Best Machine Learning Software for PyTorch

The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation.

Select the subset of data that has the greatest impact on the accuracy of your model. This allows you to improve your model by using the best data in retraining. Reduce data redundancy and bias and focus on edge cases to get the most from your data. Lightly's algorithms are capable of processing large amounts of data in less than 24 hour. Connect Lightly with your existing buckets to process new data automatically. Our API automates the entire data selection process. Use the latest active learning algorithms. Combining active- and selfsupervised learning algorithms lightly for data selection. Combining model predictions, embeddings and metadata will help you achieve your desired distribution of data. Improve your model's performance by understanding data distribution, bias and edge cases. Manage data curation and keep track of the new data for model training and labeling. Installation is easy via a Docker Image and cloud storage integration. No data leaves your infrastructure.

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

Explore a new library and dataset in a notebook. A 2orkflow automates preprocessing, training, and testing. A deployment brings your application to life. You can use notebooks, workflows, or deployments separately. Compatible with all. Gradient is compatible with all major frameworks. Gradient is powered with Paperspace's top-of-the-line GPU instances. Source control integration makes it easier to move faster. Connect to GitHub to manage your work and compute resources using git. In seconds, you can launch a GPU-enabled Jupyter Notebook directly from your browser. Any library or framework is possible. Invite collaborators and share a link. This cloud workspace runs on free GPUs. A notebook environment that is easy to use and share can be set up in seconds. Perfect for ML developers. This environment is simple and powerful with lots of features that just work. You can either use a pre-built template, or create your own. Get a free GPU

The workflow automation platform that automates complex, mission-critical data processing and ML processes at large scale. Flyte makes it simple to create machine learning and data processing workflows that are concurrent, scalable, and manageable. Flyte is used for production at Lyft and Spotify, as well as Freenome. Flyte is used at Lyft for production model training and data processing. It has become the de facto platform for pricing, locations, ETA and mapping, as well as autonomous teams. Flyte manages more than 10,000 workflows at Lyft. This includes over 1,000,000 executions per month, 20,000,000 tasks, and 40,000,000 containers. Flyte has been battle-tested by Lyft and Spotify, as well as Freenome. It is completely open-source and has an Apache 2.0 license under Linux Foundation. There is also a cross-industry oversight committee. YAML is a useful tool for configuring machine learning and data workflows. However, it can be complicated and potentially error-prone.

Qwak build system allows data scientists to create an immutable, tested production-grade artifact by adding "traditional" build processes. Qwak build system standardizes a ML project structure that automatically versions code, data, and parameters for each model build. Different configurations can be used to build different builds. It is possible to compare builds and query build data. You can create a model version using remote elastic resources. Each build can be run with different parameters, different data sources, and different resources. Builds create deployable artifacts. Artifacts built can be reused and deployed at any time. Sometimes, however, it is not enough to deploy the artifact. Qwak allows data scientists and engineers to see how a build was made and then reproduce it when necessary. Models can contain multiple variables. The data models were trained using the hyper parameter and different source code.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

Giskard provides interfaces to AI & Business teams for evaluating and testing ML models using automated tests and collaborative feedback. Giskard accelerates teamwork to validate ML model validation and gives you peace-of-mind to eliminate biases, drift, or regression before deploying ML models into production.

TrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently.

Simplify your MLOps pipelines. ZenML allows you to manage, deploy and scale any infrastructure. ZenML is open-source and free. Two simple commands will show you the magic. ZenML can be set up in minutes and you can use all your existing tools. ZenML interfaces ensure your tools work seamlessly together. Scale up your MLOps stack gradually by changing components when your training or deployment needs change. Keep up to date with the latest developments in the MLOps industry and integrate them easily. Define simple, clear ML workflows and save time by avoiding boilerplate code or infrastructure tooling. Write portable ML codes and switch from experiments to production in seconds. ZenML's plug and play integrations allow you to manage all your favorite MLOps software in one place. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code.

