Best AI Development Platforms for Mac of 2025

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

Cloobot X, a Next Gen No-Code Platform for Enterprises with in-house IT Teams, offers the following value proposition: Overcome the limitations of existing No Code Platforms (Scalability and Vendor Lock-In, etc.) 2. Support problem solvers such as consultants, domain experts and product managers at all stages of the Software Development Lifecycle. Instantly deliverables such as Prototypes (in a programming language you choose), MVPs (in a chosen programming language), Workflow Apps and Codebases (in whichever programming languages you choose) can reduce the time to delivery 10x. 4. Enable Enterprises adapt Gen AI and address concerns (Platform can run on custom LLMs; code is generated using proprietary algorithm, not by Code-based LLMs).

TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

Faros AI combines all your operational data from multiple sources and enhances them with machine learning signals. The Faros AI Engineering Operations Platform allows you to harness this data so you can accelerate productivity, and better manager your engineering operations. With Faros AI, engineering leaders can scale their operations in a more data-informed way — using data to identify bottlenecks, measure progress towards organizational goals, better support teams with the right resources, and accurately assess the impact of interventions over time. DORA Metrics come standard in Faros AI, and the platform is extensible to allow organizations to build their own custom dashboards and metrics so they can get deep insights into their engineering operations and take intelligent action in a data-driven manner. Leading organizations including Box, Coursera, GoFundMe, Astronomer, Salesforce, etc. trust Faros AI as their engops platform of choice.

SuperAGI SuperCoder combines AI-native development platform & AI agents in order to enable fully autonomous software creation starting with the python programming language & frameworks. SuperCoder 2.0 leverages Large Action Models (LAMs) and LLMs fine-tuned to python code creation leading to one-shot or few-shot python functional programming with significantly higher accuracy on SWE-bench and Codebench SuperCoder 2.0 is an autonomous system that combines software guardrails for Flask & Django development frameworks with SuperAGI's Generally Intelligent Developer Agents in order to deliver complex real-world software systems SuperCoder 2.0 integrates deeply with existing developer stacks such as Jira or Github, Jenkins, CSPs, and QA solutions like BrowserStack/Selenium Clouds, to ensure a seamless experience in software development.

CORE is a high performance computing platform that can be used for a variety of applications. CORE is easy to use with its point-and-click interface. You can run the most complex applications. CORE provides unlimited computing power on-demand. Cloud computing is available without the high-cost. CORE for teams offers powerful tools that allow you to sort, filter, create, connect, and create users, machines, networks, and machines. With an intuitive and simple GUI, it's easier than ever to see all of your infrastructure from one place. It is easy to add Active Directory integration or VPN through our simple but powerful management console. It's now possible to do things that used to take days, or even weeks. Even complex network configurations can be managed with just a few clicks.

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.

Exspanse simplifies the path between development and business value. Build, train and rapidly deploy powerful machine-learning models using a single interface that scales with your business. Exspanse Notebook allows you to train, tune and prototype models with the help from high-powered GPUs, processors & AI code assistant. Rapid deploy allows you to go beyond training and modeling by deploying models as APIs directly from an Exspanse Notebook. Clone and publish unique AI project to DeepSpace AI Marketplace to advance the AI Community. Power, efficiency and collaboration all in one platform. You can maximize your impact as a data scientist working alone. Our integrated platform allows you to manage and accelerate the AI development process. Turn your innovative ideas quickly and efficiently into working models. You can seamlessly transition from building AI solutions to deploying them, without needing extensive DevOps expertise.

DeepSpeed is a deep learning optimization library that is open source for PyTorch. It is designed to reduce memory and computing power, and to train large distributed model with better parallelism using existing computer hardware. DeepSpeed is optimized to provide high throughput and low latency training. DeepSpeed can train DL-models with more than 100 billion parameters using the current generation GPU clusters. It can also train as many as 13 billion parameters on a single GPU. DeepSpeed, developed by Microsoft, aims to provide distributed training for large models. It's built using PyTorch which is a data parallelism specialist.

Start using large language models in your locality.

LM-Kit.NET, a cutting edge high-level inference toolkit, is designed to bring the advanced capabilities Large Language Models into the C# ecosystem. LM-Kit.NET is a powerful Generative AI toolkit that's tailored for developers who work within.NET. It makes it easier than ever before to integrate AI functionality into your applications. The SDK offers a wide range of AI features to cater to different industries. Text completion, Natural Language Processing, content retrieval and summarization, text enrichment, language translation are just a few of the many features. Whether you want to automate content creation or build intelligent data retrieval system, LM Kit.NET provides the flexibility and performance to accelerate your project.

Composio is a platform for integration that enhances AI agents and Large Language Models by providing seamless connections with over 150 tools. It supports a variety of agentic frameworks, LLM providers and function calling for efficient task completion. Composio provides a wide range of tools including GitHub and Salesforce, file management and code execution environments. This allows AI agents to perform a variety of actions and subscribe to different triggers. The platform offers managed authentication that allows users to manage authentication processes for users and agents through a central dashboard. Composio's core features include a developer first integration approach, built in authentication management, and an expanding catalog with over 90 ready to connect tools. It also includes a 30% reliability increase through simplified JSON structure and improved error handling.

The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions.

AlwaysAI offers developers a simple and flexible way for them to create, train, and deploy computer-vision applications to a wide range of IoT devices. You can choose from a variety of deep learning models, or upload your own. Our flexible and customizable APIs make it easy to quickly enable core computer vision services. You can quickly prototype, test, and iterate using a variety camera-enabled ARM32, ARM64, and x86 devices. Identify objects in an image using their names or classifications. Identify and count objects in real-time video feeds. Follow the same object through a series of frames. To count or track faces or complete bodies in a scene, locate them. Identify and draw borders around objects. You can separate the key objects from the background visuals in an image. Determine human body postures, fall detection, and emotions. To train an object detection model, use our model training toolkit. You can create a model that is tailored to your particular use-case.

The Intel Distribution of OpenVINO makes it easy to adopt and maintain your code. Open Model Zoo offers optimized, pre-trained models. Model Optimizer API parameters make conversions easier and prepare them for inferencing. The runtime (inference engines) allows you tune for performance by compiling an optimized network and managing inference operations across specific devices. It auto-optimizes by device discovery, load balancencing, inferencing parallelism across CPU and GPU, and many other functions. You can deploy the same application to multiple host processors and accelerators (CPUs. GPUs. VPUs.) and environments (on-premise or in the browser).

RapidCanvas is a no-code AI platform that helps businesses go from idea to live AI solution within hours. Our AutoAI technology automates the AI build and deploy process, reducing time to value by 90% or more. We work with you to understand your needs and create out-of-the-box AI solutions that are tailored to your specific business requirements. Our data science experts are available to help you optimize the results and ensure that you are satisfied with the performance of your AI solution. RapidCanvas is used by leaders in the Financial Services, Retail, Renewable Energy, and Manufacturing industries. We are committed to helping businesses of all sizes get the most out of AI.

Open Agent Studio is more than just another copilot. It's a co-pilot builder with no code that allows solutions that are not possible in any other RPA tool today. We believe that other tools will copy our idea, and so we are giving our customers a head start in the next few months. They can target markets previously untapped by AI using their deep industry insights. Subscribers can access a 4-week free course that teaches them how to evaluate product concepts and launch a customized agent with a white label enterprise-grade. You can easily build agents by recording your keyboard and mice actions, such as scraping data and detecting a start node. The agent recorder allows you to build generalized agents in as little time as possible. Record once and then share it across your organization for future-proof agents.