By 2030, PricewaterhouseCoopers estimates artificial intelligence will contribute $15.7 trillion to the global economy. According to this research, 45% of total economic gains by 2030 will come from product enhancements that stimulate consumer demand, as AI will drive greater product variety with increased personalization, attractiveness and affordability.
Since using artificial intelligence (AI) and machine learning (ML) will provide businesses with innovative solutions to a wide variety of problems, what is stopping companies from harnessing the power of AI? Organizations’ main challenge revolves around the resources needed to incorporate AI/ML into their existing workflows and processes. Cloud computing AI/ML services can help to overcome this obstacle.
AI – technology for special assignments
In machine learning, data patterns, correlations, and trends are identified by computer models through experience, as machine learning models can provide additional insights into data. Meanwhile, artificial intelligence refers to the use of machine learning to automate tasks that typically require human-like intelligence. People can perform such tasks, but the right AI can do them faster and more efficiently.
A machine-learning model is constructed by applying a large dataset to an algorithm. The created model learns from various patterns derived from available data – the more data delivered to the model, the better the result. Maximizing the power of AI/ML models requires extensive computing power furnished by cloud services providers. Using cloud-based AI/ML services enables organizations to access powerful machine learning algorithms and tools without needing specialized hardware or in-house expertise, making it more accessible and cost-effective for companies of all sizes to adopt ML-driven solutions.
Cloud computing services driven by AI/ML provide a scalable and flexible platform for machine learning. With cloud-based services, enterprises can scale up machine-learning efforts without investing in additional hardware or infrastructure. Additionally, given the availability of off-the-shelf AI/ML tools, the cost of producing an effective solution is lower compared to the amount of time it takes a human to complete a job. It’s a significant benefit for organizations that must process large amounts of data or handle high traffic volumes. The benefits do not end there, as cloud-based AI/ML services easily integrate with other cloud-based tools and create seamless and efficient workflows.
Real-case scenarios of cloud-supported AI/ML
Machine learning models that leverage cloud services can help many industries, even business endeavors that may seem unrelated to AI/ML. One example is the scrap metal industry, where you can use AI to identify scrap quantities from satellite images – a solution that provides significantly better results than legacy systems. Use-case scenarios do not end there.
As more and more industries lack sufficient specialists, machine learning models can replace specific processes previously carried out by humans. One real case example of such technology is performing an automated verification of the correctness of a telecommunications installation using image analysis. No technician is required on-site, as the customer can upload all the photos needed for the system to run an analysis.
Read also: What is machine learning model management?
The more advanced the project gets, the more machine learning engineering it needs. Cloud services support working with models for anomaly detection, natural language processing (NLP), cognitive services, computer vision and AutoML mechanisms. There’s no denying that building the necessary pipelines to prepare and process data is more efficient with the cloud.
A use case of cloud-supported machine learning engineering worth mentioning is an advanced mechanism built for recognizing and categorizing objects in images that a company can apply in various industries:
- In the retail industry, to analyze the number of products of a specific brand on the shelves and the amount of traffic in stores.
- In the industrial and mining industries, to verify the volume of traffic in the factories, to help detect anomalies in the operation of machinery, maintain health and safety rules and verify work attire.
- In the ecommerce industry, to support the creation of bots that communicate with users or to vastly expand website traffic analysis.
MLOps – machine learning model management
To manage a machine learning model correctly, it is worth referring to the application lifecycle management, because both scenarios work on similar principles. The key is to design and implement mechanisms to gather data, train ML models accurately, and then deploy them to dev, test, stage and production environments. Optimized models must be monitored based on performance, adhere to the highest security standards, and run and trained at scale in a distributed model.
Considering the scope of such an endeavor, organizations with in-house data science services departments that develop machine learning projects will eventually need support using the cloud at some stage of work. Machine Learning Operations (MLOps) practices can improve the quality and consistency of machine learning solutions, by combining the power of ML, data engineering, and software engineering to improve the development and deployment of ML models and streamline the continuous delivery of high-functioning models into production.
Microsoft describes MLOps as a set of principles and practices, similar to DevOps, that increase the efficiency of workflows, with the main goals of faster experimentation and development of models, as well as quality assurance and end-to-end lineage tracking. The MLOps service involves support in projects and preparation with the potential implementation of appropriate standards tailored to a project’s needs.
Read more: Machine learning in marketing
AI/ML services will lead the way for businesses in 2023
Sudi Bhattacharya and Ashwin Patil, managing directors at Deloitte Consulting LLP, expressed it well in their blog post, “It’s easy to see how the cloud helps fuel AI/ML to drive insights and innovation. However, it takes planning and insight to get there. Cloud-fueled AI/ML takes vision, a solid foundation, and education coupled with a governance discipline”. Experts help to train, set up and run ML systems on the cloud, but the innovative solution will not completely replace human ingenuity. The practical and technical limitations of AI/ML do not allow it to understand every single situation correctly and respond in the best conceivable manner. The key to success is, and will continue to be, the right collaboration between humans and the potential offered by AI/ML services, allowing you to leverage the sheer power of cloud services cost-efficiently to gain a competitive .edge.
If you want to know how cloud computing AI/ML platforms can elevate your products and services, contact us to take advantage of artificial intelligence’s great potential.
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About the authorDamian Mazurek
Chief Innovation Officer
A certified cloud architect and AI expert with over 15 years’ experience in the software industry, Damian has spent the last several years as a cloud and AI consultant. In his current role he oversees the technology strategy and operations, while working with clients to design and implement scalable and effective cloud solutions and AI tools. Damian’s cloud, data and machine learning expertise has enabled him to help numerous organizations leverage these technologies to improve operations and drive business growth.
