4 min readAccelerate the Productization of IoT Specific AI/ML with Mnubo’s SmartObjects AIoT Studio [...]
Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools for building complex IoT systems. But launching a new IoT project and managing the full life-cycle of AI/ML presents a number of challenges, and very often they are not of the nature and scale initially planned. Mnubo’s AIoT Studio aims to overcome this challenge, empowering Data Scientists with the appropriate tools to work in a product environment.
While the development of AI/ML is inherently challenging – training sets, availability of data scientists, domain expertise, etc – from a product point of view, the AI/ML code makes up a very small portion of the overall solution. Only a small fraction of real-world AI/ML systems are composed of the AI/ML code. The surrounding expertise and infrastructure is vast and complex, as depicted in the diagram below. The paper “hidden technical debt in machine learning systems“, where the below diagram is first referenced, provides an interesting angle to this topic as well.
Figure 1.0 Only a small fraction of real-world AI/ML systems are composed of the AI/ML code
In fact, when it comes to commercial solutions, deploying and running AI/ML code against live streaming data as well as maintaining it over time, requires a specific level of expertise and goes well beyond the original challenges of AI/ML. Add the need for live collaboration between experts, corporate-wide centralization and circulation of this intellectual property (IP), and you have yourself a very expensive puzzle to solve.
It is with this complexity in mind that Mnubo developed and released its SmartObjects AIoT Studio. This fully-managed IoT-focused AI/ML studio allows customers to develop, train/maintain, deploy, and run AI/ML models as well as ad-hoc algorithms against live streaming data. Using microservices and a function-as-a-service architecture, Mnubo’s AIoT Studio unscrambles and abstracts the complexity of building and maintaining the infrastructure required to power production ready AI/ML at scale.
No ETL data extraction
Access any AI/ML framework or libraries
Centralize your IP and share worldwide
One click deployment process
Scale as you grow architecture
Figure 2.0: AIoT Studio – Example of Temperature Prediction using TensorFlow
Business Use Cases
Many of Mnubo’s IoT customers are already leveraging the power of SmartObjects AIoT Studio to:
Schedule Proactive Maintenance
Use artificial intelligence models to predict machine downtime and schedule proactive maintenance. Identify impending faults early to proactively counteract product malfunctions and reduce costly emergency repairs
Figure 3.0 Identify impending faults early to proactively counteract product malfunctions
Enable Preventative Replenishment
Use advanced data science and machine learning models to enable targeted preventative actions. Predict time to action for equipment requiring replenishment/replacement. Help field technicians with inventory and resolution planning.
Focus R&D Spend
Identify anomalies before products are being deployed and updated. Determine which equipment has the most faults and alerts, what the trend is over time, how does this vary across product versions.
Figure 4.0 Identify anomalies before products are being deployed and updated.
Improve Service Efficiency
Visualize critical sensor readings and asset states in real-time to reduce downtime, service calls and truck rolls. Create new service offerings, empower dealer networks with condition-based monitoring and automated reports to improve service efficiency
Increase Engagement & Reduce Churn
Understand how customers are interacting with your connected product. Group customers based on their usage behaviour, and profile them to facilitate a faster adoption rate. Track the entire connected product life-cycle to reinforce brand engagement.
Figure 5.0 Understand how customers are interacting with your connected product