Best MLOps Tools: What to Look for and How to Evaluate Them

Aug 30, 2022

15 min read

Before diving straight into MLOps tools, you need to understand what MLOps is, the workflow involved in a MLOps project, the various algorithms involved, and how one can take their model into deployment.

MLOps is the process of executing a machine learning project successfully. MLOps is not anything like Devops because DevOps is a linear action; it pushes code to production. Whereas MLOps is a loop action that does not end with the deployment but continues towards retraining and redeployment.

The various steps involved in a machine learning project:

1. Data Collection

2. Data Pre-processing

3. Building Datasets

4. Model Training or Selection

5. Model Deployment

6. Prediction

7. Monitoring Models

8. Maintenance, Diagnosis, and Retraining

The above is the standard steps involved in any machine learning project. We have divided the steps into the following stages, and we will talk in detail about MLOps tools stacks available in each stage.

1. Machine learning frameworks

2. Distributed compute

3. Model evaluation and experiment tracking

4. Model deployment

5. Model monitoring and management

6. End-to-end platform solutions

What are MLOps tools?

By definition, MLOps tools are single-use software or end-to-end platform that helps you execute a stage or an entire machine learning project. All the MLOps tools serve a particular purpose, but if you look at the bigger picture, they collectively work towards solving a real-world problem through data science.

Your tool stack should reduce the time you spend figuring it out and increase your time in solving a business problem. Put together tools that help you in each stage of the ML workflow, and you have a MLOps tool stack that streamlines your MLOps pipeline and makes pushing your models into production quicker and easier.

How to select the right MLOps tools?

No matter how many demos you sit down to, numerous hours contemplating the right fit won’t be sufficient for you to decide whether the tool will serve its purpose. Fortunately, there are ways you can decide whether the tool will be a right fit for your pipeline.

Some considerations before choosing a MLOps tool, it should not lock you in a single platform; you should be able to expand as necessary or cut down when needed. It must be cloud-agnostic; this is a given no argument in that. Finally, the MLOps tools you’re looking for should include various libraries and support multiple languages.

Tools for MLOps that you need to know about

You can use several MLOps tools in various stages of your machine learning project workflow. We have divided them into six groups based on the workflow stage they cater to.

In this article, we will go through various stages in the MLOps pipeline and the best MLOps tools available in each step of the pipeline. We will also go through the features, screenshots, and what makes the tool stand out from the rest.

Machine Learning Frameworks

The first stage of any machine learning project is deciding on the framework we will use. ML frameworks let data scientists and developers build and deploy models faster. Let us take a look at some of the best MLOps tools available to us in this phase.

Hugging Face

Hugging Face is an open-source machine learning framework focusing on AI ethics and easy-to-deploy tools. Clément Delangue and Julien Chaumond founded Hugging Face in 2016 as a chatbot company. Hugging Face is offered in two categories, one as an open-source platform and the other as a subscription-based NLP feature.

Hugging Face is famous for a couple of reasons:

  • Community-driven through huge open source repositories and paid services.

  • Attractive pricing, their features start as low as $9.

Some of the noteworthy features of Hugging Face are:

  • Community and the sheer volume of models mainly around audio, vision, and language.

  • Transformers, the natural language processing library, come with support from Flair, Asteroid, ESPnet, and Pyanote.

  • Huge research contributions to democratize NLP.


PyTorch was created inside the Facebook research lab by Facebook AI Research in 2017. Since then, it has become quite popular with data scientists and machine learning engineers because of its flexibility and speed.

PyTorch is a deep learning tensor library built on Python and Torch. It uses Pythonic, a dynamic computation graph that allows us to run codes in real-time.

Features of PyTorch are:

  • Production-ready capabilities, seamless transition between eager and graph modes, and faster production time.

  • TorchServe to deploy models at scale, with cloud-agnostic environments and support features.

  • Automatic differentiation for deep neural networks.


