2029. Expecting this growth means that in 2023, organizations will see a paradigm shift in how they prioritize ML investments, Spiceworks reports.

Most companies say they are using six different tools to build models and learn, with executives focusing more on downstream ML capabilities such as observability and function management. The shift from building complex, company-wide ML models to smaller, task-focused models increases their portability and reduces barriers to market. And today we’ve chosen to introduce you to the top four ML trends that Spiceworks presents.

Generative AI
Generative AI can create new content, including audio, code, images, text, simulations, and videos. It uses deep neural networks with billions of parameters to enable complex pattern recognition.

Unsupervised or semi-supervised learning algorithms are making huge strides in accelerating research and development (R&D) cycles in the medical and financial forecasting fields.

The sector will be increasingly regulated. The EU’s AI law, the US Privacy and Data Protection Act and the Open Source Software Assurance Act are all breaking the mould to promote the safety and security of modern technological lifestyles. Whether your company has entered the world of ML or not, enterprises should be aware of these acts and plan strategies to strengthen fraud detection to mitigate risks against the latest ML tools.

Computer vision
Computer vision (CV) accounts for the largest share of the AI and ML market. It is a field of AI that can capture, process and analyze real-world images, enabling the extraction of meaningful, contextual information.

One of the sectors where CV is making an impact is the automotive industry. It can detect defects in the bodywork of cars and underpin the development of applications such as self-driving cars. High-resolution cameras with background CV systems identify surrounding objects, people and movements that automatically trigger the vehicle’s response.

Increasingly, it will help maintenance service providers perform efficient and thorough inspections by using cameras to identify dents and mechanical parts that are out of place. Using CV, engineers can process the images and identify discrepancies within seconds.

Cross-industry AI Synergy
ML will be less concerned with company-specific models and more with data-driven models that can be used across sectors.

Doctors and scientists are experimenting with ML and CV technologies, training them to recognize and classify rare genetic skin conditions. Specialists walk the aisles, in some cases hourly, to count merchandise and ensure product availability. But what if they put CV apps on the shelves to help them monitor real-time inventory?

As companies begin to share the investment cost of tools that can analyze visual patterns and detect everything from rare diseases to product movement, more experimentation and affordable models can be created.

Late adopters are increasingly looking at use cases from more mature ML industries, such as automotive and healthcare, and adapting them to support their business needs.

ML Data Scientist Upskilling vs. Low-code solutions

Codeless and low-code (LCNC) platforms allow users with or without programming language knowledge to manage and build ML tools through intuitive interfaces such as point-and-click and drop-down menus.

ML use cases are expanding rapidly as developers begin to reinvent workflows based on what the technology can provide. AI people can re-engineer systems by pre-programming them to proactively deliver alerts and red flags to users when certain triggers are reached.

Nonetheless, LCNC tools are fundamentally limited in the scope of customization by design. Highly skilled software engineers will be needed to build, monitor, and scale these platforms. The latter needs are likely to lead to a demand for distinct new positions, such as human-computer interaction managers.

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