31 Essential MLOps Tools for Seamless AI Model Management

You may feel excited about the possibilities as your machine learning model moves from development to production. Even so, you might also have lingering concerns about operational efficiency. Not only do models need to be accurate, but they must also perform reliably over time. That means addressing any issues that arise as quickly as possible. MLOps tools help mitigate these challenges by automating and streamlining the processes for deploying and monitoring machine learning models in production. This article will help you get started with MLOps tools to help you build, deploy, and scale AI models with minimal friction. Additionally, understanding AI Inference vs Training is crucial to optimizing model performance and ensuring smooth deployment.

Inference's AI inference APIs can help you achieve your objectives by ensuring models perform reliably in real-world applications.

What are MLOps Tools?

MLOps tools are software programs that help data scientists, machine learning engineers, and IT operations teams integrate, streamline workflows and machine learning components, and collaborate more effectively. Ultimately, they support the central goal of MLOps:

• Automating the process of generating, deploying, and monitoring models.
• Merging machine learning, DevOps, and data engineering.

What Do MLOps Tools Do? Get Ready to Meet Your New Best Friends

MLOps tools can be categorized based on their primary functions within the MLOps pipeline. By understanding these categories, organizations can choose the tools that align with their unique needs.

• MLOps Platform: This comprehensive solution includes capabilities for every aspect of the MLOps pipeline, from data ingestion to model deployment and monitoring.
• Experiment Tracking and Model Management: Manages machine learning experiments and different model versions to ensure they can be shared and reproduced
• Data Versioning and Management: Tracks changes to data, maintains data lineage, and ensures data quality for machine learning projects
• Feature Stores: A centralized repository for storing, managing, and serving features (measurable characteristics within a dataset) to machine learning models
• Model Serving and Deployment: Streamlines the process for delivering machine learning models to production environments
• Model Monitoring and Observability: Tracks metrics and provides insights into model performance in production
• Workflow Orchestration: Automates and coordinates various steps in the MLOps pipeline

Choosing the Right MLOps Tool

Some tools may span multiple categories and offer more comprehensive solutions. This means the best tool for your organization depends on a project’s unique requirements, your team’s skills, and your existing technology infrastructure.

It’s worth noting that some tools may span multiple categories and offer more comprehensive solutions. This means the best tool for your organization depends on a project’s unique requirements, your team’s skills, and your existing technology infrastructure.

Related Reading

• Model Inference
• AI Learning Models
• MLOps Best Practices
• MLOps Architecture
• Machine Learning Best Practices
• AI Infrastructure Ecosystem

31 Essential MLOps Tools for Seamless AI Model Management

1. Inference – The New Frontier in MLOps Tools

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

2. Large Language Models Framework

With the introduction of GPT-4 and GPT-4o, the race has begun to produce large language models and realize the full potential of modern AI. LLMs require vector databases and integration frameworks for building intelligent AI applications.

3. Qdrant

Qdrant is an open-source vector similarity search engine and vector database that provides a production-ready service with a convenient API, allowing you to store, search, and manage vector embeddings.

Its most notable features include:

• Easy-to-use API: It provides an easy-to-use Python API, allowing developers to generate client libraries in multiple programming languages.
• Fast and accurate: It uses a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search, providing state-of-the-art search speeds without compromising accuracy.
• Rich data types: Qdrant supports various data types and query conditions, including string matching, numerical ranges, geo-locations, and more.
• Distributed: It is cloud-native and can scale horizontally, allowing developers to use just the right amount of computational resources for any amount of data they need to serve.
• Efficient: Qdrant is developed entirely in Rust, a language known for its performance and resource efficiency.

4. LangChain

LangChain is a versatile and robust framework for developing applications powered by language models. It offers several components to enable developers to build, deploy, and monitor context-aware and reasoning-based applications.

The framework consists of four main components:

• LangChain libraries: The Python and JavaScript libraries offer interfaces and integrations that allow you to develop context-aware reasoning applications.
• LangChain templates: A collection of easily deployable reference architectures covering various tasks, providing developers with pre-built solutions.
• LangServe: A library that enables developers to deploy LangChain chains as a REST API.
• LangSmith: A platform that allows you to debug, test, evaluate, and monitor chains built on any LLM framework.

