MLOps Managed Services, Processes and Tools

As AI and machine learning permeate every industry, organizations face a critical challenge—not in developing ML models, but in maintaining them at scale. Despite sophisticated tools and techniques, most businesses struggle to transition from models trained on historical datasets to applications handling real-world data volumes. MLOps addresses this gap by unifying ML development and operations, automating the entire machine learning lifecycle from integration and testing to deployment and infrastructure management.

What is MLOps? 

MLOps is the communication between data scientists and operations teams. It has mixed data scientists and services designed to automate ML pipelines and gain more valuable insights into production systems. It provides reproducibility, visibility-managed access control, and computing resources to test, train, and deploy AI algorithms to Data engineers, business analysts, and operations teams. 

According to the survey, data scientists are not focused on data science tasks. They spend most of their time on other relevant tasks such as data preparation, data wrangling, management of software packages and frameworks, infrastructure configurations, and integration of various other components. Data scientists can quickly implement and train a Machine Learning Model with excellent performance on an offline dataset by providing relevant training data for particular use cases. However, the real challenge is not to build an ML model. The problem lies in creating an integrated ML system and continuing to operate it in production. 

It is a collection of practices for communication and collaboration between operations professionals and data scientists. Applying these practices simplifies the management process, increases the quality, and automates the deployment of Deep Learning and Machine Learning models in large-scale production environments. It works with data developers, machine learning engineers, and DevOps to turn the algorithm into production systems once it's ready. It aims to improve production models' automation and quality while considering business and regulatory requirements. 

The primary role of DevOps is to take continuous feedback of the process at every step. Click to explore about, Role of ML and AI in DevOps Transformation

Why is MLOps Important? 

MLOps emerged as a hybrid approach in AI to address key challenges:

• Orchestration of Pipelines: ML model development involves multiple pipelines (pre-processing, feature engineering, training, inference). MLOps automates their coordination for seamless updates.

• Managing the Full Lifecycle: Traditional DevOps struggles with ML’s dynamic sub-parts. MLOps integrates people, processes, and technology for optimized deployment.

• Scaling ML Applications: Managing thousands of models at scale is complex. MLOps streamlines production pipelines for efficient scaling.

• Maintaining ML Health: Ensures post-deployment model reliability with automated drift detection (data/model drift) and adaptive corrections.

• Continuous Integration & Deployment: ML models require specialized CI/CD tools to handle large-scale deployments effectively.

• Model Governance: Enables large-scale monitoring, logging, audit trails, and compliance continuity for robust model performance tracking.

Architecture of MLOps 

The MLOps (Machine Learning Operations) architecture is a set of practices and procedures for managing the machine learning lifecycle, from data preparation to model deployment and maintenance. It aims to provide a standard and flexible way of working on learning models and to ensure that they can be easily maintained and updated over time. The MLOps architecture has several key features, including: 

• Data Management - This stage focuses on collecting, organizing, and maintaining data for machine learning models. It may include setting up an automated data transfer system to streamline the data flow from source to model. 

• Model Development - In this phase, machine learning models are designed using various algorithms and techniques. Tasks include selecting optimal hyperparameters, validating the model, and evaluating its performance. 

• Model Deployment - This stage involves integrating models into production environments like web or mobile applications. Often, an API is created to enable other applications to interact with the model. 

• Model Monitoring - Regular monitoring ensures the model continues to perform as expected. An alert system can notify developers when the model's performance deviates from set expectations. 

An effective MLOps operation must be supported by various tools and technologies, such as management models, automated measurement systems, and continuous integration/continuous delivery(CI/CD) pipelines. By providing a structured and structured approach to managing machine learning models, the MLOps architecture can help organizations realize the full potential of machine learning and stay ahead of the world's rapid evolution in AI and machine learning. 

ML Project Life Cycle and MLOps Process 

The steps involved in the ML Life cycle and MLOps are all about advocating for automation and monitoring at all the above steps. Machine learning project development is an iterative process that means we continue to iterate from each of the above processes (except scoping) during the life cycle of a model to improve the efficiency of the process. 