Select the configurations and resources required for specific code segments within your project. It only takes seconds to save and apply changes in a training scenario. Select the right configuration of computing resources to launch training models in a matter of seconds. All will be created automatically, without the need to manage infrastructure. Select a serverless or dedicated operating mode. All in one interface, manage project data, save to datasets and connect to databases, object storage or other repositories. Create a ML model with colleagues from around the world, share the project and set budgets across your organization. Launch your ML within minutes, without developers' help. Try out experiments with different models being published simultaneously.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

Your ML model can be served in minutes in any cloud. Unified model packaging format that allows online and offline delivery on any platform. Our micro-batching technology allows for 100x more throughput than a regular flask-based server model server. High-quality prediction services that can speak the DevOps language, and seamlessly integrate with common infrastructure tools. Unified format for deployment. High-performance model serving. Best practices in DevOps are incorporated. The service uses the TensorFlow framework and the BERT model to predict the sentiment of movie reviews. DevOps-free BentoML workflow. This includes deployment automation, prediction service registry, and endpoint monitoring. All this is done automatically for your team. This is a solid foundation for serious ML workloads in production. Keep your team's models, deployments and changes visible. You can also control access via SSO and RBAC, client authentication and auditing logs.

Neptune.ai, a platform for machine learning operations, is designed to streamline tracking, organizing and sharing of experiments, and model-building. It provides a comprehensive platform for data scientists and machine-learning engineers to log, visualise, and compare model training run, datasets and hyperparameters in real-time. Neptune.ai integrates seamlessly with popular machine-learning libraries, allowing teams to efficiently manage research and production workflows. Neptune.ai's features, which include collaboration, versioning and reproducibility of experiments, enhance productivity and help ensure that machine-learning projects are transparent and well documented throughout their lifecycle.

One development environment for all data science workflows. Natively analyze your data without the need to switch between services. Data to training at scale Models can be built and trained 5X faster than traditional notebooks. Scale up model development using simple connectivity to Vertex AI Services. Access to data is simplified and machine learning is made easier with BigQuery Dataproc, Spark and Vertex AI integration. Vertex AI training allows you to experiment and prototype at scale. Vertex AI Workbench allows you to manage your training and deployment workflows for Vertex AI all from one location. Fully managed, scalable and enterprise-ready, Jupyter-based, fully managed, scalable, and managed compute infrastructure with security controls. Easy connections to Google Cloud's Big Data Solutions allow you to explore data and train ML models.

You can now build what took years. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy and maintain ML in production. Superwise integrates with any ML stack, and can connect to any number of communication tools. Want to go further? Superwise is API-first. All of our APIs allow you to access everything, and we mean everything. All this from the comfort of your cloud. You have complete control over ML monitoring. You can set up metrics and policies using our SDK and APIs. Or, you can simply choose a template to monitor and adjust the sensitivity, conditions and alert channels. Get Superwise or contact us for more information. Superwise's ML monitoring policy templates allow you to quickly create alerts. You can choose from dozens pre-built monitors, ranging from data drift and equal opportunity, or you can customize policies to include your domain expertise.

You can deploy Mystic in your own Azure/AWS/GCP accounts or in our shared GPU cluster. All Mystic features can be accessed directly from your cloud. In just a few steps, you can get the most cost-effective way to run ML inference. Our shared cluster of graphics cards is used by hundreds of users at once. Low cost, but performance may vary depending on GPU availability in real time. We solve the infrastructure problem. A Kubernetes platform fully managed that runs on your own cloud. Open-source Python API and library to simplify your AI workflow. You get a platform that is high-performance to serve your AI models. Mystic will automatically scale GPUs up or down based on the number API calls that your models receive. You can easily view and edit your infrastructure using the Mystic dashboard, APIs, and CLI.

Keepsake, an open-source Python tool, is designed to provide versioning for machine learning models and experiments. It allows users to track code, hyperparameters and training data. It also tracks metrics and Python dependencies. Keepsake integrates seamlessly into existing workflows. It requires minimal code additions and allows users to continue training while Keepsake stores code and weights in Amazon S3 or Google Cloud Storage. This allows for the retrieval and deployment of code or weights at any checkpoint. Keepsake is compatible with a variety of machine learning frameworks including TensorFlow and PyTorch. It also supports scikit-learn and XGBoost. It also has features like experiment comparison that allow users to compare parameters, metrics and dependencies between experiments.

