MLOps Engineering Teams: Top 33 Leading ML Frameworks to Succeed in 2023
In the rapidly evolving field of machine learning (ML), MLOps engineering teams play a vital role in developing and deploying successful ML projects. These teams are responsible for managing the entire ML lifecycle, from data acquisition and model training to deployment and monitoring. To achieve success in 2023 and beyond, it is crucial for MLOps teams to choose the right ML frameworks that provide the necessary tools and capabilities. In this deep-dive report, we will explore the top 33 ML frameworks that can empower MLOps engineering teams to excel in their endeavours.
Introduction to MLOps Engineering Teams
MLOps engineering teams are multidisciplinary groups that bring together expertise in machine learning, software engineering, and operations. These teams focus on creating and managing ML systems in production, ensuring scalability, reliability, and performance. MLOps engineers collaborate with data scientists, software developers, and IT operations to bridge the gap between ML research and deployment.
Importance of ML Frameworks in MLOps
ML frameworks serve as the backbone of MLOps engineering teams, providing the necessary tools and infrastructure to develop and operationalize ML models. These frameworks offer a wide range of capabilities, such as data preprocessing, model training, hyperparameter tuning, and model serving. By leveraging ML frameworks, MLOps teams can streamline their workflows, accelerate model development, and achieve faster time-to-market.
Evaluating ML Frameworks for MLOps Engineering
When selecting ML frameworks for MLOps engineering, several factors need to be considered. These include:
Ease of use:
Firstly, ease of use is crucial – the framework should have an intuitive API and a supportive community to ensure that users can easily navigate and utilize its features.
Scalability:
Scalability is also essential; the chosen framework should enable distributed training and deployment on various infrastructures to accommodate for growth and changing needs.
Flexibility:
Flexibility is another critical consideration. The selected ML framework should support different ML algorithms and architectures to provide diverse options for problem-solving.
Performance:
Performance must not be overlooked either; efficient computations are vital, especially when working with large-scale datasets.
Integration:
Seamless integration with other tools and platforms is imperative as well; the chosen framework must harmoniously work alongside existing technologies without disruption or difficulty.
Documentation and support:
Finally, comprehensive documentation paired with active community support are necessary resources for troubleshooting issues while providing valuable learning opportunities.
By keeping these considerations in mind when selecting an ML framework for MLOps engineering, you can confidently choose a reliable tool that meets your specific requirements while empowering optimal performance, flexibility, scalability, efficiency, seamless integration into existing systems and strong community support.
Also Read : Top 8 Critical MLOps KPIs for Modern High Performance Tech Teams
Our Ranking Methodology for ML Frameworks for MLOps Engineering
In the highly competitive world of MLOps engineering, it is crucial to select a reliable and efficient ML framework that can help in achieving successful implementation of AI/ML models. With an alarming 80% rate of failure for ML/AI models reaching production environments, it becomes even more essential to choose the right framework.
Our ranking methodology takes into account various critical factors such as the background of main contributors to the projects who are responsible for shaping and guiding these frameworks towards success. Additionally, we also consider the burden of technical debt faced by teams and leaders while operating these frameworks.
To ensure our rankings reflect real-world usage and feedback from industry professionals, we actively seek input from DevOps & MLOps communities. The opinions shared by active contributors, groups, forums, online servers, newsletters help us gauge how well each framework performs in practice. This includes Github & Stackoverflow Signals.
We also put into consideration if the framework is adopted by Universities or Online Certification Programs as a part of their curriculum. Frameworks adopted by Universities and Certification programs are often recommended by industry experts based on the demands in job sectors of the industry, ensuring smooth transition from student to successful candidate for the industry.
We evaluate them on their rate of adoption among firms across different serving sectors because popularity often indicates reliability and functionality. Lastly,the programming language required to operate with full functionality plays a vital role in determining how easy or difficult it will be for engineers to use these frameworks effectively.
By taking all these crucial factors into account when ranking ML frameworks for MLOps engineering purposes – we assure you that our recommendations are based on robust research methods that take into account both theoretical considerations as well as practical experience gained through working with these tools over time.
Top 33 ML Frameworks for MLOps Success
Here goes the top 33 Leading ML Frameworks adopted across leading companies & ML teams.
PyTorch is an open-source deep learning framework that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system. It is available with a Python and C++ interface and resides inside the torch module.
One of the key features of PyTorch is its ability to transition seamlessly between eager and graph models with TorchScript, and accelerate the path to production with TorchServe. It also has a rich ecosystem of tools and libraries that extends PyTorch and supports development in computer vision, NLP, and more.
PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. It also has a robust community of developers who contribute, learn, and get their questions answered.
Major Contributing Companies – Meta/Facebook, Alibaba, Quantsight.
Inception Year – 2012
All time contributors – 3.6K
StackOverflow Tags – 22K
TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud.
One of the key features of TensorFlow is its ability to prepare and load data for successful ML outcomes. It offers multiple data tools to help consolidate, clean, and preprocess data at scale 2. Additionally, it provides robust capabilities to deploy models on any environment – servers, edge devices, browsers, mobile, microcontrollers, CPUs, GPUs, FPGAs 2.
TensorFlow also has an entire ecosystem built on the Core framework that streamlines model construction, training, and export. It supports distributed training, immediate model iteration and easy debugging with Keras, and much more. It also has a rich community of developers who contribute, learn, and get their questions answered.
Major Contributing Companies – Google/Alphabet, MobileIron.
Inception Year – 2015
All time contributors – 3.7K
StackOverflow Tags – 82K
MLflow is an open-source platform for managing the machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry. It is a versatile, expandable platform with built-in integrations with many popular ML libraries, but can be used with any library, algorithm, or deployment tool.
One of the key features of MLflow is its ability to track ML experiments to record and compare model parameters, evaluate performance, and manage artifacts. It also offers components such as MLflow Projects to package data science code in a format to reproduce runs on any platform and MLflow Models to deploy machine learning models in diverse serving environments.
MLflow is designed to scale from 1 user to large organizations.
Major Contributing Companies – Databricks,Linkedin
Inception Year – 2018
All time contributors – 590
StackOverflow Tags – 700
Keras is an open-source, high-level neural network library written in Python that is capable of running on Theano, TensorFlow, or CNTK. It was developed by one of the Google engineers, Francois Chollet, and is made user-friendly, extensible, and modular for facilitating faster experimentation with deep neural networks.
One of the key features of Keras is its focus on user experience. It follows best practices for reducing cognitive load by offering consistent and simple APIs, minimizing the number of user actions required for common use cases, and providing clear and actionable error messages. It also has a large adoption in the industry and a research community that works amazingly with the production community.
Keras provides high flexibility to its developers by integrating low-level deep learning languages such as TensorFlow or Theano, ensuring that anything written in the base language can be implemented in Keras. It also has a massive community and ecosystem with around 2.5 million developers as of early 2023.
Major Contributing Companies – GoodDollar.org,Google (Alphabet),Activeloop,American Family Insurance,Hugging Face
Inception Year – 2015
All time contributors – 1.1K
StackOverflow Tags – 42K
Ivy is an ML framework that currently supports JAX(Just After eXecution), TensorFlow, PyTorch, MXNet, and Numpy. It is a unified machine learning framework that maximizes the portability of machine learning codebases by wrapping the functional APIs of existing frameworks.
One of the key features of Ivy is its ability to run any ML code with any ML framework on any hardware. It enables automatic code conversions between frameworks and has a mission to unify all ML frameworks.
Ivy also has a host of derived libraries written in Ivy in the areas of mechanics, 3D vision, robotics, gym environments, neural memory, pre-trained models and implementations, and builder tools with trainers, data loaders and more.
Major Contributing Companies – Ivy (UnifyAI), Google Summer of Code
Inception Year – 2021
All time contributors – 1K
StackOverflow Tags – 13.5K
ROOT, which is a modular scientific software framework. It provides a set of OO frameworks with all the functionality needed to handle and analyze large amounts of data in a very efficient way.
One of the key features of ROOT is its ability to get direct access to the separate attributes of selected objects without having to touch the bulk of the data. It includes histogramming methods in an arbitrary number of dimensions, curve fitting, function evaluation, minimization, graphics and visualization classes to allow the easy setup of an analysis system that can query and process the data interactively or in batch mode, as well as a general parallel processing framework, PROOF, that can considerably speed up an analysis.
ROOT is an open system that can be dynamically extended by linking external libraries. This makes ROOT a premier platform on which to build data acquisition, simulation and data analysis systems.
Major Contributing Companies – APPSULOVE, CERN
Inception Year – 2000
All time contributors – 449
StackOverflow Tags – 3.3K
Scikit-learn is an open-source Python library for machine learning built on top of SciPy. It provides simple and efficient tools for predictive data analysis that are accessible to everybody and reusable in various contexts.