The Google Brain team developed TensorFlow in 2015. It is an open-source framework for mathematical computation, making machine learning and developing neural networks faster. TensorFlow uses python to provide an API for building applications. For example, TensorFlow allows developers to create graphs that would enable them to see how data moves through the graph, called data graphs.

Features of TensorFlow:

  • TensorFlow takes care of the background process while developers can focus on the logic.

  • TensorFlow makes debugging more straightforward, and every graph can be evaluated and modified separately.

  • Powerful visualization, TensorBoard lets developers visualize graphs in an interactive and web-based dashboard.


Grid.ai is a framework that lets data scientists train models on the cloud at scale. Founded by William Falcon and Luis Capelo in 2019, it enables people without ML engineering or MLOps experience to develop ML models.

Features of Grid.Ai:

  • Grid run enables users to scale their model without additional coding and supports all major ML frameworks.

  • Datastores that let you train models on vast datasets from the cloud, like accessing them on your local computer.

  • Preconfigured environments with JupyterHub, Integrated with GitHub and accessible through SSH.

Distributed Compute

Distributed computing is multiple nodes of machine learning algorithms and systems that improve performance, increase accuracy, and scale with large datasets. First, look at some of the top MLOps tools available in this category.


Anyscale is a fully scalable distributed computing platform that makes it easier for anyone to build, deploy, and manage scalable ML applications on Ray. It is a framework for building machine learning frameworks and has two sets of libraries, one for new workloads and the second for replacing existing libraries.

Features that make anyscale standout:

  • Effortless scaling, anyscale lets us scale faster from the local computer to any server with zero code changes.

  • You can develop, test, and productionize in one framework that supports data loading, training, tuning, learning, and serving.

  • It lets you run on any cloud, Kubernetes, or laptop by integrating with any ML library, data platform, and orchestrators.


Coiled lets you quickly scale your Python applications by making them cloud agnostic. Founded in 2020 by Matthew Rocklin, Hugo Bowne-Anderson, and Rami Chowdhury, Coiled has recently announced public availability in the DASK distributed summit. Their DASK projects vary from machine learning pipelines to demand forecasting and modeling.

Prominent features of Coiled:

  • Scale Python applications to the cloud effortlessly and with one click.

  • Controls cluster usage, end-to-end network security in multi-cloud instances, and built-in credential management.

  • Deploy on Kubernetes or move workloads to the cloud.


Dask is an open-source python library for parallel computing that scales python applications from local systems to large distributed clusters in the cloud. Dask also makes it easier to work with Numpy, Pandas, and Scikit-learn. Also, it is a framework used to build distributed applications with systems like XGBoost, PyTorch, Prefect, Airflow, and Rapids.

Critical features of DASK:

  • Ease of use which requires no time to configure or set up.

  • DASK data framework enables us to parallelize large datasets to operate beyond storage capabilities.

  • DASK arrays and DASK ML help us to execute NumPy and SciKit-Learn in parallel.

  • DASK collections provide the API to write DASK codes, the DASK scheduler manages the workflow, and workers compute the task.

Apache Spark

Apache Spark is a MLOps tool that is used for data processing that can process large amounts of datasets faster and more efficiently than Hadoop. In addition, it can also distribute data processing to multiple systems on its own or using distributed computing tools.

Founded in AMPLab at U.C. Berkeley in 2009, Apache Spark has been the go-to framework for processing big data. Apache Spark supports SQL and graph processing and binds with Java, Scala, Python, and R programming languages.

Here’s why Apache Spark shines over the rest:

  • Speed, even though Hadoop includes Spark in most distributions, Spark can perform tasks 100 times faster than Hadoop.

  • Developer-friendly API. What takes more than 50 lines of code in other systems takes less than ten lines of code in Spark.

  • Apache Spark’s RDD (Resilient Distributed Dataset) enables us to batch process, leading to fast and scalable parallel processing.

Model Evaluation and Experiment Tracking

Model evaluation and experiment tracking in machine learning are tracking and saving all the information regarding the experiments you have added to your training. In addition, model evaluation and experiment tracking help us to track the model performance, compare versions and select the ideal version for deployment.

