What is MLOps or Machine Learning Operations ?
Machine Learning Operations (MLOps) is a set of practices that combines machine learning, developer operations (DevOps), and data engineering to streamline the deployment, monitoring, and management of machine learning models. It aims to bridge the gap between model development and operationalization, ensuring reliable and efficient machine learning workflows.
MLOps emphasizes automation, continuous integration, continuous delivery (CI/CD), and cross-team collaboration to enhance scalability, reproducibility, and deployment speed. By integrating these practices, MLOps enables organizations to effectively manage the entire lifecycle of machine learning models—from development to production—while ensuring high performance and reliability. MLOps also ensures continuous model monitoring and risk mitigation, helping organizations maintain regulatory compliance and build trust in their AI system.
Fig 1: Key components of MLOps (e.g., data engineering, model development, deployment, monitoring).
Why is MLOps important for your business growth?
MLOps is crucial for your business because it enhances efficiency, reliability, and scalability in deploying machine learning models.
Enhanced Efficiency for faster GTMs: By streamlining ML workflows, MLOps reduces errors and boosts collaboration between data science and operations teams, leading to faster time-to-market and improved model quality.
Data-Driven Decision Making: MLOps enables data-driven decision-making, optimizing operational efficiency, and fostering innovation. For business leaders, particularly in the C-suite, MLOps provides strategic advantages.
Continuous Integration and Delivery: MLOps supports continuous integration and delivery (CI/CD), allowing for rapid iteration and deployment of new features based on real-time insights. This agility helps leaders adapt quickly to changing market shifts and evolving customer needs.
Increased ROI: McKinsey reports that companies implementing comprehensive MLOps practices can increase the value they realize from their AI work by as much as 60%. By leveraging MLOps, business leaders can ensure their AI investments yield sustainable, scalable, and high-impact results, ultimately driving business growth and success.
"Our People Analytics team at Swiggy had been trying to build the "unhappy employee" prediction model using contemporary Python scripts and the like. However, the ML Ops was a big challenge we were facing while deploying these solutions. QuickML with its API capabilities helped us deploy the model overnight, and then use the production data back in our data mart for interventions."
Daksh Sahni
People Analytics & Behavioural Sciences, Swiggy
Components of MLOps pipeline
MLOps techniques using Catalyst QuickML facilitates continuous monitoring and risk mitigation that help maintain regulatory compliance and build trust. The following steps are involved in the MLOps pipeline building:
Data preprocessing
Catalyst QuickML simplifies data preprocessing by providing intuitive techniques to clean, transform, and extract data for machine learning models. The different data preprocessing techniques available in QuickML are Data Cleaning, Data Transformation, and Dataset Extraction.
Data can be sourced from widely-used cloud storage services, such as Google Cloud, AWS S3 object storage, or Azure Blob, as well as from in-house Zoho products like Bigin, CRM, Creator, or Recruit. Catalyst QuickML also offers functionalities for handing missing values, scaling features, different data visualization charts and many more. This way, you can ensure that only high-quality data is fed into the ML models for effective training.
Train model
Once a ML pipeline is executed successfully, a ML model is created. This model view can be viewed to understand its internal metrics. Catalyst QuickML streamlines model training with its user-friendly interface and offers a range of pre-built algorithms, including both supervised and unsupervised methods. With automated machine learning (AutoML) capabilities, users can quickly experiment with different models and hyperparameters to find the best fit.
Evaluate model
Catalyst QuickML provides you with cross-validation metrics to track performance of both classification or regression models. Cross-validation works by training the model on a subset of the data and evaluating its performance on the remaining subset. This process is repeated multiple times, and the average performance across all subsets is used to evaluate the model's overall performance. Users can select relevant metrics and data visualization types to assess accuracy, precision, recall, and other key performance indicators (KPIs).
Deploy the model
Deploying models with Catalyst QuickML is straightforward and efficient. The platform supports seamless integration with other Zoho applications and external systems, allowing users to deploy models as APIs with minimal effort. This ensures that the models can be readily accessed and utilized in real-time applications.
Serve the model
Serving models with Catalyst QuickML ensures scalability and reliability. The platform's infrastructure supports real-time predictions, handling multiple requests simultaneously without compromising performance. This is critical for applications that require immediate responses based on model predictions.
Monitor the model
Monitoring deployed models is crucial for maintaining their performance over time, Picking a platform that enables comprehensive monitoring allows you to track different model versions and performance metrics, detect anomalies, and receive alerts for any deviations. This continuous monitoring helps to identify and resolve issues proactively, ensuring that models remain accurate and effective.
Best practices to optimize MLOps Strategy
MLOps techniques using Catalyst QuickML facilitates continuous monitoring and risk mitigation that help maintain regulatory compliance and build trust. The following steps are involved in the MLOps pipeline building:
Leverage scalable infrastructure to deploy models
The MLOps pipeline must scale efficiently and reliably, whether in terms of data size or computational resources. As your company grows, so does your dataset, and a model that performs well for your small dataset may not scale for multiple large datasets.
Serverless platforms like Catalyst ensure that your ML deployments auto-scale according to traffic needs and also offer an infrastructure capable of supporting continuous delivery and integration. A robust MLOps infrastructure goes a long way in overcoming the challenges of model deployment, especially as models evolve.
Improve model quality by implementing version control
Model performance degradation is the most common challenge faced by companies, due to constantly changing datasets, algorithms, and user interactions. To overcome this, updated versions of models must be constantly shipped. Model versioning empowers you to track hyperparameters for different versions of the model, their performance metrics, and the data they were trained on.
Catalyst QuickML ensures that your ML models are versioned whenever there is a change in the pipeline stage configuration during the pipeline execution, enabling you to track performance changes over time.
Validate the datasets for errors
Identifying errors in your data set is crucial to the long-term performance of your ML model. Datasets must be checked for relevancy, consistency, and accuracy before being used to build ML models. This includes detecting missing values, unique and duplicate values, and inconsistent data formats, and validating the data for the required business logic.
QuickML’s data profiling automatically analyzes any data uploaded to the QuickML dataset module. This process helps improve data quality by enabling pre-processing, data visualization, and overall quality scoring. Detecting and correcting data quality issues improves model reliability.
Stay relevant with automated model retraining and updating
Models tend to become obsolete and decay over time. To remain effective, ML models must be retrained regularly. Whenever new data becomes available, it is imperative to use them to retrain your ML models to keep them updated. Your MLOps pipeline should include an automated process that triggers model retraining based on specific performance metrics or predefined conditions. This ensures that models adapt to changing business requirements or data, and remain accurate and relevant.
Prioritize overall security
An integral part of MLOps strategy is ensuring compliance and governance. Continuously monitoring your ML models for potential biases or performance drifts leading to security vulnerabilities is crucial. Additionally, Catalyst QuickML ensures that you secure your model endpoints with robust authentication and authorization mechanisms. The live endpoints exposed via REST APIs from QuickML service can be accessed using, External OAuths authentication and Internal authentication.
Resources
Explore our comprehensive resources section, tailored for anyone looking to dive deep into Catalyst solutions for MLOps. Transform your business and drive growth with advanced MLOps strategy using Catalyst.