Best Machine Learning Software for GitHub

Mixpanel's mission is to increase innovation. Mixpanel is not only a company but also a service provider for businesses. Companies can use our engagement and analytics product to analyze how users interact, convert, retain, and engage with them in real-time on web, mobile, or smart devices. They can then use this data to improve their products and business. Mixpanel serves more than 26,000 companies in different industries worldwide, including Samsung, Twitter and BMW. Mixpanel is headquartered in San Francisco and has offices in New York City, Seattle, Austin. London, Paris, Barcelona, Paris, London, and Singapore.

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.

Amazon CodeGuru is an intelligent developer tool that uses machine learning to make intelligent recommendations for improving code quality, and identifying the most costly lines of code in an application. Integrate Amazon CodeGuru in your existing software development workflow to get built-in code reviews that will help you identify and optimize the most expensive lines of code to lower costs. Amazon CodeGuru Profiler allows developers to find the most expensive lines in an application's code. It also provides visualizations and suggestions on how to improve code to make it more affordable. Amazon CodeGuru Reviewer uses machine-learning to identify critical issues and difficult-to-find bugs in application development to improve code quality.

Saturn Cloud is an AI/ML platform available on every cloud. Data teams and engineers can build, scale, and deploy their AI/ML applications with any stack.

Colaboratory, also known as "Colab", allows you to create and execute Python from your browser using the web browser. - Zero configuration required Free access to GPUs Easy sharing Colab is available to all levels of the AI research community, including students, data scientists, and researchers. Colab notebooks enable you to combine executable and rich text into one document. They also include images, HTML, LaTeX and more. Your Google Drive account stores your Colab notebooks. Your Colab notebooks can be shared with friends and coworkers. They can be edited or commented on by them.

Clarifai is a leading AI platform for modeling image, video, text and audio data at scale. Our platform combines computer vision, natural language processing and audio recognition as building blocks for building better, faster and stronger AI. We help enterprises and public sector organizations transform their data into actionable insights. Our technology is used across many industries including Defense, Retail, Manufacturing, Media and Entertainment, and more. We help our customers create innovative AI solutions for visual search, content moderation, aerial surveillance, visual inspection, intelligent document analysis, and more. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been a market leader in computer vision AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai is headquartered in Delaware

Deepnote is building the best data science notebook for teams. Connect your data, explore and analyze it within the notebook with real-time collaboration and versioning. Share links to your projects with other analysts and data scientists on your team, or present your polished, published notebooks to end users and stakeholders. All of this is done through a powerful, browser-based UI that runs in the cloud.

Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

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

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.

InsightFinder Unified Intelligence Engine platform (UIE) provides human-centered AI solutions to identify root causes of incidents and prevent them from happening. InsightFinder uses patented self-tuning, unsupervised machine learning to continuously learn from logs, traces and triage threads of DevOps Engineers and SREs to identify root causes and predict future incidents. Companies of all sizes have adopted the platform and found that they can predict business-impacting incidents hours ahead of time with clearly identified root causes. You can get a complete overview of your IT Ops environment, including trends and patterns as well as team activities. You can also view calculations that show overall downtime savings, cost-of-labor savings, and the number of incidents solved.

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.

Self-service machine learning platform that allows you to turn models into APIs with just a few mouse clicks. Sign up for a Deep Infra Account using GitHub, or login using GitHub. Choose from hundreds of popular ML models. Call your model using a simple REST API. Our serverless GPUs allow you to deploy models faster and cheaper than if you were to build the infrastructure yourself. Depending on the model, we have different pricing models. Some of our models have token-based pricing. The majority of models are charged by the time it takes to execute an inference. This pricing model allows you to only pay for the services you use. You can easily scale your business as your needs change. There are no upfront costs or long-term contracts. All models are optimized for low latency and inference performance on A100 GPUs. Our system will automatically scale up the model based on your requirements.

We are the only company that can autonomously tune applications across multiple applications. Opsani rightsizes an application automatically so that your cloud application runs faster and is more efficient. Opsani COaaS optimizes cloud workload performance using the latest AI and Machine Learning. It continuously reconfigures and tunes with every code release and load profile change. This is done while seamlessly integrating with one app or across your service delivery platform, while also scaling autonomously across thousands of services. Opsani makes it possible to solve all three problems autonomously and without compromise. Opsani's AI algorithms can help you reduce costs by up to 71% Opsani optimization continually evaluates trillions upon trillions of configuration possibilities and pinpoints the most effective combinations of resources, parameter settings, and other parameters.

Data engineering workflows that are powerful, but without the headaches of infrastructure. Simple, reusable Python is used to define complex streaming, scheduling and data backfill pipelines. Fetch all your data in real time, no matter how complicated. Deep learning and LLMs can be used to make decisions along with structured business data. Don't pay vendors for data that you won't use. Instead, query data right before online predictions. Experiment with Jupyter and then deploy into production. Create new data workflows and prevent train-serve skew in milliseconds. Instantly monitor your data workflows and track usage and data quality. You can see everything you have computed, and the data will replay any information. Integrate with your existing tools and deploy it to your own infrastructure. Custom hold times and withdrawal limits can be set.