Let us go over some of the top MLOps tools available in this segment.

Weights & Biases

Weights & Biases, also known as W&B, is an open-source MLOps tool for performance tracking and visualization in a machine learning project. They organize and analyze your experiments and save the model’s hyperparameters and metrics. They also provide training, model comparisons, and accuracy tracking visualization charts.

Features of W&B:

  • Centralized, user-friendly dashboard to visualize and track experiments and their performance.

  • Sweeps for automated hyperparameter tuning.

  • Collaborative reports for teams working together.

  • End-to-end performance tracking of the whole MLOps pipeline.

Comet ML

Comet ML is a MLOps tool founded by Gideon Mendels in 2017, used to track, organize, compare, and visualize the performance of experiments in a machine learning project. They also help us keep track of performance history, code changes, and production models. Comet ML is also moving towards an automated ML approach by adding predictive early stopping and neural architecture search.

Promising features of Comet ML:

  • You can start tracking your experiments with easy integration with any training environment with two lines of code.

  • Track and share results in real-time with fellow data scientists.

  • Build your own tailored, interactive visualization, it also has 30+ built-in visualizations, and you can create your visualization using Plotly and Matplotlib.

  • Model monitoring in production, Comet ML allows you to track data drifts and the performance of your models in production.


Iterative.ai is a git-based MLOps tool for data scientists and ML engineers with DVC (data version control) and CML (continuous machine learning). Iterative.ai was created by Dmitry Petrov while working as a Microsoft data scientist, aiming to bring engineering practices to data science and machine learning.

Key features of iterative.ai are:

  • DVC offers a single view history of changes made in data, source code, and ML models. This also enables us to track how the project evolved, reproduce without retraining, and share the project as a whole.

  • Metafiles allow the system to store huge volumes of data using metadata created by users instead of storing them in Git.

  • CML enables us to automate the machine learning workflows, including training and evaluation stages.

  • CML also lets us run reports comparing model versions, spot changes, and monitor changing datasets.


MLflow is an open source platform built on an open interface philosophy helping us manage certain aspects of the machine learning workflow. So, any data scientists working with any framework, supported or unsupported, can use the open interface, integrate with the platform, and start working.

There are four main features in MLflow:

  • MLflow tracking lets us record model training sessions (runs) and run queries on Python, Java, R, and Rest APIs.

  • MLflow projects package data science codes in a format that allows us to reproduce runs on any platform.

  • MLflow models provide a standard package for reusing machine learning models across different environments.

  • MLflow model registry will enable us to manage the lifecycle of the model.

Machine Learning Model Deployment

Model deployment in machine learning is the stage where we deploy our trained models into production. This enables the model to serve its purpose of predicting results as it was intended to do so. For a complete guide to model deployment, you can read our blog here.

Now, we look into various MLOps tools for model deployment in machine learning.


Creators of Apache TVM spun out of the University of Washington and created OctoML to help companies to develop and deploy deep learning models in specific hardware as needed. OctoML supports a variety of machine learning frameworks, such as PyTorch and TensorFlow.

Features of OctoML are:

  • OctoML supports common machine learning frameworks such as PyTorch and TensorFlow.

  • Scalable and deployable on any hardware, cloud, or device.

  • Model comparison and benchmarking to optimize model performance.


BentoML is an end-to-end platform solution for model serving. It gives the data scientist the power to develop production-ready models, with best practices from DevOps and optimization at each stage. Furthermore, its standard and easy architecture simplify the building of production-ready models.

Key features of BentoML are:

  • A modular framework supports multiple machine learning frameworks such as PyTorch, TensorFlow, Keras, and XGboost.

  • Its unified model packaging format lets us manage models better in model serving and packaging.

  • BentoML is capable of serving high-performance API serving both online and offline.

  • Brings best practices from DevOps, ensuring high-quality prediction and integration with common infrastructures.