One of the key features of Scikit-learn is its ability to provide a selection of efficient tools for machine learning and statistical modeling, including classification, regression, clustering, and dimensionality reduction via a consistent interface in Python 34. It is built on NumPy, SciPy, and matplotlib and is open-source with a commercially usable BSD license.
Scikit-learn has a rich ecosystem of tools and libraries that extends its capabilities and supports development in various areas of machine learning. It also has a robust community of developers who contribute, learn, and get their questions answered.
Major Contributing Companies – Inria,Quansight,Microsoft,Brookhaven National Laboratory
Inception Year – 2010
All time contributors – 2.7K
StackOverflow Tags – 27.8K
Also Read : Top 5 Most Wanted AIOps & ChatOps Services for High Performance Dev Teams – Part 1 : FinOps
PyTorch Lightning is a deep learning framework with “batteries included” for professional AI researchers and machine learning engineers who need maximal flexibility while super-charging performance at scale. It is built on top of PyTorch and organizes PyTorch code to remove boilerplate and unlock scalability.
One of the key features of PyTorch Lightning is its ability to provide a lightweight PyTorch wrapper for high-performance AI research. It scales models, not the boilerplate, and evolves with projects as they go from idea to paper/production.
PyTorch Lightning has a rich ecosystem of tools and libraries that extends its capabilities and supports development in various areas of machine learning. It also has a robust community of developers who contribute, learn, and get their questions answered.
Major Contributing Companies – Lightning AI,Grid AI
Inception Year – 2019
All time contributors – 870
StackOverflow Tags – 530
FiftyOne is an open-source tool for building high-quality datasets and computer vision models. It supercharges machine learning workflows by enabling users to visualize datasets and interpret models faster and more effectively.
One of the key features of FiftyOne is its ability to provide the building blocks for optimizing dataset analysis pipelines. It allows users to get hands-on with their data, including visualizing complex labels, evaluating models, exploring scenarios of interest, identifying failure modes, finding annotation mistakes, and much more.
FiftyOne has a rich ecosystem of tools and libraries that extends its capabilities and supports development in various areas of machine learning.
Major Contributing Companies – Voxel51
Inception Year – 2020
All time contributors – 74
StackOverflow Tags – 21
Hub is an open-source dataset format that allows users to store vectors, images, texts, videos, etc. and stream data in real-time to PyTorch/TensorFlow. It is developed by Activeloop and can be used with LLMs/LangChain.
One of the key features of Hub is its ability to provide a fast and efficient way to store and access large datasets for machine learning. It allows users to easily manage their data and use it with popular machine learning frameworks.
Major Contributing Companies – Activeloop
Inception Year – 2019
All time contributors – 115
StackOverflow Tags – 56K
BentoML is an open-source platform for building, shipping, and running AI-powered applications. It combines the best developer experience with a focus on operating ML in production and enables Data Science teams to do their best work.
One of the key features of BentoML is its ability to accelerate and standardize the process of taking ML models to production. It makes it easy for developers and data scientists alike to test, deploy, and integrate their models with other systems. BentoML also provides tools to build scalable and high-performance prediction services.
Major Contributing Companies – BentoML
Inception Year – 2019
All time contributors – 162
Github Stars – 5K
cuML is a suite of fast, GPU-accelerated machine learning algorithms designed for data science and analytical tasks. It is part of the RAPIDS ecosystem and provides an API that mirrors Sklearn’s, allowing practitioners to use the easy fit-predict-transform paradigm without ever having to program on a GPU.
One of the key features of cuML is its ability to provide fast and effective processing of machine learning tasks on GPUs. It enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming.
Major Contributing Companies – NVIDIA
Inception Year – 2018
All time contributors – 14
StackOverflow Tags – 154
Jina AI is an open-source platform that empowers businesses and developers to create cutting-edge neural search, generative AI, and multimodal services using state-of-the-art LMOps, MLOps, and cloud-native technologies. It provides a groundbreaking, multi-modal content-serving machine learning production and solution.
One of the key features of Jina AI is its ability to provide complex models ready to use out of the box and even support low-threshold construction of customized models. It enables anyone to build cross-modal and multi-modal applications on the cloud.
Major Contributing Companies – Jina AI
Inception Year – 2020
All time contributors – 172
Github Stars – 18.5K
PostgresML is an AI application database that enables you to perform training and inference on text and tabular data using SQL queries. It allows you to download open-source models from Huggingface or train your own, to create and index LLM embeddings, generate text, or make online predictions using only SQL.
