🎉 AI Engineers: Join our webinar on Prompt Engineering for AI Agents. Register here >>

May 30, 2024 - last updated

MLOps Platforms: Benefits, Key Features, & Top 5 Solutions

Gon Rappaport
Gon Rappaport

Solutions Architect

7 min read Oct 10, 2022

What Is an MLOps Platform?

An MLOps (Machine Learning Operations) platform is a set of tools, frameworks, and practices that streamline the process of deploying, monitoring, and maintaining machine learning models in production. It helps bridge the gap between data science and IT operations by automating various tasks involved in the machine learning lifecycle. The goal of an MLOps platform is to ensure that machine learning models are efficiently integrated, managed, and scaled within an organization while maintaining quality and performance.

What Are the Benefits of MLOps Platforms?

MLOps platforms support ML engineers by streamlining and automating the processes involved in the machine learning lifecycle. These benefits include:

  • Faster time-to-market: MLOps platforms enable organizations to deploy machine learning models more rapidly by automating key steps in the ML lifecycle, such as data preprocessing, model training, and deployment. This accelerated process helps businesses respond to changing market conditions and customer needs more quickly.
  • Improved collaboration: MLOps platforms foster collaboration between data scientists, ML engineers, and other stakeholders by providing a centralized hub for managing ML projects. These platforms often include features for communication, project management, and knowledge sharing, which help break down silos between teams, facilitate smooth handoffs between different stages of the ML lifecycle, and also improve business outcomes, such as more accurate models and faster time to market.
  • Enhanced model quality and performance: MLOps platforms offer tools for automated model evaluation, hyperparameter tuning, and performance monitoring, ensuring that deployed models meet performance standards and deliver accurate predictions. Additionally, these platforms can help detect and alert teams to data drift or model degradation, enabling proactive maintenance and retraining.
  • Reproducibility and traceability: With MLOps platforms, organizations can maintain version control for data, code, models, and experiments. This enables data scientists to easily reproduce results, track model lineage, and compare different model versions, which is crucial for maintaining high-quality models and adhering to industry regulations and standards.
  • Scalability: MLOps platforms are designed to handle large-scale machine learning projects, supporting the deployment and management of multiple models simultaneously. They can also integrate with cloud infrastructure and leverage distributed computing resources to scale model training and deployment according to the needs of the organization.
  • Cost savings: By automating various aspects of the ML lifecycle and enabling efficient collaboration between teams, MLOps platforms can help organizations reduce the time and resources spent on machine learning projects. This translates into cost savings, both in terms of human resources and computing infrastructure.
  • Governance and compliance: MLOps platforms provide tools and processes for model governance, access control, and auditing, ensuring that organizations can comply with industry regulations and maintain ethical and responsible use of machine learning models.

Key Features of MLOps Platforms

MLOps platforms typically offer the following features:

  • Data versioning: Tracking and managing different versions of datasets to ensure reproducibility and traceability in ML projects.
  • Model versioning: Storing and managing multiple versions of ML models, including their code, configurations, and dependencies.
  • Experiment tracking: Logging, comparing, and visualizing experiments, hyperparameters, and results to facilitate the model selection process.
  • Continuous integration and continuous delivery (CI/CD): Automating the process of building, testing, and deploying ML models to ensure seamless updates and reduce errors.
  • Model validation: Ensuring that ML models meet performance and quality requirements before deployment through rigorous testing and validation.
  • Model deployment: Streamlining the process of deploying ML models to various environments, such as cloud, on-premises, or edge devices.
  • Model monitoring: Tracking the performance of deployed models, detecting data drift and model degradation, and setting up alerts to maintain model accuracy and reliability.
  • Model governance: Managing access control, compliance, and security in ML workflows, ensuring transparency and adherence to organizational policies and regulations.
  • Collaboration: Facilitating effective communication and collaboration among data scientists, ML engineers, and operations teams, driving faster innovation and decision-making.
  • Scalability: Supporting the development and management of ML models at scale, enabling organizations to grow and adapt to increasing data volumes and complexity.
  • Integration: Offering compatibility with popular data science tools, libraries, and frameworks, enabling seamless integration into existing workflows and ecosystems.