AI teams are faced with challenges that require new technologies. These technologies are built by us. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. AI and software development go hand in hand. Built with data scientists, ML experts, and data engineers at heart. Don't reinvent your wheel! Production is fast and cost-effective. All your data is stored by you. Your machines are used to train your models. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. New technologies are required for AI teams. These technologies are built by us. Studio is an extension to BitBucket, GitLab, and GitHub. Register for the online SaaS version, or contact us to start an on-premise installation

Even if you have the best developers, every test makes them slower. 80% of your software testing is pointless. The problem is that you don't know which 20%. We use your data to find the right 20% so you can ship faster. We offer shrink-wrapped predictive testing selection. This machine learning-based method is used by companies like Facebook and can be used by all companies. We support multiple languages, test runners and CI systems. Bring Git. Launchable uses machine-learning to analyze your source code and test failures. It doesn't rely solely on code syntax analysis. Launchable can easily add support for any file-based programming language. This allows us to scale across projects and teams with different languages and tools. We currently support Python, Ruby and Java, JavaScript and Go, as well as C++ and C++. We regularly add new languages to our support.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

Privacy engineering tools delivered as APIs. In minutes, you can synthesize and transform data. Trust your users and the community. Gretel's APIs allow you to instantly create anonymized or synthetic data sets so that you can safely work with data while protecting your privacy. Access to data must be faster in order to keep up with the development pace. Gretel's data privacy tools bypass blockers, and allow for Machine Learning and AI applications to access data faster. Gretel Cloud runners makes it easy to scale up your workloads to the cloud or keep your data safe by running Gretel containers within your own environment. Developers will find it much easier to train and create synthetic data using our cloud GPUs. Scale workloads instantly with no infrastructure required. Invite colleagues to collaborate on cloud projects, and share data between teams.

All work can be synced, searched and managed across your data science team. Zepl's powerful search allows you to discover and reuse models, code, and other data. Zepl's enterprise collaboration platform allows you to query data from Snowflake or Athena and then build your models in Python. For enhanced interactions with your data, use dynamic forms and pivoting. Zepl creates new containers every time you open your notebook. This ensures that you have the same image each time your models are run. You can invite your team members to join you in a shared space, and they will be able to work together in real-time. Or they can simply leave comments on a notebook. You can share your work with fine-grained access controls. You can allow others to read, edit, run, and share your work. This will facilitate collaboration and distribution. All notebooks can be saved and versioned automatically. An easy-to-use interface allows you to name, manage, roll back, and roll back all versions. You can also export seamlessly into Github.

Amazon SageMaker offers all the tools and libraries needed to build ML models. It allows you to iteratively test different algorithms and evaluate their accuracy to determine the best one for you. Amazon SageMaker allows you to choose from over 15 algorithms that have been optimized for SageMaker. You can also access over 150 pre-built models available from popular model zoos with just a few clicks. SageMaker offers a variety model-building tools, including RStudio and Amazon SageMaker Studio Notebooks. These allow you to run ML models on a small scale and view reports on their performance. This allows you to create high-quality working prototypes. Amazon SageMaker Studio Notebooks make it easier to build ML models and collaborate with your team. Amazon SageMaker Studio notebooks allow you to start working in seconds with Jupyter notebooks. Amazon SageMaker allows for one-click sharing of notebooks.

Amazon SageMaker Studio Lab provides a free environment for machine learning (ML), which includes storage up to 15GB and security. Anyone can use it to learn and experiment with ML. You only need a valid email address to get started. You don't have to set up infrastructure, manage access or even sign-up for an AWS account. SageMaker Studio Lab enables model building via GitHub integration. It comes preconfigured and includes the most popular ML tools and frameworks to get you started right away. SageMaker Studio Lab automatically saves all your work, so you don’t have to restart between sessions. It's as simple as closing your computer and returning later. Machine learning development environment free of charge that offers computing, storage, security, and the ability to learn and experiment using ML. Integration with GitHub and preconfigured to work immediately with the most popular ML frameworks, tools, and libraries.

It works with Jupyter Notebooks or any other Python environment. Modelbit will deploy your model and all its dependencies to production by calling modelbi.deploy. Modelbit's ML models can be called from your warehouse just as easily as a SQL function. They can be called directly as a REST-endpoint from your product. Modelbit is backed up by your git repository. GitHub, GitLab or your own. Code review. CI/CD pipelines. PRs and merge request. Bring your entire git workflow into your Python ML models. Modelbit integrates seamlessly into Hex, DeepNote and Noteable. Modelbit lets you take your model directly from your cloud notebook to production. Tired of VPC configurations or IAM roles? Redeploy SageMaker models seamlessly to Modelbit. Modelbit's platform is available to you immediately with the models that you have already created.