One of the key features of PostgresML is its ability to seamlessly integrate machine learning models into your PostgreSQL database and harness the power of cutting-edge algorithms to process data efficiently. It provides a simple and efficient way to perform natural language processing (NLP) tasks like sentiment analysis, question answering, translation, summarization, and text generation.
Major Contributing Companies – Hyperparam AI,Hydra
Inception Year – 2022
All time contributors – 26
Github Stars – 3.3K
Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system. It is suitable for a wide variety of AI tasks and is hosted by the Linux Foundation AI & Data.
One of the key features of Ludwig is its ability to provide a simple and flexible way to define machine learning pipelines. It allows users to easily specify their desired model architecture, training procedure, and evaluation metrics using a data-driven configuration system.
Major Contributing Companies – Predibase, Freddie Mac
Inception Year – 2019
All time contributors – 140
Github Stars – 8.9K
DALI, or Data Loading Library, is a GPU-accelerated library for data loading and pre-processing to accelerate deep learning applications. It provides a collection of highly optimized building blocks for loading and processing data, and an execution engine to offload data processing to the GPU.
One of the key features of DALI is its ability to provide fast and efficient data loading and pre-processing for deep learning applications. It allows developers to easily define complex data processing pipelines and execute them on the GPU, reducing the time spent on data preparation and increasing the performance of deep learning models.
Major Contributing Companies – NVIDIA
Inception Year – 2018
All time contributors – 88
Github Stars – 4.4K
Seldon Core is an open-source platform for deploying machine learning models on Kubernetes at massive scale. It converts ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices.
One of the key features of Seldon Core is its ability to provide advanced machine learning capabilities out of the box, including advanced metrics, request logging, explainers, outlier detectors, A/B tests, canaries and more. It handles scaling to thousands of production machine learning models and provides an easy and efficient way to deploy and manage them.
Major Contributing Companies – Seldon,RedHat (IBM)
Inception Year – 2017
All time contributors – 191
Github Stars – 3.7K
LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be distributed and efficient, with faster training speed, lower memory usage, and better accuracy than many other gradient boosting frameworks.
One of the key features of LightGBM is its ability to support parallel, distributed, and GPU learning, making it capable of handling large-scale data. It also adopts two novel techniques called Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) to further improve its performance and efficiency.
Major Contributing Companies – SpotHero,Financial University,DPTechnology
Inception Year – 2016
All time contributors – 291
Github Stars – 15K
Hopsworks is a data platform for machine learning with a Python-centric feature store and MLOps capabilities. It is a modular platform that can be used as a standalone feature store, to manage, govern, and serve models, or to develop and operate feature and training pipelines.
One of the key features of Hopsworks is its ability to provide collaboration for machine learning teams, enabling them to develop, manage, and share machine learning assets such as features, models, training data, batch scoring data, logs, and more in a secure and governed manner. It also provides a wide range of capabilities for building and managing feature pipelines using popular Python frameworks.
Major Contributing Companies – Hopsworks
Inception Year – 2013
All time contributors – 52
Github Stars – 920
ML.NET is a free, open-source, cross-platform machine learning framework made specifically for .NET developers. With ML.NET, developers can easily build, train, deploy, and consume custom machine learning models in their .NET applications without requiring prior expertise in developing machine learning models or experience with other programming languages like Python or R.
One of the key features of ML.NET is its ability to provide a simple and flexible way for .NET developers to integrate machine learning into their applications. It allows developers to reuse their existing .NET skills, code, and libraries to easily build and deploy custom machine learning models.
Major Contributing Companies – Microsoft
Inception Year – 2018
All time contributors – 206
Github Stars – 8.4K
Core ML Tools is a Python package that converts models from third-party libraries to Core ML. This allows developers to integrate models trained from TensorFlow or PyTorch into their applications. Additionally, Core ML Tools provides features that read, write, and optimize Core ML models and make predictions.
One of the key features of Core ML Tools is its ability to provide a simple and flexible way for developers to convert third-party machine learning models to the Core ML format. It allows developers to easily integrate machine learning models into their applications and take advantage of the performance and efficiency of Core ML.
Major Contributing Companies – Apple,TRAVECO Transporte AG
Inception Year – 2017
All time contributors – 149
Github Stars – 3.4K
dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real-world problems. It is used in both industry and academia in a wide range of domains, including robotics, embedded devices, mobile phones, and large high-performance computing environments.
