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The Best MLOps Tools You Need to Know as a Data Scientist!

This article was published as a part of the Data Science Blogathon


According to Data Scientists, there are several ingredients for an entire MLOps system:

  • You need to be ready to build model artifacts that contain all the knowledge needed to preprocess your data and generate a result.

  • Once you’ll build model artifacts, you’ve got to be ready to track the code that builds them, and therefore the data they were trained and tested on.

  • The models, their code, and their data and related to each other and you have to track how all three of those things are related.

  • Once you’ll track these things, you’ll also mark them ready for staging, and production, and run them through a CI/CD process.

  • Finally, to truly deploy them at the top of that process, you would like how to spin up a service supported that model artifact.

We’ve compiled a listing of the most effective MLOps tools. We’ve divided them into six categories so you’ll choose the correct tools for your team and your business. Let’s dig in!

Table of contents

1. Data and pipeline versioning

2. Run orchestration

3. Experiment tracking and organization

4. Hyperparameter tuning

5. Model serving

6. Production model monitoring

Data and pipeline versioning

1. DVC

  • DVC, or Data Version Control, is an open-source version system for machine learning projects. It’s an experimentation tool that helps you define your pipeline no matter the language you utilize. by leveraging code, data versioning, and reproducibility, DVC helps to save lots of time, in the instance, you find controversy in an exceedingly previous version of your ML model. You’ll be able to also train your model and share it along with your teammates via DVC pipelines. DVC can address the versioning and organization of huge amounts of information and store them in an exceedingly well-organized, accessible way. It emphasizes data and pipeline versioning and management.

          DVC – summary:

  • Possibility to use differing kinds of storage— it’s storage agnostic

  • Full code and data provenance help to trace the entire evolution of each ML model

  • Reproducibility by consistently maintaining a mixture of the input file, configuration, and therefore the code that was initially accustomed to run an experiment

  • Tracking metrics

  • A built-in thanks to connecting ML steps into a DAG and run the total pipeline end-to-end


2. Pachyderm

Pachyderm may be a platform that mixes data lineage with end-to-end pipelines on Kubernetes.

The three available versions are:

1. Community Edition – an open-source, with the ability to be used anywhere

2.Enterprise Edition -complete version-controlled platform

3. Hub Edition – it combines characteristics of the two previous versions.

You need to integrate Pachyderm with your infrastructure/private cloud. Since during this section we are talking about data and pipeline versioning we’ll mention the 2 but there’s more to Pachyderm than simply that (check out the website for more info).

In the section on data versioning, the main concepts Pachyderm data versioning system includes are:

Repository – a Pachyderm repository is that the highest level data object.

Strictly, each dataset in Pachyderm is its own repository

Commit – an immutable snapshot of the repo at a specific point in time

Branch – an alias to a selected commit, or a pointer, that automatically moves as new data is submitted

File – The actual data in your repository is files and directories.

Pachyderm supports any type, size, and variety of files Provenance – expresses the connection between various commits, branches, and repositories. It helps you to trace the origin of every commit


3. Kubeflow

Kubeflow is the ML toolkit for Kubernetes. By packaging and managing docker containers, it helps in the maintenance of machine learning systems. While making run orchestration, it facilitates the scaling of machine learning models and deployments of machine learning workflows easier.

Kubeflow Pipelines is out there as a core component of Kubeflow or as a standalone installation

Run orchestration

1. Kubeflow

As you’ve noticed, we’ve already mentioned Kubeflow in data and pipeline versioning, but the tool also can be helpful in other areas, also orchestration.

You can use Kubeflow Pipelines to overcome long ML training jobs, manual experimentation, reproducibility, and DevOps obstacles.

With Kubeflow’s tools and frameworks, it’s easier to orchestrate your experiments.

2. Polyaxon

Polyaxon may be a platform for reproducing and managing the entire life cycle of machine learning projects also as deep learning applications.