For instance, we improve the data when new data comes in or feature engineer new features from existing data. We iterate through the modelling process according to its performance in production. Accordingly, the deployed model gets replaced with the best model developed during iteration. This process goes on with the iteration, but one should follow some best practices while iterating. 

The critical phases of MLOps are: 

• Data gathering 

• Data Analysis 

• Data transformation/preparation 

• Model training and development 

• Model validation 

• Model serving 

• Model monitoring 

• Model re-training 

MLOps Process for Continuous Delivery 

Developing a machine learning model, deploying it fast and cheaply, and maintaining it over time becomes difficult. Any team developing Machine Learning solutions must follow best practices to get the most out of the models. This helps avoid "machine learning technical debt." 

The best practice that needs to be followed while developing ML solutions: 

Data Validation - Data is the most crucial part of the ML system. If it does not validate correctly, it may cause various issues in the model. Therefore, input data that is fed to the pipeline must be validated. Otherwise, as data science says, garbage in, garbage out. As a result, data must be considered a top priority in the ML system. It should be continuously monitored and validated at every execution in the ML pipeline.

Experiment and track experiments - To get the best accuracy, one needs to do experiments. Machine learning is all about experimentation. It may involve trying out different combinations of code, preprocessing, training, evaluation methods, data, and hyperparameter tuning. Each unique combination produces different metrics to compare with other experiments and keep track of. Later, you can compare which combination is performing better. 

Model validation across segments - Machine learning models' performance can degrade over time, and they need to be retrained to maintain good performance. Before deploying a model into production, it needs to be validated. Model validation includes producing metrics (e.g., accuracy, precision, rose, etc.) on the test datasets to check the model's performance so it can fit business objectives.

The model should also be validated on various data segments to ensure they meet requirements. Otherwise, the model can be biased in the data, and several incidents have happened where the model was biased and performed inadequately for some users. 

Reproducibility - Reproducibility means that in machine learning, every phase should produce the same results, given the same input. It can be data preprocessing, model training, or model deployment. Reproducibility is challenging and requires tracking model artefacts such as code, data, algorithms, packages, and environment configuration. 

Monitoring predictive service performance - The practice mentioned above can help you deliver a robust ML model. In operations, different metrics need to be measured to evaluate the performance of the deployed model. These metrics evaluate model performance regarding business objectives. Users might need good performance and better accuracy of the model, but they also need as fast as possible and availability all the time. To monitor operational metrics such as: 

• Latency: measured in milliseconds 

• Scalability: how much traffic can the service handle at the expected latency? 

• Service update: how much downtime is introduced during the service update? 

For instance, delaying any service can impact the user, and it can cause loss to the business. 

Automate the process - Managing machine learning tasks manually becomes difficult and time-consuming when the models get into production. Data preprocessing, model training and retraining, hyperparameter tuning, and model deployment can be automated. If data drift, model drift or the model's performance degrades. So it can be retrained automatically. It just needs to be triggered. After automating the process, the error margin becomes less and more models can be deployed. An ML pipeline can be used to automate the process. So, the model can follow continuous training and continuous delivery. 

AWS charges each SageMaker customer for the computation, storage, and data processing tools used to build, train, perform and log machine learning models and predictions. Know more about Amazon SageMaker

Challenges of Productionizing ML Models 

The common challenges organizations face while productionizing the Machine Learning model into active business gains are listed below. 

• Dataset Dependency: In real-world scenarios, the lack of data regularity over time can lead to poor model performance, as data frequently changes.

• Simple to Complex Pipelines: Initially, models may be trained and deployed manually, but in business, regular retraining on new data is essential, requiring automated retraining pipelines.

• Multiple Training Pipelines: Models may need human approval for production, and when using ensemble models or federated learning, managing multiple pipelines becomes more complex.

• Scalability Issues: ML models designed in a data scientist sandbox often face scalability issues when deployed, especially with hardware requirements like GPUs for complex models and CPUs for simpler ones.

• Production ML Risk: Continuous monitoring is needed in production to track performance metrics and address challenges like data drift, using tools like canary pipelines and A/B testing.