One of the key features of dlib is its ability to provide a wide range of machine learning algorithms, including deep learning, support vector machines, relevance vector machines, and clustering algorithms. It also provides tools for creating complex software systems, such as design by contract and component-based software engineering.
dlib is open-source software released under a Boost Software License and has a growing community of developers who contribute, learn, and get their questions answered.
Major Contributing Companies – Intel,Aurora
Inception Year – 2008
All time contributors – 188
Github Stars – 12K
Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow or MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models.
One of the key features of Thinc is its ability to provide a simple and flexible way to define and compose machine learning models. It allows developers to easily integrate layers from other frameworks and take advantage of the performance and efficiency of Thinc’s functional-programming API.
Major Contributing Companies – Explosion,TRAVECO Transporte AG
Inception Year – 2014
All time contributors – 61
Github Stars – 2.7K
Deeplearning4j is a suite of tools for deploying and training deep learning models using the JVM. It allows you to train models from Java while interoperating with the Python ecosystem through a mix of Python execution via its CPython bindings, model import support, and interop of other runtimes such as TensorFlow-Java and ONNXRuntime.
One of the key features of Deeplearning4j is its ability to provide a simple and flexible way to define, train, and deploy deep learning models. It allows developers to easily integrate models from other frameworks and take advantage of the performance and efficiency of the JVM.
Deeplearning4j is open-source software released under Apache License 2.0 and has a growing community of developers who contribute, learn, and get their questions answered.
Inception Year – 2019
All time contributors – 66
Github Stars – 12.9K
JARVIS is a system developed by Microsoft to connect LLMs (Large Language Models) with the ML community. It introduces a collaborative system that consists of an LLM as the controller and numerous expert models as collaborative executors (from HuggingFace Hub).
The workflow of JARVIS consists of four stages: Task Planning, Model Selection, Task Execution, and Response Generation. In the Task Planning stage, ChatGPT is used to analyze the requests of users to understand their intention and disassemble them into possible solvable tasks. In the Model Selection stage, ChatGPT selects expert models hosted on Hugging Face based on their descriptions. In the Task Execution stage, each selected model is invoked and executed, and the results are returned to ChatGPT. Finally, in the Response Generation stage, ChatGPT integrates the prediction of all models and generates responses.
Inception Year – 2023
All time contributors – 21
Github Stars – 21K
Sparkling Water is an open-source machine learning framework that combines the power of H2O.ai and Apache Spark. It allows users to leverage the best of both worlds: the scalability and speed of Apache Spark, and the advanced machine learning algorithms of H2O.ai.
With Sparkling Water, users can seamlessly integrate H2O.ai’s machine learning algorithms into their existing Apache Spark workflows. This means that they can easily build and deploy machine learning models at scale, using the familiar Spark API.
One of the key benefits of using Sparkling Water is its ability to handle large datasets. Thanks to the distributed nature of both Apache Spark and H2O.ai, users can process massive amounts of data in parallel, significantly reducing the time it takes to train machine learning models.
In addition to its scalability, Sparkling Water also offers a wide range of advanced machine learning algorithms, including deep learning, gradient boosting, and generalized linear models. These algorithms are highly optimized and can deliver accurate results even on large datasets.
Major Contributing Companies – H20.ai
Inception Year – 2014
All time contributors – 62
Github Stars – 946
PrimeHub is an all-in-one platform for machine learning that provides users with a comprehensive set of tools and features to develop, train, and deploy machine learning models. It is designed to simplify the machine learning workflow, making it easier for data scientists and machine learning practitioners to build and deploy models at scale.
One of the key features of PrimeHub is its user-friendly interface, which allows users to easily manage their machine learning projects. Users can create and organize their datasets, train and evaluate their models, and deploy their models to production, all from a single, intuitive interface.
In addition to its user-friendly interface, PrimeHub also offers a wide range of advanced machine learning tools and features. These include support for popular machine learning frameworks such as TensorFlow and PyTorch, as well as built-in support for distributed training and hyperparameter tuning.
Major Contributing Companies – InfuseAI
Inception Year – 2019
All time contributors – 30
Github Stars – 350
WEKA (Waikato Environment for Knowledge Analysis) is an open-source machine learning toolkit developed by the University of Waikato in New Zealand. It provides a collection of machine learning algorithms and tools that can be used for data mining, data analysis, and predictive modeling.
One of the key features of WEKA is its user-friendly graphical user interface, which allows users to easily explore and analyze their data. Users can apply various machine learning algorithms to their data, visualize the results, and evaluate the performance of their models.