Experiment Tracking and Model Metadata Management Tools

These tools allow you to manage model metadata and help with experiment tracking:

5. MLFlow

MLflow is an open-source tool that helps you manage core parts of the machine learning lifecycle. It is generally used for experiment tracking, but you can also use it for reproducibility, deployment, and model registry. You can manage machine learning experiments and model metadata using:

• CLI
• Python
• R
• Java
• REST API

MLflow has four core functions:

• MLflow tracking: Storing and accessing code, data, configuration, and results.
• MLflow projects: Packaging data science source for reproducibility.
• MLflow models: Deploying and managing machine learning models to various serving environments.
• MLflow model registry: A central model store that provides versioning, stage transitions, annotations, and model management.

6. Comet ML

Comet ML is a platform for tracking, comparing, explaining, and optimizing machine learning models and experiments. It can be used with any machine learning library, such as:

• Scikit-learn
• PyTorch
• TensorFlow
• Hugging Face

Comet ML is designed for individuals, teams, enterprises, and academics. It allows users to easily visualize and compare experiments and visualize samples from images, audio, text, and tabular data.

7. Weights & Biases

Weights & Biases is an ML platform for experiment tracking, data and model versioning, hyperparameter optimization, and model management. It also allows users to:

• Log artifacts:
  • Datasets
  • Models
  • Dependencies
  • Pipelines
  • Results
• Visualize datasets:
  • Audio
  • Visual
  • Text
  • Tabular

Weights & Biases features a user-friendly central dashboard for machine learning experiments. Like Comet ML, it integrates with other machine learning libraries, such as:

• Fastai
• Keras
• PyTorch
• Hugging Face
• YOLOv5
• Spacy and more

Note: You can also use TensorBoard, Pachyderm, DagsHub, and DVC Studio to track experiments and manage ML metadata.

Orchestration and Workflow Pipelines MLOps Tools

These tools help you create data science projects and manage machine learning workflows:

8. Prefect

Prefect is a modern data stack for monitoring, coordinating, and orchestrating workflows between and across applications. It is an open-source, lightweight tool built for end-to-end machine-learning pipelines.

You can use either Prefect Orion UI or Prefect Cloud for databases:

• Prefect Orion UI: An open-source, locally hosted orchestration engine and API server providing insights into the local Prefect Orion instance and workflows.
• Prefect Cloud: A hosted service that allows you to visualize flows, flow runs, and deployments while managing accounts, workspaces, and team collaboration.

9. Metaflow

Metaflow is a powerful, battle-hardened workflow management tool for data science and machine learning projects. It was built for data scientists so they can focus on creating models instead of worrying about MLOps engineering.

With Metaflow, you can design workflows, run them at scale, and deploy models in production. It automatically tracks and versions machine learning experiments and data. Additionally, you can visualize results in a notebook.

Metaflow works with multiple cloud providers (including AWS, GCP, and Azure) and various machine-learning Python packages (such as Scikit-learn and TensorFlow). The API is also available for the R language.

10. Kedro

Kedro is a Python-based workflow orchestration tool for creating reproducible, maintainable, modular data science projects. It integrates software engineering concepts into machine learning, such as modularity, separation of concerns, and versioning.

With Kedro, you can:

• Set up dependencies and configuration
• Set up data
• Create, visualize, and run pipelines
• Perform logging and experiment tracking
• Deploy on a single or distributed machine
• Create maintainable, modular, and reusable data science code
• Collaborate with teammates on projects

Data and Pipeline Versioning Tools

With these MLOps tools, you can manage tasks around data and pipeline versioning:

11. Pachyderm

Pachyderm automates data transformation with data versioning, lineage, and end-to-end pipelines on Kubernetes. You can integrate with any data (Images, logs, video, CSVs), any language (Python, R, SQL, C/C++), and at any scale (Petabytes of data, thousands of jobs).

The Community Edition is open-source and designed for small teams, while organizations and teams needing advanced features can opt for the Enterprise Edition.

Just like Git, you can version your data using a similar syntax. In Pachyderm, the highest level of the object is Repository, and you can use Commit, Branches, File, History, and Provenance to track and version datasets.

