So your company just started looking into their first data science project to modernize the production site or to improve Shop Floor Management. You might even be on the way to building your first machine learning model, and are wondering what the best practices are.
Well, before any machine learning model can be deployed into production, there are some things you need to know:
Identifying the right ML model according to your business goals
In order to achieve your business goal by leveraging machine learning, (i.e., higher efficiency, lower material waste etc.), it is advised to first get an understanding of what your data can achieve. This is made possible by trying out machine learning models on real world data, or, if you don’t already have an existing data set, simulating your models on test data can help.
Additionally, you have to keep in mind that the models will need to be continuously adjusted. In the modeling process, machine learning models will undergo improvements and updates. They need to be hosted on a platform that allows for flexible, real-time interactions with the model. This is best achieved by an MLOps (Machine Learning Operations) platform that takes over the lifecycle management of the model, accommodates replacements and integrations.
The MLOps challenge
A practice derived from the term DevOps, already used in software engineering, MLOps was born out of a fusion of the words Machine Learning and Operations. It encompasses a technology stack that is language-, framework-, platform- and infrastructure-agnostic for handling all operations surrounding the AI landscape. A good MLOps workflow should include tools & features that empower the user to automate repetitive and time-consuming steps in the process, and support the user in the following challenges:
Continuous integration and continuous delivery
Deployment & automation
Scalability & Availability
Collaboration
Model Monitoring
In the realm of applied data science, a common challenge that organizations face is successfully transitioning from a prototype stage to production, resulting in the struggle to realize a real return on investment, even if their models were meticulously designed. As articulated by Run.ai, a frequently cited reason for this high failure rate is the difficulty of establishing a connection between data scientists who develop and train inference models, the IT team responsible for maintaining the underlying infrastructure, and the machine learning engineering teams accountable for deploying production-ready machine learning applications. The divide between these teams' skill sets, perspectives, and objectives can pose a significant barrier to the seamless integration of machine learning models into a production environment, where they can provide tangible business value.
The Solution
These challenges can be solved by having a well-defined, robust MLOps workflow. craftworks’ own MLOps platform — navio, acts as a compass to help businesses navigate the puzzling waters of MLOps tooling. navio guides you to your destination by solving many of the above challenges out-of-the-box. Built-in user management, model management, monitoring & security allows you to productionize any of your models faster than ever before. Integrate navio into your existing infrastructure with ease, allowing you to focus on building the optimal model and navio will take care of the rest.
The Integration
For reliably obtaining consistent machine data, navio can be integrated with edge devices that feed the platform with the necessary information for improving real-time predictions.
To enhance edge computing functionalities, craftworks has partnered up with TTTech Industrial and uses its modular industrial edge computing platform Nerve. The combination of navio and Nerve is a ready-to-use edge computing solution with navio’s added-value of seamlessly deploying and integrating AI models into new or existing infrastructure.
Bringing MLOps to the manufacturing shop floor
On the manufacturing shop floor, Nerve is providing the required edge computing infrastructure for a navio model to ingest machine data. The data is processed further for making real-time predictions on the edge, by deploying models, hosted by navio, directly where the data is produced. navio allows you to monitor your edge devices, models and clients all in one place. It decreases latency, increases security and optimizes the workflow.
As Jakob Lahmer, founder and CTO at craftworks, puts it:
“Our customers often do not have the technology base to run our models. With Nerve, we can offer them a platform for ingesting data and running our models that also seamlessly interacts with our navio solution.”
Therefore, manufacturers can make better data-driven decisions for a multitude of use cases: cutting costs, reducing material scrap, saving on energy consumption, improving production planning and enhancing their competitive edge through a high-quality product portfolio. By combining navio with TTTech Industrial’s Nerve, we enable customers to finally demystify the complexities of AI and join the 4th industrial revolution. We empower customers to gain the upper hand in any industry with this all-in-one hardware, and software solution.