In addition to its graphical user interface, WEKA also offers a command-line interface and a Java API, making it a versatile tool for both novice and experienced users. It supports a wide range of machine learning algorithms, including classification, regression, clustering, and association rule mining.
Major Contributing Companies – The University of Waikato
Inception Year – 1999
All time contributors – 20
StackOverflow Tags – 3K
Magenta is an open-source machine learning framework developed by Google that focuses on the creation of art and music. It provides a collection of machine learning algorithms and tools that can be used to generate new artistic content, such as music, drawings, and paintings.
One of the key features of Magenta is its ability to generate new content that is both original and coherent. Using advanced machine learning algorithms, Magenta can analyze existing artistic content and generate new content that is similar in style and structure.
In addition to its content generation capabilities, Magenta also offers a range of tools for artists and musicians to interact with its machine learning models. These tools allow users to control various aspects of the content generation process, such as the style, structure, and complexity of the generated content.
Major Contributing Companies – Google (Alphabet),Hugging Face
Inception Year – 2016
All time contributors – 159
Github Stars – 18.5K
Shogun is an open-source machine learning library that provides a wide range of machine learning algorithms and tools. It is designed to be both user-friendly and highly efficient, making it an excellent choice for data scientists and machine learning practitioners of all skill levels.
One of the key features of Shogun is its support for multiple programming languages, including C++, Python, and R. This means that users can easily integrate Shogun into their existing workflows, regardless of their preferred programming language.
In addition to its language support, Shogun also offers a wide range of machine learning algorithms, including classification, regression, clustering, and dimensionality reduction. These algorithms are highly optimized and can deliver accurate results even on large datasets.
Major Contributing Companies – DeepMind,Neo Cybernetica,TomTom
Inception Year – 2006
All time contributors – 233
Github Stars – 2.9K
Sonnet is a high-level library for building neural networks, developed by DeepMind. It is built on top of TensorFlow, and provides a simple and intuitive interface for defining and training complex neural network architectures.
One of the key features of Sonnet is its modular design, which allows users to easily define and reuse neural network components. This makes it easy to build complex neural network architectures, without having to write large amounts of boilerplate code.
In addition to its modular design, Sonnet also offers a range of advanced features, such as support for distributed training and automatic differentiation. These features make it easier for users to train large-scale neural network models, and to experiment with new architectures and techniques.
Major Contributing Companies – Google (Alphabet)
Inception Year – 2017
All time contributors – 56
Github Stars – 9.5K
CNTK (Microsoft Cognitive Toolkit) is a powerful deep learning toolkit developed by Microsoft. It provides a wide range of tools and features for building and training deep neural networks, making it an excellent choice for data scientists and machine learning practitioners working on complex deep learning projects.
One of the key features of CNTK is its support for distributed training, which allows users to train large-scale neural network models across multiple machines. This can significantly reduce the time it takes to train complex models, and makes it easier to experiment with different architectures and techniques.
In addition to its distributed training capabilities, CNTK also offers a wide range of advanced features, such as support for multiple programming languages (including Python and C++), automatic differentiation, and built-in support for popular deep learning frameworks such as TensorFlow and Keras.
Major Contributing Companies – Microsoft,Meta / Facebook,Lyft
Inception Year – 2014
All time contributors – 253
Github Stars – 17.3K
Chainer is an open-source deep learning framework developed by Preferred Networks. It provides a flexible and intuitive interface for building and training deep neural networks, making it an excellent choice for data scientists and machine learning practitioners working on complex deep learning projects.
One of the key features of Chainer is its dynamic computational graph, which allows users to define their neural network architectures on-the-fly. This makes it easy to experiment with different architectures and techniques, without having to pre-define the entire computational graph.
In addition to its dynamic computational graph, Chainer also offers a wide range of advanced features, such as support for multiple GPUs, automatic differentiation, and built-in support for popular deep learning frameworks such as TensorFlow and PyTorch.
Major Contributing Companies – Preferred Networks
Inception Year – 2015
All time contributors – 310
Github Stars – 5.8K
MLOps Engineering Teams – Final thoughts
ML frameworks are essential tools for MLOps engineering teams to achieve success in 2023 and beyond. By selecting the right ML framework, teams can streamline their workflows, enhance model development, and effectively manage the ML lifecycle. The top 33 ML frameworks mentioned in this article provide a wide range of capabilities and options to cater to the diverse needs of MLOps engineering teams.