MLOps

Why Machine Learning Operations (MLOps) for Scalable AI ?

Organizations are enthusiastic about embracing AI/ML solutions to be competitive in the market at the same time they need to be cautious that the models they are working with are up-to-date, and the various stakeholders involved in building ML Life Cycle have better team coordination and faster time-to-market goals, accomplished with better scalability. Machine Learning Operations (MLOps) can act as a panacea for all these requirements and this blog explores the reasons.

DevOps is a familiar term in the world of technology, isn’t it? Yes, DevOps fosters collaboration between Software Development and Operations teams by ensuring they do not work in silos. Ditto here, MLOps or Machine Learning Operations fosters collaboration between Data Scientists, Data Engineers, and operations professionals. It is an ML life cycle management process comprising data gathering, data model development, orchestration, deployment, diagnostics, governance, and managing business metrics.

MLOps for Artificial Intelligence

MLOps are more dynamic than traditional SDLC or DevOps. The Data Science infrastructure comprises episodic events depending on the number of times they train models, employ data sets, and model topology, as they conduct many parallel experiments. 

Moreover, SDLC’s main focus is on performance, reliability, security, and defect handling in software applications; MLOps must also handle ‘Model-Drifts’ and work on the frequency with which the model should be retrained.

Data teams can look into the MLOps pipeline as two segments:

1.     Training Pipeline

2.     Serving/Production Pipeline

The following schematic diagram illustrates this CI/CD/CT pipeline succinctly.

During the Training phase, the first FOUR steps are initiated: Data Collection, Data Preparation, Data Segregation, and Model Training. In the Serving phase Model Testing & Validation, and Model Deployment are accomplished.

According to a study conducted by Dimensional Research, over 80 percent of companies rely on stale data for decision-making. Today we have hundreds of AI/ML models helping businesses in decision-making but the challenge is keeping these models up to date so that there is no ‘drift’ in the decisions these models suggest.

The next challenge is that the current Large AI/ML models have several moving parts ensuring coordination between these components by managing datasets and pipelines is a tough ask.

Most importantly many organizations experiment with ML models in silos, which leads to the absence of shareable and repeatable processes for monitoring and managing models at scale.

These challenges can be effectively addressed with Machine Learning Operations. Its sophisticated capabilities enable organizations to implement ML programs at scale. MLOps combines best practices and relevant technologies for a centralized and governed mechanism to automate, manage, and scale ML deployments in production environments. 

ML Life Cycle comprises the following key steps Data Preparation, Model Training, Model Testing, Model Deployment, Monitoring, and Scalability. MLOps play a vital role in better management of all these steps.

Data Preparation involves Data Ingestion, Data Cleaning, Data Transformation, and Data Analysis sub-steps. MLOps enables the automation of all these processes thus ensuring better ML Life Cycle management.

Handling Model Training with multiple pipelines is a complex task. Automating the entire Model Training process with MLOps enables organizations to manage this ML Life Cycle with precision.

Data scientists must put aside a major chunk of their time in Model Testing as it is an iterative process. They are expected to track model configuration, pipeline, and results which is time intensive. Through MLOps implementation chaos in testing can be avoided and Data Scientists can focus on other productive tasks.

Manual deployments of ML models are prone to error. With machine learning operations organizations can deploy automated CI/CD pipelines and also ensure rollback mechanisms are in place in case of performance degradation.  

Experimental and Production models should be continuously monitored to ensure ‘NO PERFORMANCE DEGRADATION.’ Without an automated MLOps monitoring mechanism Monitoring both production and experimental models is a herculean task for ML teams.

The ML models should be easily scalable as the number of stakeholders and teams working with them keeps changing dynamically. MLOps offers better scalability.     

Here we have identified 5 key reasons why and how MLOps enable organizations to build scalable AI.

  1. Model Deployment and Management:

MLOps help your organization automate model deployment to production ensuring consistent and efficient delivery. Next, it provisions a better version control mechanism so that rollbacks are easier if needed. Also, it provides a better Monitoring mechanism to observe deployed models for performance degradation and drifts.  

  1. Data Management and Governance

MLOps ensures data quality and consistency throughout the ML lifecycle by improving model accuracy. Further, it facilitates data compliance and troubleshooting by tracking the origin and transformation of data. Finally, it ensures data privacy by protecting sensitive information.

  1. Experiment Tracking and Reproducibility

MLOps tools enable you with an Experiment Logging feature to capture details about experiments that include parameters, hyperparameters, and results. Also, it enables researchers to reproduce/replicate experiments ensuring consistency in findings.

  1. Collaboration and Teamwork

MLOps provides a centralized Shared Platform for ML teams to collaborate on resources. It provides a ‘Workflow Automation’ process streamlining workflows, reducing bottlenecks, and improving efficiency. The version control feature enables everyone to work on the latest code and data.

  1. Continuous Integration and Continuous Delivery (CI/CD)

MLOps with its automation capabilities integrates CI/CD pipelines for automated testing, building, and deployment of models. Ensures quality standards are met before deployment of ML models. Moreover, CI/CD speeds up the delivery of ML solutions by adhering to faster Time-to-Market responsibilities.

  1. Scalability and Performance Optimization

MLOps helps you manage infrastructure to scale models as per the demand. It identifies performance bottlenecks and thus improves model efficiency. Also, it optimizes resource usage and costs

Though many features are in common between SDLC and ML Life Cycle. There are unique factors to be considered in Machine Learning Models. 

Data Bias and Data Size: It is important to ensure the data bias is nullified and the data sample size is significantly large.

Explainability Feature: The Model deployed should be accompanied by a description for all the stakeholders on why the model took a particular decision.

 Use Evolving Frameworks like TensorFlow, Scikit-learn, Pytorch, etc.

Employ Evolving algorithms such as SVM, Bayes, KNN, K-Means, Random Forests, Reinforcement Learning

MetricDescription
Model PerformanceMeasures how well the model is performing its intended task
Data QualityAssesses the quality of the data used to train and evaluate the model.
Model DriftDetects changes in the underlying data distribution that could impact model performance over time.
Deployment FrequencyMeasures how often models are deployed to production.
Deployment Success RateIndicates the percentage of successful model deployments.
Model UptimeMeasures the amount of time a model is available for predictions.
Prediction LatencyMeasures the time it takes for the model to generate a prediction.
CostTracks the costs associated with running the model.
Resource UtilizationMonitors the usage of resources (e.g., CPU, memory, GPU) by the model.
Feedback Loop EffectivenessMeasures how well feedback from model performance is used to improve the model.

Embracing Machine Learning Operations (MLOps) is crucial for organizations looking to scale AI effectively. MLOps streamlines the integration of machine learning models into production, enhances collaboration between data teams, and ensures continuous delivery and monitoring. By adopting MLOps, businesses can harness the full potential of AI, accelerating innovation, improving efficiency, and driving data-driven decision-making at scale.

Machine Learning operation for Scalable Gen AI Services
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