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Compare 45+ MLOps Tools: A comprehensive vendor benchmark in '24

Cem Dilmegani
Updated on Jan 4
5 min read
Compare 45+ MLOps Tools: A comprehensive vendor benchmark in '24Compare 45+ MLOps Tools: A comprehensive vendor benchmark in '24

In our previous articles, we discussed what the machine learning lifecycle is and how DevOps-inspired Machine Learning Operations (MLOps) helps build and deploy machine learning systems by standardizing and streamlining ML workflows. In this article, we’ll explore the landscape of MLOps tools for different components of the ML lifecycle.

What are the types of MLOps solution providers?

Open Source

Half of IT organizations use open-source tools for AI and ML and the figure is expected to be around two-thirds in 2023. On GitHub alone, there are 65 million developers and 3 million organizations contributing to 200 million projects. 

Therefore, it is no surprise that there are advanced open-source toolkits in AI and ML landscape. Open-source tools concentrate on specific tasks within MLOps rather than providing end-to-end machine learning lifecycle management. These tools and platforms typically require a development environment in Python and R.

Startups

Like open-source tools, most startups in the MLOps landscape provide tools for specific tasks within MLOps. Unlike open-source, startups tend to offer tools that target non-technical users.

Tech Giants

There are open-source tools developed by tech giants that address specific use cases in MLOps practices. However, the end-to-end MLOps solutions (or MLOps platforms) landscape is dominated by tech giants such as Google, Microsoft, or Alibaba.

What are the different types of MLOps tools?

MLOps tools can be divided into three major areas dealing with:

  • Data management
  • Modeling
  • Operationalization

There are also tools that can be considered as “MLOps platforms”, providing end-to-end machine learning lifecycle management.

We’ll explore tools for individual tasks within the major areas and MLOps platforms in turn.

Data Management

Data Labeling

Data labeling tools (also called data annotation, tagging, or classification tools) are used to label large volumes of data such as texts, images, or audio. Labeled data is then used to train supervised machine learning algorithms in order to make predictions about new, unlabeled data. Some examples of data labeling tools include:

NameStatusLaunched In
DoccanoOpen Source2019
iMeritPrivate2012
LabelboxPrivate2017
ProdigyPrivate2017
Segments.aiPrivate2020
SnorkelPrivate2019
SuperviselyPrivate2017

For more, check our article on how to choose a data labeling vendor. Also, don’t forget to check our data annotation services list.

Data Versioning

Data versioning (also called data version control) tools enable managing different versions of datasets and storing them in an accessible and well-organized way. This allows data science teams to gain insights such as identifying how data changes impact model performance and understanding how datasets are evolving.

Some popular data versioning tools are:

NameStatusLaunched In
CometPrivate2017
Data Version Control (DVC)Open Source2017
Delta LakeOpen Source2019
DoltOpen Source2020
LakeFSOpen Source2020
PachydermPrivate2014
QriOpen Source2018
Weights & BiasesPrivate2018

Modeling

Feature Engineering

Feature engineering tools automate the process of extracting useful features from raw datasets to create better training data for machine learning models. These tools can accelerate the process of feature engineering for common applications and generic problems. However, it may be necessary to improve machine-generated feature engineering results using domain knowledge. Some feature engineering tools include:

NameStatusLaunched In
AutoFeatOpen Source2019
dotDataPrivate2018
FeastOpen Source2019
FeaturetoolsOpen Source2017
RasgoPrivate2020
TSFreshOpen Source2016

Experiment Tracking

Developing machine learning projects involves running multiple experiments with different models, model parameters, or training data. Experiment tracking tools save all the necessary information about different experiments during model training. This allows tracking the versions of experiment components and the results and allows comparison between different experiments. Some examples of experiment tracking tools are:

NameStatusLaunched In
CometPrivate2017
Guild AIOpen Source2019
ModelDBOpen Source2020
Neptune.aiPrivate2017
TensorBoardOpen Source2017
Weights & BiasesPrivate2018
MLFlow TrackingOpen Source2018

Hyperparameter Optimization

Hyperparameters are the parameters of the machine learning models such as the size of a neural network or types of regularization that model developers can adjust to achieve different results. Hyperparameter tuning or optimization tools automate the process of searching and selecting hyperparameters that give optimal performance for machine learning models. Popular hyperparameter tuning tools include:

NameStatusLaunched In
Google VizierPublic2017
HyperoptOpen Source2013
OptunaOpen Source2018
Scikit-OptimizeOpen Source2016
SigOptPublic2014
TalosOpen Source2018

Model Versioning

Model versioning tools help data scientists manage different versions of ML models. Through model versioning, information such as model configuration, provenance data, hyperparameters, validation loss scores, and other metadata is stored in an easily accessible model registry. This metadata store helps data scientists quickly identify the configuration they used to build a particular model, ensuring that they don’t inadvertently use an incorrect or outdated model.