Top MLOps Platforms and Tools

Amazon SageMaker

SageMaker is a fully managed MLOps platform provided by Amazon Web Services (AWS). It simplifies and accelerates the end-to-end ML lifecycle, offering capabilities such as data preprocessing, model training, hyperparameter tuning, deployment, and monitoring. SageMaker integrates with other AWS services and popular open-source frameworks.

Azure Machine Learning

Offered by Microsoft, Azure Machine Learning is an MLOps platform that streamlines the development, deployment, and management of ML models. It provides a wide range of tools and services for collaboration, experiment tracking, model versioning, automated ML, and deployment to the cloud or edge devices. It supports popular frameworks like TensorFlow and PyTorch, and seamlessly integrates with other Azure services.

Google Cloud AI Platform

This MLOps platform from Google Cloud combines Google’s AI and ML technologies to simplify and accelerate ML workflows. It offers a unified environment for building, training, and deploying models using popular frameworks like TensorFlow, PyTorch, and scikit-learn. Key features include data preprocessing, distributed training, hyperparameter tuning, model deployment, and monitoring, all accessible through a web-based interface.


An open-source MLOps platform created by Databricks, MLflow provides a modular approach to streamline the ML lifecycle. It offers tools for experiment tracking, project packaging, model versioning, and model deployment. MLflow supports a wide range of ML libraries and frameworks and can be deployed on-premises or in the cloud.

TensorFlow Extended (TFX)

Developed by Google, TFX is an end-to-end platform for deploying production ML pipelines using TensorFlow. It provides a suite of components to manage data validation, preprocessing, model training, evaluation, and deployment. TFX integrates with popular data processing systems like Apache Beam and Apache Flink, and can be deployed on various platforms, including Google Cloud, AWS, and on-premises infrastructure.

Aporia in Your MLOps Platform: Mastering Model Monitoring and Management

Aporia fits into the MLOps platform as a crucial component in the model monitoring and management stage. In a typical MLOps pipeline, different stages include data ingestion, data preprocessing, model training, model validation, model deployment, and continuous monitoring. Aporia comes into play during the continuous monitoring phase, after a machine learning model has been deployed to a production environment.

Aporia’s advanced monitoring tools enable ML engineers and data scientists to keep a close watch on their models’ performance metrics, track potential data drift, detect anomalies, and understand the reasoning behind model predictions. This continuous monitoring ensures that the models maintain their expected performance levels and that any anomalies are quickly identified and addressed. By integrating Aporia into the MLOps platform, organizations can automate the monitoring and maintenance of their machine learning models, significantly enhancing their ability to scale AI applications and maintain their effectiveness in rapidly changing environments. Aporia empowers organizations with key features and tools to ensure high model performance:

Production Visibility

  • Single pane of glass visibility into all production models. Custom dashboards that can be understood and accessed by all relevant stakeholders.
  • Track model performance and health in one place. 
  • A centralized hub for all your models in production.
  • Custom metrics and widgets to ensure you’re getting the insights that matter to you.

ML Monitoring

  • Start monitoring production models in ±7 minutes.
  • Instant alerts and advanced workflows trigger. 
  • Customize monitors to detect data drift, model degradation, performance, etc.
  • Track relevant custom metrics to ensure your model is drift-free and performance is driving value. 
  • Choose from our automated monitors or get hands-on with our code-based monitor options. 

Explainable AI

  • Get human readable insight into your model predictions. 
  • Simulate ‘What if?’ situations. Play with different features and find how they impact predictions.
  • Gain valuable insights to optimize model performance.
  • Communicate predictions to relevant stakeholders and customers.

Root Cause Investigation

  • Slice and dice model performance, data segments, data stats, or distribution.
  • Identify and debug issues.
  • Explore and understand connections in your data.

To get a hands-on feel for our ML observability platform, we recommend:

Green Background

Control All your GenAI Apps in minutes