12. Data Version Control (DVC)

Data Version Control (DVC) is an open-source and popular tool for machine learning projects. It works seamlessly with Git to provide code, data, model, metadata, and pipeline versioning.

DVC is more than just a data-tracking and versioning tool. You can use it for:

• Experiment tracking (model metrics, parameters, versioning).
• Creating, visualizing, and running machine learning pipelines
• Workflow for deployment and collaboration
• Reproducibility
• Data and model registry
• Continuous integration and deployment (CI/CD) for machine learning using CML

Note: DagsHub can also be used for data and pipeline versioning.

13. LakeFS

LakeFS is an open-source scalable data version control tool that provides a Git-like version control interface for object storage. It enables users to manage their data lakes as they would their code. With LakeFS, users can version-control data at an exabyte scale, making it a highly scalable solution for managing large data lakes.

Additional capabilities:

• Perform Git operations like branch, commit, and merge over any storage service
• Faster development with zero-copy branching for frictionless experimentation and easy collaboration
• Use pre-commit and merge hooks for CI/CD workflows to ensure clean workflows
• A resilient platform allows for faster recovery from data issues with the revert capability

Feature Stores

Feature stores are centralized repositories for storing, versioning, managing, and serving features (processed data attributes used for training machine learning models) in production and for training purposes.

14. Feast

Feast is an open-source feature store that helps ML teams productionize real-time models and build a feature platform that fosters collaboration between engineers and data scientists.

Key features:

• Manage an offline store, a low-latency online store, and a feature server to ensure consistent feature availability for training and serving purposes
• Avoid data leakage by creating accurate point-in-time feature sets, freeing data scientists from dealing with error-prone dataset joining
• Decouple ML from data infrastructure by providing a single access layer

15. Featureform

Featureform is a virtual feature store that enables data scientists to define, manage, and serve their ML model's features. It helps data science teams enhance collaboration, organize experimentation, facilitate deployment, increase reliability, and ensure compliance.

Key features:

• Enhance collaboration by sharing, reusing, and understanding features across the team
• Orchestrated data infrastructure to ensure features are ready for production
• Increase reliability by preventing modifications to features, labels, or training sets
• Ensure compliance with built-in role-based access control, audit logs, and dynamic serving rules

Model Testing

These MLOps tools help test model quality and ensure machine learning models' reliability, robustness, and accuracy.

16. Deepchecks ML Models Testing

Deepchecks is an open-source solution that ensures thorough ML validation from research to production. It provides a holistic approach to validating data and models through three key components:

Key components:

• Deepchecks Testing: You can build custom checks and suites for tabular, NLP, and computer vision validation.
• CI & Testing Management: Helps you collaborate with your team and manage test results effectively.
• Deepchecks Monitoring: Tracks and validates models in production.

17. TruEra

TruEra is an advanced platform that drives model quality and performance through automated testing, explainability, and root cause analysis.

Key features:

• Model Testing & Debugging: Improves model quality during development and production.
• Automated & Systematic Testing: Ensures performance, stability, and fairness.
• Model Evolution Tracking: Extracts insights for faster and more effective model development.
• Bias Identification: Pinpoints specific features contributing to model bias.
• Seamless Integration: It easily integrates into your current infrastructure and workflow.

Model Deployment and Serving Tools

These MLOps tools make model deployment simple, scalable, and efficient.

Kubeflow

Kubeflow simplifies machine learning model deployment on Kubernetes, making it portable and scalable. It supports data preparation, model training, optimization, prediction serving, and performance monitoring in production.

Key features:

• A centralized dashboard with an interactive UI
• ML pipelines for reproducibility and streamlined workflows
• Native support for JupyterLab, RStudio, and Visual Studio Code
• Hyperparameter tuning and neural architecture search
• Training jobs for TensorFlow, PyTorch, PaddlePaddle, MXNet, and XGBoost
• Job scheduling for managing workflows
• Multi-user isolation for administrators
• Works with all major cloud providers

18. BentoML

BentoML is a Python-first deployment tool for fast and scalable machine learning applications. It provides parallel inference, adaptive batching, and hardware acceleration for optimized performance.