Model versioning systems also have mechanisms for capturing model outputs during training, providing a snapshot of how well a given model performed for each iteration. Versioning helps promote reproducibility, ensuring that published results can be verified in future iterations or investigations. Some tools that enable model versioning are:

NameStatusLaunched In
Data Version Control (DVC)Open Source2017
Neptune.aiPrivate2017
MLFlowOpen Source2018
ModelDBOpen Source2020
ML Metadata (MLMD)Open Source2021

Operationalization

Model Deployment / Serving

Machine learning model deployment tools facilitate integrating ML models into a production environment to make predictions. Some tools in this category are:

NameStatusLaunched In
AlgorithmiaPrivate2014
BentoMLOpen Source2019
KubeflowOpen Source2018
SeldonPrivate2020
TensorFlow ServingOpen Source2016
Torch ServeOpen Source2020

Model Monitoring

Machine learning model monitoring is a key aspect of every successful ML project because ML model performance tends to decay after model deployment due to changes in the input data flow over time. Model monitoring tools detect data drifts and anomalies in real-time and allow setting up alerts in case of performance issues according to specified metrics. Model monitoring tools include:

NameStatusLaunched In
ArizePrivate2020
Evidently AIOpen Source2020
FiddlerPrivate2018
LosswisePrivate2018
Superwise.aiPrivate2019
Unravel DataPrivate2013

MLOps Platforms

As mentioned above, there are also tools that cover the machine learning lifecycle end-to-end. These platforms are often provided by startups or tech giants but there’re also open-source platforms. Popular MLOps platforms include:

NameStatusLaunched In
Alibaba Cloud ML Platform for AIPublic2018
Amazon SageMakerPublic2017
ClouderaPublic2020
DatabricksPrivate2015
DataRobotPrivate2019
Google Cloud PlatformPublic2008
H2O.aiOpen Source2012
IguazioPrivate2014
Microsoft AzurePublic2010
MLFlowOpen Source2018
OpenMLOpen Source2016
PolyaxonPrivate2018
ValohaiPrivate2016

For more, feel free to check our data-driven list of MLOps platforms.

Other categories

Other MLOps tools

These tools are used to assist MLOps and LLMOps developers in specific aspects of MLOps and LLMOps deployment. These tools include:

  • Feature stores:Feature stores serve as a centralized hub for storing, managing, and delivering ML features. They facilitate the discovery and sharing of feature values, supporting both model training and serving. Key features include the ability to create feature engineering pipelines, efficient feature serving, scalability, versioning, validation, metadata management, and integration with ML workflows for reproducibility.
  • Integration frameworks: These frameworks help developing LLM applications such as document analyzers, code analyzers, chatbots etc.
  • Vector databases (VD): VDs store high-dimensional data vectors, such as patient data covering symptoms, blood test results, behaviors, and general health. VDs can search and retrieval unstructured data (like images, video, text, and audio) by content rather than labels or tags. VDs can help with model versioning and management in MLOps and LLMOps.

LLMOps

Large Language Models Operations is a specialized subset of machine learning operations (MLOps) tailored for the efficient development and deployment of Large Language Models (LLMs).

LLMOps ensures that the model quality remains high and that the data quality maintained throughout data science projects by providing infrastructure and tools.

LLMOps tools include MLOps tools and platforms, LLMs that offer LLMOps capabilities, and other tools that can help with fine-tuning, testing and monitoring. Explore more on other LLMOps tools by checking out our data-driven market guide.

AI governance

AI governance involves the creation of regulations, guidelines, and structures designed to steer the progression, implementation, and application of artificial intelligence technologies. Its primary goal is to uphold ethical conduct, transparency, responsibility, and the overall betterment of society, while mitigating potential risks and biases that may be linked to AI systems.

AI governance is a crucial aspect of ML projects, which is why end-to-end MLOPs platforms offer AI governance capabilities. Discover other AI governance tools by reading our comprehensive market guide.

The image shows MLOPs Tools market map including all subcategories of MLOPs like LLMOPs, RAG LLMOPs, Other MLOPs tools and related fields like responsible AI.
Figure 1: MLOPs Tools market map displays subcategories of MLOPs like LLMOPs and related fields.

What is MLOps?

MLOps (Machine Learning Operations) is a set of practices to standardize and streamline the process of developing and deploying machine learning models. It covers the entire machine learning workflow, including data collection, machine learning model deployment, and model management.

If you still have questions about MLOps tools and vendors or artificial intelligence in general, we would like to help:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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