Seldon is an open-source platform that helps data scientists and ML engineers to solve problems faster and effectively through audit trails, advanced experiments, CI/CD, scaling, model updates, and more. In addition, Seldon converts ML models or language wrappers into containerized product microservices.

Prominent features of Seldon:

  • Custom servers, language wrappers, and pre-packed inference servers to containerize ML models.

  • Predictors, transformers, routers, combiners, and more for interference graphs.

  • Elasticsearch integration for input-output request logging.

  • Microservice tracing using Jaeger.


Wallaroo is a MLOps tool that helps in model deployment. The platform consists of four main components, MLOps, process engine, data connectors, and audit and performance reports. Wallaroo allows data scientists to deploy models against live data to testing, staging, and production using machine learning frameworks.

Four main features of Wallaroo:

  • Deploying machine learning models with a single line of Python code or through APIs.

  • Scalability of the machine learning models with reduced cost.

  • Monitor model performance to identify loss of performance or data drifts in real time.

  • Retraining and optimizing live models on the go with insights.

Model Monitoring and Management

Post-deployment, model monitoring, and management play a vital role in the MLOps pipeline. However, the reality from test data to actual data is a vast difference, and data drifts and performance degradation are common. This is where MLOps differs from DevOps; model monitoring is a huge task, and fortunately, MLOps tools are available to solve this problem.


Arize is leading the model performance monitoring space. It’s a full-stack platform designed to solve daily pain points and bottlenecks faced by data scientists and ML engineers. Arize detects errors and data drifts when they appear, analyzes why the error occurred, and improves the model’s overall performance.

Features of Arize are:

  • Identify model drift through monitoring, accurately find data drifts across dimensions, and compare different data sets.

  • Tight data integrity through rigorous checks on missing, unexpected, and random data.

  • It helps us to find how model dimensions affect prediction by leveraging SHAP and find feature importance for specific use cases.

Arthur AI

Arthur AI is a machine learning performance platform that monitors model performance, bias detection, and explainability. The platform enables data scientists, ML engineers, and developers to detect errors, data drift, and anomalies.

Features of Arthur AI:

  • Setup and implementation with just a few lines of code.

  • Platform agnostic fits right into your workflow and has a unified dashboard.

  • Monitor models at one instance, collaborate with stakeholders on the platform and set up alerts to detect data drifts as it happens.

Fiddler AI

Fiddler AI is a model performance management platform that gives us a common language, centralized controls, and actionable insights into the performance of the models. The platform also auto-generates valuable real-time insights into incidents and enables users to perform a complete analysis, including bias and fairness.

Features of Fiddler AI:

  • Powered XAI, you can detect data drifts and find the root cause of the data drifts.

  • Check data violations such as missing values, mismatches, and errors in real time.

  • Quick view of the performance and accuracy of models.

  • Query-based analytics to dive deeper into specific scenarios to validate models.


WhyLabs is the conjunction of data observability and MLOps into a single platform. WhyLabs aims to reduce the time spent on error identification and solving. “The goal of the company is to first build a data observability platform for data and machine learning,” says Andy Dang, head of engineering and co-founder of Whylabs.

Key features of WhyLabs are:

  • There are two offerings; the open source offering called WhyLogs and the enterprise offering that offers a long-term store and data visualization.

  • The open source works by creating a data profile of the data in your machine learning environment and detects data drifts.

  • The enterprise version deploys the concept of ‘recipes’ that offers simplified deployment, collaboration, visualization, monitoring, and integration tools.

End-to-End Platforms

These platforms offer a comprehensive solution covering the entire machine learning pipeline spectrum. In addition, these platforms provide a one-stop solution for all, from data and pipeline, model experimentations, and hyperparameter tuning, to deployment and monitoring.


NimbleBox.ai is a complete MLOps platform that enables data scientists and ML engineers to build, deploy, and allocate jobs to their machine learning projects. The four core components of NimbleBox are their Build, Jobs, Deploy and Manage components. These features let anyone start their machine learning project with just a few lines of code and push their model deployment in the easiest ways.