Our platform allows you to create AI products, train, fine-tune, and deploy models on the cloud. You don't have to worry about scaling, infrastructure, cost management, or other technical issues. Prebuilt, fully customizable modular components make it easy to train, fine tune, and deploy models. The science, not the engineering, should be your focus. Lightning components organize code to run on the cloud and manage its own infrastructure, cloud cost, and other details. 50+ optimizations to lower cloud cost and deliver AI in weeks, not months. Enterprise-grade control combined with consumer-level simplicity allows you to optimize performance, reduce costs, and take on less risk. Get more than a demo. In days, not months, you can launch your next GPT startup, diffusion startup or cloud SaaSML service.

Data scientists and developers can collaborate on ML projects by empowering them. Before publishing to end-users, build, load, optimize, and test models and their integrations. Data science workload can be reduced and decision-making improved by sharing and storing ML models throughout the organization. Publish updates to automatically push any changes to production models. ML-powered insights can be instantly provided within any web-based business app to increase efficiency and boost productivity. Our browser extension allows analysts and business users to seamlessly integrate models into any web application using drag-and-drop.

MLReef allows domain experts and data scientists secure collaboration via a hybrid approach of pro-code and no-code development. Distributed workloads lead to a 75% increase in productivity. This allows teams to complete more ML project faster. Domain experts and data scientists can collaborate on the same platform, reducing communication ping-pong to 100%. MLReef works at your location and enables you to ensure 100% reproducibility and continuity. You can rebuild all work at any moment. To create interoperable, versioned, explorable AI modules, you can use git repositories that are already well-known. Your data scientists can create AI modules that you can drag and drop. These modules can be modified by parameters, ported, interoperable and explorable within your organization. Data handling requires a lot of expertise that even a single data scientist may not have. MLReef allows your field experts to assist you with data processing tasks, reducing complexity.

With just one line of code, you can deploy all major ML frameworks like Pytorch and Onnx. Do you not have your own models? Prebuilt models can be deployed to reduce latency and cost. You can fine-tune models for specific tasks to reduce latency and costs while increasing performance. It's easy to do and you don't have to worry about infrastructure. Integrate with the top ML observability platform to be alerted on feature or prediction drift, compare models versions, and resolve issues quickly. To resolve model performance problems, discover the root causes of prediction and feature drift. Find out which features contribute the most to your model's performance.

Amazon Elastic Compute Cloud Trn1 instances powered by AWS Trainium are designed for high-performance deep-learning training of generative AI model, including large language models, latent diffusion models, and large language models. Trn1 instances can save you up to 50% on the cost of training compared to other Amazon EC2 instances. Trn1 instances can be used to train 100B+ parameters DL and generative AI model across a wide range of applications such as text summarizations, code generation and question answering, image generation and video generation, fraud detection, and recommendation. The AWS neuron SDK allows developers to train models on AWS trainsium (and deploy them on the AWS Inferentia chip). It integrates natively into frameworks like PyTorch and TensorFlow, so you can continue to use your existing code and workflows for training models on Trn1 instances.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

Amazon EC2 instances G5 are the latest generation NVIDIA GPU instances. They can be used to run a variety of graphics-intensive applications and machine learning use cases. They offer up to 3x faster performance for graphics-intensive apps and machine learning inference, and up to 3.33x faster performance for machine learning learning training when compared to Amazon G4dn instances. Customers can use G5 instance for graphics-intensive apps such as video rendering, gaming, and remote workstations to produce high-fidelity graphics real-time. Machine learning customers can use G5 instances to get a high-performance, cost-efficient infrastructure for training and deploying larger and more sophisticated models in natural language processing, computer visualisation, and recommender engines. G5 instances offer up to three times higher graphics performance, and up to forty percent better price performance compared to G4dn instances. They have more ray tracing processor cores than any other GPU based EC2 instance.