The MLOps Workflow: How Barbara fits in

Most industrial companies (up to 77% according to a last-year study by IBM) are working or planning to work with AI and Machine Learning as a means to optimize their operations or enable new revenue streams. And Machine Learning Operations (MLOps) is becoming the paradigm as a work framework for the Data and Infrastructure teams involved. 

Understanding the MLOps workflow is crucial for those companies; but also for Barbara, who is at the forefront of Edge AI for industrial applications. So, let’s break down this workflow and see how Barbara integrates into each phase.

The MLOps Workflow: An Overview

Don’t panic with the image above. It’s less complex that it may look at first (in fact, this is a quite good visual representation drawed by the AI Infrastructure Alliance). Imagine the workflow as a sophisticated assembly line for data. It starts with raw materials (data) and ends with a finished product (a deployed AI model). This workflow is split into several stages:

1. Data Stage

In this initial stage, data is gathered (Ingestion), cleaned to remove inconsistencies (Clean), checked to ensure it meets certain criteria (Validate), and transformed into a usable format (Transform). In certain cases, data is labeled for supervised learning (Label), or enhanced with synthetic data to improve model training (Synthetic Data Generation/Augmentation).

2. Training Stage

Here’s where the model starts learning. It begins with experiments (Experiment) to find the best approach. The model is then trained (Train) with our prepared data, followed by tuning (Tune) to refine its performance.

3. Deployment Stage

Once the model is trained, it’s deployed (Deploy) into a production environment where it makes predictions or inferences (Prediction/Inference). The outcomes of these predictions are then logged (Log) for further analysis.

4. Monitoring and Maintenance

The final, ongoing stage is monitoring both the training and deployment stages to ensure everything runs smoothly.

This workflow relies on a strong foundation, which includes the data infrastructure (AI/ML Data Foundation) and the tools to keep track of all the changes (Versioning/Data Lake with Lineage Tracking).

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Barbara’s Role in the ML Workflow

This picture above visualizes where in the workflow Barbara's Edge AI Platform plays an important role (we are using again the draw made by the AI Infrastructure Alliance, as a reference). Let’s review these stages:

1. Data Ingestion

Barbara platform includes a bunch of Industrial connectors, data ingesters, databases…and other tools that basically are aimed to help users in the data ingestion process in industrial environments. All these off-the-shelf apps and tools are available through Barbara Marketplace for every user of the platform.

2. Deployment and Serving

Barbara shines here by helping companies to deploy their AI models seamlessly at the edge. This means that rather than running on remote servers, the models operate directly on the local hardware, closer to where data is collected. This reduces latency and the volume of data traveling and processed in the cloud (not a very inexpensive operation…) and increases the efficiency of real-time predictions while avoiding the constant dependency of a network connection.

Barbara platform is primarily an Edge AI and Edge Apps Orchestrator. It makes it easier for Data teams to deploy and run their models on edge nodes compared to the cloud.

3. Monitoring

Barbara platform monitors the lifecycle of any workload being deployed and executed in the edge, including AI models so users can keep an eye on deployed models. This is vital for maintaining the accuracy and reliability of AI applications in industrial settings.

And not only the models but also the edge nodes serving them. Barbara includes several mechanisms and features to monitor and control the nodes containing the models

4. Logging

Although Barbara is partially involved in the logging process, its role is crucial. Logging is all about keeping a record of the model's performance and the predictions it makes. This data is invaluable for understanding how the model behaves over time and can provide insights into how it can be improved.

Why Every Phase Matters

Each stage of the MLOps workflow plays a critical role in the development of an effective AI model.

Ingestion and Data Preparation: Garbage in, garbage out, as the saying goes. Without clean, well-organized data, even the best algorithms won’t be much use.

Training: This is where the magic happens, turning data into a model that can learn and adapt. It's a bit like teaching a child to recognize shapes and colors – it takes time and patience.

Deployment: Deploying a model is like sending a child off to school – it's where they prove what they've learned. For industry, this is where the real value is realized as models begin to impact business processes.

Monitoring and Logging: Even after deployment, the work isn’t over. Monitoring is like a report card, showing how well the model performs in the real world, and logging is the diary, providing a record of what has happened.

In essence, every phase of the ML workflow is about building towards a reliable, efficient AI system. 

Wrapping Up

For industrial companies working with AI, the MLOps workflow is key to understand all their needs and what tools they must have to set their use cases up in production. 

Barbara, as an Edge AI platform, fully integrates in this workflow in the stages that directly involve the edge. With a powerful UX, Barbara platform focuses on empowering Data and Infrastructure teams in their tasks of deploying running and monitoring their models at the edge – all without compromising performance, security, or efficiency.

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