Key features:

• Interactive centralized dashboard for organizing and monitoring ML models
• Supports all major ML frameworks, including:
  • Keras
  • ONNX
  • LightGBM
  • PyTorch
  • Scikit-learn
• Enables model deployment, serving, and monitoring in a single solution

19. Hugging Face Inference Endpoints

Hugging Face Inference Endpoints is a cloud-based deployment service that allows users to deploy trained ML models for inference without managing infrastructure.

Key features:

• Cost-efficient pricing starts at $0.06 per CPU core/hr and $0.6 per GPU/hr
• Quick deployment within seconds
• Fully managed with autoscaling capabilities
• Integrated into the Hugging Face ecosystem
• Provides enterprise-level security

Model Monitoring in Production ML Ops Tools

Whether your ML model is in development, validation, or deployment to production, these tools can help you monitor various factors.

20. Evidently

AI is an open-source Python library monitoring ML models during development, validation, and production. It checks data and model quality, data drift, target drift, and regression and classification performance.

Has three main components:

• Tests (Batch Model Checks): Perform structured data and model quality checks
• Reports (Interactive Dashboards): Provide interactive visualizations for data drift, model performance, and target monitoring
• Monitors (Real-Time Monitoring): Track data and model metrics from deployed ML services

21. Fiddler

Fiddler AI is an ML model monitoring tool with an easy-to-use, straightforward UI. It lets you explain and debug predictions, analyze model behavior for the entire dataset, deploy machine learning models at scale, and monitor model performance.

Key features:

• Performance Monitoring: In-depth visualization of data drift, including when and how it’s happening
• Data Integrity: Prevent incorrect data from being used for model training
• Tracking Outliers: Detect both univariate and multivariate outliers
• Service Metrics: Gain insights into ML service operations
• Alerts: Set up notifications for models or groups of models to detect issues in production

Runtime Engines

The runtime engine is responsible for loading the model, preprocessing input data, running inference, and returning the results to the client application.

22. Ray

Ray is a versatile framework designed to scale AI and Python applications, making it easier for developers to manage and optimize their machine learning projects.

The platform consists of two main components:

• Core Distributed Runtime: Provides fundamental elements for constructing and expanding distributed applications
  • Tasks: Stateless functions executed within the cluster
  • Actors: Stateful worker processes created within the cluster
  • Objects: Immutable values accessible by any component within the cluster
• AI Libraries: Includes scalable datasets for ML, distributed training, hyperparameter tuning, reinforcement learning, and scalable serving

23. Nuclio

Nuclio is a robust framework focused on data, I/O, and compute-intensive workloads. It is designed to be serverless, meaning you don't need to worry about managing servers. Nuclio is well-integrated with popular data science tools, such as Jupyter and KFP.

It also supports various data and streaming sources and can be executed over CPUs and GPUs.

Key features:

• Requires minimal CPU/GPU and I/O resources to perform real-time processing while maximizing parallelism
• Integrates with a wide range of data sources and ML frameworks
• Provides stateful functions with data-path acceleration
• Offers portability across all kinds of devices and cloud platforms, especially low-power devices
• Designed for enterprise use

End-to-End MLOps Platforms

If you’re looking for a comprehensive MLOps tool that can help during the entire process, here are some of the best:

24. AWS SageMaker

Amazon Web Services SageMaker is a one-stop solution for MLOps. It allows you to train and accelerate model development, track and version experiments, catalog ML artifacts, integrate CI/CD ML pipelines, and deploy, serve, and monitor models in production seamlessly.

Key features:

• A collaborative environment for data science teams
• Automate ML training workflows
• Deploy and manage models in production
• Track and maintain model versions
• CI/CD for automatic integration and deployment
• Continuous monitoring and retraining models to maintain quality
• Optimize cost and performance

25. DagsHub

DagsHub is a platform for the machine learning community to track and version data, models, experiments, ML pipelines, and code. It allows your team to build, review, and share machine learning projects.

It is a GitHub for machine learning, providing various tools to optimize the end-to-end ML process.