The tool is often deployed into any data center, cloud provider, and may be hosted and managed by Polyaxon. It supports all the main deep learning frameworks, e.g., Torch, Tensorflow, MXNet.

When it comes to orchestration, the usage of your cluster is maximized by Polyaxon through jobs scheduling and experiments via their CLI, dashboard, SDKs, or REST API.

Polyaxon – summary:

  • Supports the whole lifecycle including run orchestration but can do far more than that

  • Allows to watch, track, and analyze every single optimization experiment with the experiment insights dashboard


3. Airflow

airflow | Best MLOps ToolsSource

Airflow is an open-source platform that permits you to watch, schedule, and manage your workflows using the online application. It provides an insight into the status of completed and ongoing tasks alongside an insight into the logs.

To manage workflow orchestration, Airflow uses directed acyclic graphs (DAGs) or simply topological sort. The tool is written in Python but you’ll use it with the other language.


  • Easy to use together with your current infrastructure—integrates with Google Cloud Platform, Amazon Web Services, Microsoft Azure, and lots of other services

  • You can visualize pipelines running in production

  • It can assist you to manage different dependencies between tasks

Experiment tracking and organization

1. Neptune

Neptune could be a metadata store that was built for research and production teams that run many experiments.

It’s composed of three major components:

Data versioning

Experiment tracking

Model registry

These components allow Neptune to function as a connector between different parts of the MLOps workflow. the most essential goal is to make a centralized place for all machine life-cycle metadata and make it pretty easier for teams to store, organize, display, track the lineage, share and compare all metadata generated during model development. Furthermore, Neptune is incredibly flexible, works with many other frameworks, and because of its stable computer program, it enables great scalability (to uncountable runs).

Finally, being a strong software, Neptune facilitates efficient team collaboration and project supervision still as allows to store, retrieve, and analyze an outsized amount of information.

Neptune – summary:

  • Fast and exquisite UI with lots of capabilities to arrange runs in groups, save custom dashboard views, and share them with the team

  • You can use a hosted app to avoid all the effort of maintaining yet one more tool (or have it deployed on your on-prem infrastructure)

  • Your team can track experiments that are executed in scripts (Python, R, other), notebooks (local, Google Colab, AWS SageMaker), and do this on any infrastructure (cloud, laptop, cluster)

  • Provides individuals and teams with notebook checkpointing and model registry to trace model version and lineage.

2. MLflow

MLflow is an open-source platform that helps manage the full machine learning lifecycle that has experimentation, reproducibility, deployment, and a central model registry. MLflow is suitable for people and teams of any size. The tool is library-agnostic. you’ll use it with any machine learning library and in any programing language. MLflow comprises four main functions that help to trace and organize experiments:

  1. MLflow Tracking – when running machine learning code it acts as an API and UI for code versions, logging parameters, metrics, and artifacts, and for later comparing and visualizing the results

  2. MLflow Projects –to share with other data scientists or transfer to production it perform the packaging of ML code during a reusable, reproducible form

  3. MLflow Models – managing and deploying models from different ML libraries to a spread of model serving and inference platforms

  4. MLflow Model Registry – a central model store to collaboratively manage the complete lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations


3. Comet

Comet may be a meta machine learning platform for tracking, comparing, explaining, and optimizing experiments and models. It allows you to look at and compare all of your experiments in one place.

Wherever you run your code with any machine learning library it works very well. Comet is suitable for teams, individuals, academics, organizations, and anyone who wants to simply visualize experiments and facilitate work and run experiments.

Some of the Comet most notable features include:

  • Sharing add a team: multiple features for sharing in an exceeding team

  • Works well with existing ML libraries

  • Deals with user management

  • Has a bunch of Integrations to attach it to other tools easily

Hyperparameter tuning

1. Optuna

Optuna is an automatic hyperparameter optimization framework that will be used both for machine learning/deep learning and in other domains. it’s a set of state-of-the-art algorithms that you just can choose (or connect to), it’s very easy to distribute training to multiple machines, and enables you to visualize your results nicely. It integrates with popular machine learning libraries like TensorFlow, FastAI, Keras, scikit-learn, LightGBM, PyTorch, and XGBoost.