• Process and Collaboration: MLOps involves cross-functional teams (data scientists, analysts, and operations) working together to manage models, algorithms, and pipelines, which requires version control and object storage.

• Data-Related Challenges: Clean, relevant, and secure data is crucial for model accuracy, and privacy concerns must be addressed with proper protocols.

• Model-Related Challenges: Model performance depends on suitability, interpretability, and adaptability. Overfitting and model drift can harm performance over time.

• Infrastructure-Related Challenges: Scalable, reliable infrastructure is essential for training, testing, and deployment, with proper resource management to avoid system failures.

• People and Process Challenges: Successful MLOps requires effective collaboration and well-defined processes across teams to manage and govern ML models.

How is MLOps Different from DevOps? 

MLOps Best Practices 

Scope Management in MLOps 

Scoping is a crucial initial step that involves defining project goals aligned with machine learning objectives. For instance, if the business team requests a conversational AI to handle FAQs on a website, this goal needs to be translated into a machine learning objective, such as developing a question-answering model. 

Key Steps for Effective Scoping 

• Understand the Business Problem: To avoid wasted development effort, fully comprehend the business problem and verify your understanding with stakeholders before proceeding. 

• Team brainstorming: Gather and explore potential solutions from the team, encouraging diverse and innovative ideas. 

• Conduct Research: With a defined problem and initial ideas, research solution-oriented approaches to outline a roadmap. 

• Define the Development Roadmap: Create a visual flow of the development process with steps, timelines, and special dependencies (e.g., a data dependency from a data engineering team). Verify the roadmap with stakeholders. 

• Prepare an Approach Document: This document should outline the approach to solving the business problem, including any initial algorithms. Obtain stakeholder input to ensure alignment on the development strategy. 

Data Processing Best Practices 

Data processing is foundational before modelling. The following best practices help ensure data quality and integrity: 

• Understand Data Types and Issues - Classify datasets (structured vs. unstructured) and address specific data processing needs accordingly. 

• Define the Dataset for Structured Data - Gather detailed information on each data column to avoid ambiguities. Clearly distinguish features and labels before proceeding. 

• Ensure Consistent Labeling for Unstructured Data - When multiple labellers are involved, provide clear labelling instructions to maintain consistency across the dataset. 

• Data Versioning - Use data versioning tools like DVC to track dataset versions or maintain versioning records manually if tools are unavailable. This allows for reproducible experiments. 

• Consistency in Data Pipelines - Ensure consistency in data pipelines across development, testing, and production stages. Make pipelines fault-tolerant to handle exceptions in production. 

• Balanced Train/Validation/Test Splits - Ensure that train/dev/test splits represent the overall dataset distribution, preserving the class balance (e.g., 30% positive samples across all splits). 

• Prevent Data Leakage - Avoid exposing target information in training data that would be unavailable during prediction, as this can lead to overestimated performance during training. 

Data Modeling Best Practices 

The following best practices ensure the effective development and evaluation of machine learning models: 

• Define a Baseline and Benchmark the Model: Establish a baseline using a simple algorithm or human-level performance for unstructured data, ensuring a reference point for model comparisons. 

• Model Versioning and Tracking: Use model versioning tools like MLflow to track experiments. Alternatively, track versions manually in text files if tools are unavailable. 

• Error Analysis: Post-training, perform error analysis to identify areas where the model performs poorly, particularly in specific classes. Use metrics like precision, recall, and F1 score in addition to accuracy. 

• Data-Centric Approach over Model-Centric: Prioritize data improvements over model complexity. Simple models on high-quality data often outperform complex models with poor data quality. 

• Data Augmentation for Unstructured Data: Create additional examples in areas with higher error rates. 

• Feature Engineering for Structured Data: Adding new features if creating new samples is impractical. 

Best Practices for Productionizing ML Models 

• Model Versioning: Track different versions of models to ensure reproducibility and easy rollback if needed. Use tools like DVC or MLflow for version control. 

• Automation of Model Deployment: Automate the deployment pipeline to minimize manual errors and ensure smooth updates. CI/CD pipelines should be implemented for model testing, validation, and deployment. 

