The MLOps Workflow: How Barbara fits in

According to a recent IBM study, nearly 77% of industrial companies are either actively working with or planning to adopt AI and Machine Learning to optimize operations or unlock new revenue streams. In this landscape, Machine Learning Operations (MLOps) is emerging as the essential framework for Data and Infrastructure teams, streamlining workflows and driving successful AI implementation.

‍The MLOps Workflow: An Overview

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.

Source: AI Infrastructure Alliance.

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).

‍Barbara’s Role in Machine Learning Workflow

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The image above highlights the key stages in the workflow where Barbara's Edge AI Platform plays a critical role. Let’s take a closer look at these stages:

1. DATA INGESTION

Barbara simplifies data ingestion in industrial environments by offering a suite of industrial connectors, ingesters, and databases. These tools, available through the Barbara Marketplace, make it easier for users to integrate and manage data efficiently, setting a strong foundation for edge AI workflows.

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 and increases the efficiency of real-time predictions while avoiding the constant dependency of a network connection.

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The Barbara platform is a powerful Edge AI and Edge Apps Orchestrator, designed to simplify the deployment and execution of models on edge nodes. It provides data teams with a more efficient and streamlined alternative to traditional cloud-based solutions.

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3. MONITORING

Barbara platform monitors the lifecycle of any workload being deployed and executed at 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

While Barbara’s role in logging is partial, it is highly impactful. By capturing records of model performance and predictions, the platform helps users analyze model behavior over time. These insights are invaluable for fine-tuning and improving AI applications.

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Learn more about : Optimizing AI Deployment on Edge Nodes with Barbara

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Why Every Stage Matters

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

1. 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.

2. 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.

3. 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.

4. 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.

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Conclussion‍

For industrial companies leveraging AI, understanding the MLOps workflow is critical to identifying the requirements and tools needed to successfully transition use cases into production. Barbara, as a comprehensive Edge AI platform, is designed to seamlessly integrate into the workflow stages that directly involve edge computing. With its robust capabilities and intuitive user experience, Barbara's Edge Platform empowers Data and Infrastructure teams to efficiently deploy, run, and monitor AI models at the edge, ensuring optimal performance, security, and efficiency at every step.

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‍Ready to take your AI models to the edge? Start exploring Barbara today! Book a free trial today.

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