Key features:

• Git and DVC repository for ML projects
• DagsHub logger and MLflow instance for experiment tracking
• Dataset annotation using a Label Studio instance
• Diffing Jupyter notebooks, code, datasets, and images
• Ability to comment on files, code lines, or datasets
• Create project reports similar to GitHub wikis
• ML pipeline visualization
• Reproducible results
• CI/CD for model training and deployment
• Data merging
• Integration with GitHub, Google Colab, DVC, Jenkins, external storage, webhooks, and New Relic

26. Iguazio MLOps Platform

The Iguazio MLOps Platform is an end-to-end MLOps solution that enables organizations to automate the machine learning pipeline from data collection and preparation to training, deployment, and monitoring in production. It provides an open (MLRun) and managed platform.

A key differentiator is its flexibility in deployment options. It allows AI applications to be deployed on any cloud, hybrid, or on-premises environment—particularly beneficial for industries like healthcare and finance, where data privacy is crucial.

Key features:

• Ingest data from any source and build reusable features using an integrated feature store
• Continuous training and evaluation of models at scale with automated tracking, data versioning, and CI/CD
• Deploy models to production in just a few clicks
• Constant monitoring of model performance and mitigation of model drift
• Simple dashboard for managing, governing, and monitoring models in real time

27. TrueFoundry

TrueFoundry is a cloud-native ML training and deployment PaaS built on Kubernetes. ML teams can train and deploy models efficiently without requiring deep Kubernetes expertise.

Key features:

• Jupyter Notebooks: Start Notebooks or VSCode Server on the cloud with auto shutdown
• Batch or inference jobs: Write Python scripts, log metrics, models, and artifacts, and trigger jobs manually or on a schedule
• Model deployment as APIs: Deploy model artifacts as APIs or wrap them in FastAPI, Flask, etc., with built-in autoscaling and canary deployments
• Easy debugging: View logs, metrics, and cost insights for all services
• Model registry: Tracks all models and versions, deployment status, and metadata
• LLM fine-tuning: Deploy open-source LLMs with one click and fine-tune on custom data
• ML software deployment: Deploy commonly used ML tools like LabelStudio and Helm Charts
• Multi-environment management: Manage multiple Kubernetes clusters and move workloads seamlessly

28. Airflow

Airflow is a mature, open-source workflow orchestration platform for managing data pipelines and various tasks. It is written in Python and provides a user-friendly web UI and CLI for defining and managing workflows.

Key features:

• Generic workflow management: Not limited to ML; handles data processing, ETL, and model training workflows
• DAGs (Directed Acyclic Graphs): Defines workflows with tasks and dependencies
• Scalability: Supports running workflows across a cluster of machines
• Large community: Offers extensive documentation and resources
• Flexibility: Integrates with multiple data sources, databases, and cloud platforms

29. Anaconda

Anaconda is a comprehensive MLOps, data science, and AI platform that provides everything needed for efficient model development and deployment.

Features and capabilities:

• All-in-one solution: Includes features and integrations covering most aspects of data workflows
• Open-Source vs. Proprietary: Offers an open-source core with enterprise options
• Security: Provides robust package verification and a secure repository.
• Integrations: Works with various IDEs, cloud platforms, and data science tools
• Scalability: Suitable for individual users and large enterprises

30. Optuna

Optuna is an open-source automatic hyperparameter optimization framework designed for ML experts.

Key features:

• Uses state-of-the-art algorithms to prune unpromising samples quickly
• Automatically searches for optimal hyperparameters using Python-based implementations
• Supports parallelized hyperparameter searches across multiple threads without modifying the original code

31. SigOpt

SigOpt is a model-building and optimization tool that simplifies tracing runs, visualizing training, and scaling hyperparameter tuning across models and libraries.

Key features:

• Interactive Dashboard: This allows users to assess various models based on statistical metrics (F1 Score, accuracy, etc.)
• Version Control: Enables users to compare past models and tweak hyperparameters for better results
• Minimal Code: Runs and compares different models with just a few lines of code

Related Reading

• AI Infrastructure
• AI as a Service
• Machine Learning Inference
• Artificial Intelligence Cost Estimation
• AutoML Companies
• Edge Inference
• LLM Inference Optimization

Start building with $10 in Free API Credits Today!

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

Related Reading

• LLM Serving
• LLM Platforms
• Inference Cost
• Machine Learning at Scale
• TensorRT
• SageMaker Inference
• SageMaker Inference Pricing