• Scalability and Performance: Ensure that the model can scale with growing data and handle production traffic. Leveraging cloud infrastructure or containerization to scale the model efficiently (e.g., Kubernetes, Docker). 

• Monitoring and Logging: Continuously monitor model performance in production. Track metrics such as accuracy, latency, resource usage, and drift over time. Set up alerts for abnormal behaviours. 

• Model Explainability: Incorporate explainability tools (like SHAP or LIME) to ensure that model predictions are interpretable, especially for sensitive applications (e.g., finance or healthcare). 

MLOps on Major Cloud Platforms 

MLOps in Azure 

Azure MLOps for ML enables data science and IT teams to collaborate and increase model development and deployment speed while monitoring, validating, and governing machine learning models. 

• Training model for reproducibility with advanced tracking of datasets, experiments, and code. 

• Autoscaling, no-code deployment, powerful managed-to-computer, and tools for quick model deployment and training. 

• Efficient workflows with scheduling and management capabilities to build and deploy with CI/CD. 

• Advanced capabilities for governance and control objectives and promote model transparency. 

MLOps in AWS 

AWS MLOps (Machine Learning Operations) helps streamline and enforce architecture best practices for ML model production. The extendable framework provides a standard interface for managing ML pipelines for AWS ML services and other services. AWS template allows customers to upload their trained models, configure the pipeline, and monitor their operations. This increases the team's agility and efficiency by enabling them to repeat successful processes at a large scale. 

• Initiates a pre-configured pipeline through an API call or a Git repository 

• Automatically deploys a trained model and provides an inference endpoint. 

• Supports running integration tests to ensure the deployed model meets expectations 

• Allows multiple environments to keep the Machine Learning model's life cycle. 

• Notifies users about the pipeline outcome via email. 

MLOps in GCP 

Data scientists and ML engineers are trying to apply DevOps principles to ML systems. It is an ML engineering practice that aims to unite Machine Learning system development and ML system operation. It helps automate and monitor all ML system construction steps, including integration, release, deployment, infrastructure management, and testing. 

Characteristics of MLOps GCP (Google Cloud platform): 

• Rapid experiment: ML experiment steps are orchestrated, which automates the transition between steps and leads to the rapid iteration of experiments and better production readiness. 

• Experimental-operational symmetry: The critical aspect of its practice for uniting DevOps is implementing a pipeline used in the development or experiment environment or the preproduction and production environment. 

• Continuous delivery of models: An ML pipeline in production continuously delivers services to new models trained on new data. The model deployment step is automated, which serves the trained and validated model as a prediction service. 

• Pipeline deployment: This helps deploy a trained model as a prediction service for production. The trained pipeline is deployed automatically and recurrently to serve the trained model. 

What are the Top MLOps Tools? 

Tools are available based on the purpose for which one wishes to use them. So, to decide which tools to use, firstly, one must have a clear and concrete understanding of the task for which they will use that tool. Before choosing any tool, one should carefully consider the benefits and drawbacks of each tool before deciding on one for the project. Furthermore, this must ensure the tools are compatible with the rest of the stack. There are tools available for performing the tasks, such as: 

Model Metadata Storage and Management 

It provides a central place to display, compare, search, store, organize, review, and access all models and model-related metadata. The tools in this category are experiment tracking tools, model registries, or both. The various tools that one can use for metadata management and storage are: 

• Comet 

• Neptune AI 

• ML flow 

Data and Pipeline Versioning 

Every team needs the necessary tools to stay updated and aligned with all version updates. Data versioning technologies can aid in creating a data repository, tracking experiments and model lineage, reducing errors, and improving workflows and team cooperation. One can use various tools for this, such as: 

• DagsHub 

• Pachyderm 

• lake FS 

• DVC 

Hyperparameter Tuning 

Finding a set of hyperparameters that produces the best model results on a given dataset is known as hyperparameter optimization or tuning. Hyperparameter optimization tools are included in MLOps platforms that provide end-to-end machine learning lifecycle management. One can use various tools for hyperparameter tuning, such as: 