2. Sigopt

The aim of SigOpt is to accelerate and amplify the impact of deep learning, machine learning, and simulation models. It helps to avoid wasting time by automating processes which makes it an appropriate tool for hyperparameter tuning.

You can integrate SigOpt seamlessly into any model, framework, or platform without concern about your data, model, and infrastructure – everything’s secure. The tool also allows you to monitor, track, and analyze your optimization experiments moreover as visualize them.

High Parallelism enables you to totally leverage large-scale computer infrastructure and runs optimization experiments across up to at least one hundred workers

Model serving

1. Kubeflow

Kubeflow appears several times in our article and that’s because its components allow you to manage almost every aspect of your ML experiments.

It’s quite a flexible solution that provides you space to flexibly manipulate your data and serve models the way you would like to.

2. Cortex

cortex | Best MLOps ToolsSource

Cortex is an open-source alternative to building your own model deployment platform on top of AWS services such as Elastic Kubernetes Service (EKS), Lambda, or Fargate and open source projects like Docker, TensorFlow Serving, Kubernetes, and TorchServe or to serve models with SageMaker.

It’s a multi framework tool that permits you to deploy every type of model.

Cortex – summary:

  • Automatically scale APIs to handle production workloads

  • Run inference on an AWS instance type

  • Deploy multiple models in a very single API and update deployed APIs without downtime

  • Monitor API performance and prediction results

3. Seldon

Seldon is an open-source platform that permits you to deploy machine learning models on Kubernetes. It’s available within the cloud and on-premise.

Seldon – summary:

  • Monitor models in production with the alerting system when things fail

  • Use model explainers to grasp why certain predictions were made. Seldon also open-sourced a model explainer package alibi

Production model monitoring

1. Amazon SageMaker Model Monitor

Amazon SageMaker Model Monitor is a component of the Amazon SageMaker platform that permits data scientists to create, train, and deploy machine learning models. When it involves Amazon SageMaker Model Monitor, it helps you to automatically monitor machine learning models in production and alerts you whenever data quality issues appear. The tool helps to avoid wasting time and resources so you and your team can specialize in the results.

Amazon SageMaker Model Monitor—summary:

  • Use the tool on any endpoint— when the model was trained with a built-in algorithm, a built-in framework, or your own container

  • With the SageMaker SDK, you’ll be able to capture predictions or a configurable fraction of the information sent to the endpoint and store it in one of your Amazon Simple Storage Service (S3) buckets. Captured data is enriched with metadata, and you’ll be able to secure and access is similar to an S3 object.

  • Launch a monitoring schedule and receive reports that contain statistics and schema information on the info received during the most recent time frame, and any violation that was detected

2. Hydrosphere

The hydrosphere is an open-source platform for managing ML models. Hydrosphere Monitoring is its module that enables you to watch your production machine learning in real-time. It uses different statistical and machine learning methods to test whether your production distribution matches the training one. It supports external infrastructure by allowing you to attach models hosted outside Hydrosphere to Hydrosphere Monitoring to watch their quality.

3. Cortex

We’ve already mentioned Cortex within the Model Serving section but since it’s a multi framework tool, you’ll flexibly use it for other purposes, also to observe your model. And along with the model serving feature, it gives you full control over your models.

It’s a multiframework tool that enables you to deploy every kind of model.

Cortex – summary:

  • Automatically scale APIs to handle production workloads

  • Run inference on an AWS instance type

  • Deploy multiple models during a single API and update deployed APIs without downtime

  • Monitor API performance and prediction results


Now that you simply have the list of the most effective tools, combine your favourite with the proper system and your results will skyrocket. There’s nothing better than the combo of a decent approach and superb software. I hope you guys have enjoyed reading this text. Share your thoughts/comments/doubts within the comment section.

Happy experimenting!

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