Due to the rapid advancement in technology, the Manufacturing Industry has accepted a wide range of digital solutions that can directly benefit the organization in various ways. One of which is the application of Machine Learning and AI for predictive analytics. The same industry which earlier used to rely on MVA and other statistical techniques for inferencing the parametric relationship has now headed towards the application of predictive models. Using ML/AI now they have enabled themselves to not only understand the importance of parameters but also to make predictions in real-time and forecast the future values. This helps the industry to manage and continuously improve the process by mitigating operational challenges such as reducing downtime, increasing productivity, improving yields and much more. But, in order to achieve such continuous support for the operations in real-time, the underlying models and techniques also need to be continuously monitored and managed. This brings in the requirement of MLOps, a borrowed terminology from DEVOps that can be used to manage your model in a receptive fashion using its CI/CD capabilities. Essentially MLOps enables you to not only develop your model but also gives you the flexibility to deploy and manage them in the production environment.
Let’s try to add more relevance to it and understand how Seeq can help you to achieve that.
Note: Seeq is a self-service analytics tool that does more than modeling. This article is assuming that the reader is familiar with the basics of the Seeq platform.
Need for Seeq?
Whenever it comes to process data analytics/modeling, visuals become very much important. After all, you believe in what you see, right?
To deliver quick actionable insights, the data needs to get visualized in the processed form which can directly benefit the operations team. The processed form could be the cleaned data, derived data, or even the predicted data, but for making it actionable it needs to be visualized. The solutions should peacefully support and integrate with the culture of Industry. If we expect the operator to make a better decision then we also expect the solution to be easily accepted by them.
MLOps in Seeq
For process data analytics models could accept various forms such as first principle, statistical or ML/AI models. For the first two categories, the management and deployment become simple as it is essentially the correlations in the form of equations. Also, it comes with complete transparency, unlike ML/AI models. ML/AI on the other hand is a black-box model, adds a degree of ambiguity and spontaneity to the outcomes, which requires time management and tuning of the model parameters. This could be either due to the data drift or the addition/removal of parameters from the model inputs. To enable this workflow Seeq provides the following solution:
- Model Development:
One can make use of Seeq’s DataLab (SDL) module to build and develop the models. SDL is a jupyter-notebook like interface for scripting in python. Using SDL you get the facility to access the live data to select the best model and finally create a WebApp using its AppMode feature for a low-code environment. As a part of best practice, one can use spy.push method to extract the maximum information out of the model using Seeq Workbench and advanced Visualization capabilities.
- Model Management (CI/CD):
Once the model is deployment-ready, the python script for the developed model can be placed in a defined location in the server for accessing the production environment. After successful authentication and validation, the model can be seen to have visibility in its list of connectors. This model can then be linked with the live input streams for predicting the values in real-time.
- Visibility of the Model:
Once the model is deployed in the production environment, one can continuously monitor the predictions and get notified of any deviations which may be an outcome of data drift. The advanced visualization capabilities of Seeq enables the end-user to extract maximum value/information out of the data with the ease and flexibility of its use.
For a better deployment and utility of MLOps, we recommend you apply visual analytics for your data and model workflow. Visual analytics at each stage of the ML lifecycle provides a capability to derive better actionable insights which could be easily scaled and adopted across the organization for orchestrating the siloed information and to unify them for an enhanced outcome.
Innovate your Analytics
I really hope that this article helped you to benchmark your strategy for deriving the right analytical strategy in your Industrial Digitization journey. For this article, our focus was on how Seeq can support the easy implementation of MLOps using Industrial manufacturing data.
Who We Are?
We, Process Analytics Group (PAG), a part of Tridiagonal Solutions have the capability to understand your process and create a python based template that can integrate with multiple Analytical platforms. These templates can be used as a ready-made and a low code solution with the intelligence of the process-integrity model (Thermodynamic/first principle model) that can be extended to any analytical solution with available python integration, or we can provide you an offline solution with our in-house developed tool (SoftAnalytics) for soft-sensor modeling and root cause analysis using advanced ML/AI techniques. We provide the following solutions:
- We run a POV/POC program – For justifying the right analytical approach and evaluating the use cases that can directly benefit your ROI.
- A training session for upskilling the process engineer – How to apply analytics at its best without getting into the maths behind it (How to apply the right analytics to solve the process/operational challenges)
- Python-based solution- Low code, templates for RCA, Soft-sensors, fingerprinting the KPIs, and many others.
- We provide a team that can be a part of your COE that can continuously help you to improve your process efficiency and monitor your operations on regular basis.
- A core data-science team (Chemical Engg.) that can handle the complex unit processes/operations by providing you the best analytical solution for your processes.
Sr. Data Scientist