Features of NimbleBox.ai are:

  • Build from NimbleBox lets you build your ML project from scratch with your favorite IDE. It also enables you to scale your hardware based on your requirements. And has a built-in auto-shutdown feature that helps you save costs.

  • Jobs lets you schedule jobs to the model, automates most tedious tasks, and integrates seamlessly with the rest of your tool stack.

  • Deploy enables you to push your models into production and monitors models 24/7 to detect data drifts and performance degradation. Plus, you can deploy with any framework without any latency.

  • Manage lets you manage the roles and permissions, gives you an overview of the entire project running and allows you to control costs better and drive results.

Domino Data Lab

Domino Data Lab is a fully-fledged MLOps platform that enables data scientists and ML engineers to develop and deploy machine learning models focusing on data governance and collaboration.

Features of Domino Data Lab are:

  • System of record, Domino has a centralized repository that captures all the work done by data scientists. This allows us to reuse code, artifacts, experiments, and learnings from previous projects.

  • The integrated model factory from Domino supports the entire lifecycle of a machine learning project. This helps in repeatable workflows and processes that put models straight into production. Allows business to be more model driven by increasing model velocity.

  • Self-serve infrastructure, Domino automates the tedious DevOps tasks at scale. It lets you set up a sandbox with loaded frameworks, tools, and languages, enabling you to work immediately. This allows you to jump between environments, add more data, compare, and iterate more often.


dataBricks is a big data processing platform that integrates data science, engineering, and business across the machine learning project lifecycle. The creators of Apache Spark founded it as an alternative to the MapReduce system. DataBricks accelerates development by unifying the pipelines involved in the development process.

Features of DataBricks are:

  • DataBricks supports multiple coding languages in the same environment. You can build on Python, R, or Scala using magical commands.

  • Deploy notebooks into production environments instantly. Collaborating with fellow developers and data scientists can decrease the time to push models into production.

  • You can run both small and large-scale jobs on DataBricks. It is built on Apache Spark, which gives us the advantage of being flexible depending on the scale of the job.

  • DataBricks has limitless power when it comes to big data. It connects with all the cloud storage, such as AWS, Azure, and Google, and on-prem solutions, such as SQL, CSV, and JSON.


DataRobot is a MLOps platform used to build and deploy machine learning models. They also provide a built-in library of algorithms and prebuilt prototypes for feature extraction and data prep. It also offers automation in feature selection, algorithms, and parameter values.

Features of DataRobot are:

  • An intuitive user interface allows users to analyze prepped data, train selected algorithms, optimize algorithms, and deploy predictive models.

  • DataRobot also offers a collaborative platform that lets us share machine learning projects, reducing the time to start a project. It also creates a repository of projects and their steps for reuse.

  • DataRobot automates mundane tasks, reduces errors, and increases productivity in predictive analytics.


ZenML is an open-source MLOps framework tool that reproduces machine learning pipelines and gives us a production-ready MLOps tool. Two things make ZenML stand out from the rest, a very good Python library and third-party integrations. ZenML’s python library helps data scientists to kickstart their MLOps faster, and their integration lets them do everything from anywhere.

Features of ZenML:

  • Reproducibility, we can reproduce the ML pipeline as often as we want. As long as it is written clearly and modular, ZenML lets us reproduce by adding a particular PiP requirement file.

  • We can run our machine learning pipeline from any system, and ZenML will always do the same thing and produce the same model. With ZenML, you can run your MLOps in any environment.

  • As we mentioned above, one of the strong points of ZenML is its integration with third-party tools.


No matter your requirement in MLOps, there are tools for MLOps for every need. These tools enable data scientists and engineers to develop, train, deploy, and monitor models from machine learning frameworks to entire platforms.

In an era where we still debate the importance of developer tools and whether we can build our ML pipeline in-house, these tools certainly give us an advantage in delivering what is asked of us. These indeed are exciting times to be a data scientist.

Written By

Thinesh Sridhar

Technical Content Writer

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