Machine learning (ML) industrialization has evolved from a tech trend to a critical component of enterprise operations. While still maturing, it is now at a stage where industrial companies are increasingly adopting it with greater ease and reduced risk. This shift drives efficiencies, enables new capabilities, and transforms business processes.
Industrializing machine learning (ML), or MLOps, involves scaling and maintaining ML applications within enterprises. In 2024, MLOps tools are rapidly advancing, improving functionality and interoperability. These advancements are helping businesses move from pilot projects to fully integrated processes, scaling analytical solutions, and boosting team productivity. Successfully industrializing ML can significantly shorten production timelines and optimize resource use.
Stages of Adoption: Machine learning has progressed from experimentation and initial pilots to large-scale adoption. Companies increasingly invest in ML to enhance predictive analytics, automate processes, and improve decision-making.
Applications Across Industries: In manufacturing, ML is being deployed for smart manufacturing, predictive maintenance, supply chain optimization, energy management, and quality control. These applications are reshaping industries and delivering tangible benefits.
Challenges and Considerations: Despite its potential, industrializing machine learning presents challenges. Companies need substantial data infrastructure, skilled talent, and robust algorithms. Ethical concerns must also be addressed, including AI bias and data privacy.
Enabling Technologies: Key technologies like cloud and edge computing, advanced connectivity, and applied AI are essential for ML adoption. They provide the infrastructure, computational power, and integration capabilities for widespread deployment.
Strategic Implementation: For successful implementation, companies should focus on building a solid data foundation, fostering a culture of innovation, and ensuring continuous learning and development for their teams. Partnering with technology providers and staying updated on the latest advancements are also critical for maximizing the benefits of ML.
Why This Matters: Industrializing machine learning transforms business operations, enhancing efficiency, throughput, accuracy, and innovation. Companies that embrace ML gain a competitive edge, driving growth and unlocking new opportunities.
The state of this technology is ready for mainstream adoption. Early adopters and pilot programs have proven their value and are now entering widespread use in manufacturing. Two key enablers—access to sufficient data via IIoT and the availability of computing clusters and cloud infrastructure—are now readily accessible in most industrial environments.
Critical Areas of MLOps Impact in Manufacturing:
Showcasing Excellence: How Momenta's Portfolio Companies are Transforming Industries with Machine Learning
At Momenta, we’re proud to back companies revolutionizing industries through machine learning. Here are two standout examples from our 59 portfolio companies, demonstrating real-world impact.
Edge Impulse offers a robust platform for developers to create, manage, and deploy embedded machine-learning algorithms. It allows developers to handle datasets and design, train, test, and deploy models for embedded devices. These models efficiently process vast amounts of data, enabling automated responses such as adjusting thermostats or sending critical alerts to the cloud. For example, it can notify a factory manager about potential machinery failure. This technology optimizes data-heavy sensors, converting complex data into simple messages, thus saving energy, money, and time. Edge Impulse exemplifies how industrializing machine learning can drive significant operational improvements.
To learn more about EdgeImpulse and its cutting-edge work, tune in to the podcast featuring Edge Impulse's CEO, Zach Shelby. It’s a great opportunity to hear directly from an industry leader about the latest advancements and insights.
Composabl represents a paradigm shift in industrial automation. Its innovative solution empowers engineers to build intelligent autonomous agents that emulate human decision-making and orchestrate industrial systems. By leveraging AI, engineers can capture and replicate expert skills, automate complex tasks, and make high-value decisions. Composabl’s platform utilizes no-code tools and the Machine Teaching methodology, allowing engineers to create AI systems tailored to specific industrial challenges. This drives operational excellence and fosters innovation, making Composabl a pioneer in “Agent Engineering.”
By investing in and supporting these companies, Momenta highlights how embracing advanced machine learning and AI technologies can significantly improve efficiency, accuracy, and innovation within various industries, which is the essence of Industrial Impact.
Check out our podcast with CEO Kence Anderson to learn about Composabl's platform, which utilizes no-code tools and the Machine Teaching methodology.
For more insights on these trends and their impact on enterprise technology adoption, refer to the McKinsey Technology Trends Outlook 2024.
Momenta is the leading Industrial Impact® venture capital firm, accelerating industrial innovators across energy, manufacturing, smart spaces, and the supply chain. Our team of deep industry operators has helped scale industry leaders and innovators to improve critical industries, the environment, and people's quality of life for over a decade. PitchBook has ranked Momenta as one of the ten best-performing venture capital firms for 2023 in its prestigious Global Manager Performance Score League Tables, and the firm is the only European-headquartered VC to secure a Top 10 spot on the list. For more information, please visit http://www.momenta.one.