• Ray tune 

• Optuna 

• HyperOpt 

• Scikit-Optimize 

Run Orchestration and Workflow Pipelines 

A workflow pipeline and orchestration tool will help when the workflow contains many parts (preprocessing, training, and evaluation) that can be done separately. Production machine learning (ML) pipelines are designed to serve ML models to a company's end customers that augment the product and/or user journey. Machine learning orchestration (MLO) aids in the implementation and management of process pipelines from start to finish, influencing not just real users but also the bottom line. The various tools that one can use for running orchestration and workflow pipelines are: 

• Kedro 

• Apache Airflow 

• Polyaxon 

• Kubeflow 

Model Deployment and Serving 

The technical task of exposing an ML model to real-world use is known as model deployment. Deployment integrates a machine learning model into a production environment to make data-driven business decisions. It's one of the last steps in the machine learning process, and it's also one of the most time-consuming. The various tools that one can use for model deployment and serving are: 

• Seldon 

• Cortex 

• BentoML 

Production Model Monitoring 

The most crucial part after deploying any model to production is its monitoring, and if done properly, it can save a lot of time and hassle (and money). Model monitoring includes monitoring input data drift, monitoring concept drift, and monitoring hardware metrics. The various tools that one can use for model monitoring after production are: 

• Akira AI 

• AWS SageMaker Model Monitor 

MLOps Operations and Model Management 

ML Model Management Overview

Organizations now face challenges deploying ML systems at scale due to increasing data and models. Managing these systems requires understanding the entire ML lifecycle, which involves various teams:

• Product/Business Development: Defines business goals with KPIs.

• Data Engineering: Prepares data for ML models.

• Data Science: Develops and defines ML models.

• DevOps/IT: Manages deployment and monitoring.

MLOps Services for Enterprises

MLOps enables reliable deployment and maintenance of ML systems in production. It integrates data engineering, DevOps, and ML practices, streamlining the lifecycle. Key MLOps services include:

• Design Algorithms: Implementing design patterns (e.g., workflows, feature stores) for resilient, reproducible, and flexible ML solutions.

• Model Development: Involves data engineering, ML model engineering, and validation.

• Model Operations: Automates ML pipelines and supports continuous model training and re-training.

• CI/CD Pipeline: Automates the deployment, testing, and updates of ML models in production.

Reproducibility and Feedback

• Reproducibility: Ensures models can be reproduced despite changes like hyperparameters or architecture.

• Feedback: Monitors performance and identifies data drift to trigger re-training.

ML Operations

• Controllability: Ensures production updates are controlled to prevent instability.

• Automation: Integrates ML pipelines with DevOps tools for efficient workflows.

• Model Management: Includes versioning, tracking, and managing complex pipelines.

• Model Versioning: Supports reversible changes for stability and fault tolerance.

• Model Tracking: Uses tools like MLflow to track experiments and select the best model.

ML Monitoring

• Data Drift: Detects changes in input-output relationships and triggers necessary model adjustments.

• Inference Monitoring: Tracks model performance and data drift in production.

XenonStack for MLOps

XenonStack provides comprehensive MLOps capabilities, including:

• Model Lifecycle Management: Ensures seamless movement from development to production.

• Model Versioning & Iteration: Supports model updates and version control.

• Model Monitoring: Continuously tracks model performance and accuracy.

• Model Governance: Ensures auditability, compliance, and access control.

• Model Security: Protects models from unauthorized access and attacks.

• Model Discovery: Offers model catalogs for transparency and tracking.

Future of MLOps 

The future of MLOps, especially for TinyML, will see key advancements:

• AutoML & Auto-Tuning: Automation in ML model development and performance optimization will expand, including on platforms like Azure and AWS MLOps.

• Model Interpretation: Increased demand for transparency will drive innovations to make even TinyML models in IoT and edge computing more explainable.

• Federated Learning: Decentralized training will enhance data privacy and security, becoming integral to Azure and AWS MLOps strategies.

MLOps will continue evolving with enhanced TinyML capabilities, improved transparency, and stronger privacy measures, ensuring seamless DevOps integration.

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