Best Artificial Intelligence Software for TensorFlow - Page 5
View:
Compare the Top Artificial Intelligence Software that integrates with TensorFlow as of June 2025 - Page 5
Sort By:
This a list of Artificial Intelligence software that integrates with TensorFlow. Use the filters on the left to add additional filters for products that have integrations with TensorFlow. View the products that work with TensorFlow in the table below.
-
1
EasyODM
EasyODM
Our automated visual quality inspection software optimizes efficiency, minimizes defects, and significantly reduces production costs, resulting in substantial annual savings for our valued clients. EasyODM combines the power of computer vision and machine learning to revolutionize quality inspection, enabling machines to unlock the cognitive capabilities of AI and transform data into actionable insights. EasyODM combines the power of computer vision and machine learning to revolutionize quality inspection, enabling machines to unlock the cognitive capabilities of AI and transform data into actionable insights. Our automated visual quality inspection software optimizes efficiency, minimizes defects, and significantly reduces production costs, resulting in substantial annual savings for our valued clients. -
2
Universal Sentence Encoder
Tensorflow
The Universal Sentence Encoder (USE) encodes text into high-dimensional vectors that can be utilized for tasks such as text classification, semantic similarity, and clustering. It offers two model variants: one based on the Transformer architecture and another on Deep Averaging Network (DAN), allowing a balance between accuracy and computational efficiency. The Transformer-based model captures context-sensitive embeddings by processing the entire input sequence simultaneously, while the DAN-based model computes embeddings by averaging word embeddings, followed by a feedforward neural network. These embeddings facilitate efficient semantic similarity calculations and enhance performance on downstream tasks with minimal supervised training data. The USE is accessible via TensorFlow Hub, enabling seamless integration into various applications. -
3
Intel Open Edge Platform
Intel
The Intel Open Edge Platform simplifies the development, deployment, and scaling of AI and edge computing solutions on standard hardware with cloud-like efficiency. It provides a curated set of components and workflows that accelerate AI model creation, optimization, and application development. From vision models to generative AI and large language models (LLM), the platform offers tools to streamline model training and inference. By integrating Intel’s OpenVINO toolkit, it ensures enhanced performance on Intel CPUs, GPUs, and VPUs, allowing organizations to bring AI applications to the edge with ease. -
4
JAX
JAX
JAX is a Python library designed for high-performance numerical computing and machine learning research. It offers a NumPy-like API, facilitating seamless adoption for those familiar with NumPy. Key features of JAX include automatic differentiation, just-in-time compilation, vectorization, and parallelization, all optimized for execution on CPUs, GPUs, and TPUs. These capabilities enable efficient computation for complex mathematical functions and large-scale machine-learning models. JAX also integrates with various libraries within its ecosystem, such as Flax for neural networks and Optax for optimization tasks. Comprehensive documentation, including tutorials and user guides, is available to assist users in leveraging JAX's full potential. -
5
LaunchX
Nota AI
Optimized AI is ready to launch on-device and allows you to deploy your AI models on actual devices. With LaunchX automation, you can simplify conversion and effortlessly measure performance on target devices. Customize the AI platform to meet your hardware specifications. Enable seamless AI model deployment with a tailored software stack. Nota’s AI technology empowers intelligent transportation systems, facial recognition, and security and surveillance. The company’s solutions include a driver monitoring system, driver authentication, and smart access control system. Nota‘s current projects cover a wide range of industries including construction, mobility, security, smart home, and healthcare. Nota’s partnership with top-tier global market leaders including Nvidia, Intel, and ARM has helped accelerate its entry into the global market. -
6
Clore.ai
Clore.ai
Clore.ai is a decentralized platform that revolutionizes GPU leasing by connecting server owners with renters through a peer-to-peer marketplace. It offers flexible, cost-effective access to high-performance GPUs for tasks such as AI development, scientific research, and cryptocurrency mining. Users can choose between on-demand leasing, which ensures uninterrupted computing power, and spot leasing, which allows for potential interruptions at a lower cost. It utilizes Clore Coin (CLORE), an L1 Proof of Work cryptocurrency, to facilitate transactions and reward participants, with 40% of block rewards directed to GPU hosts. This structure enables hosts to earn additional income beyond rental fees, enhancing the platform's appeal. Clore.ai's Proof of Holding (PoH) system incentivizes users to hold CLORE coins, offering benefits like reduced fees and increased earnings. It supports a wide range of applications, including AI model training, scientific simulations, etc. -
7
TF-Agents
Tensorflow
TensorFlow Agents (TF-Agents) is a comprehensive library designed for reinforcement learning in TensorFlow. It simplifies the design, implementation, and testing of new RL algorithms by providing well-tested modular components that can be modified and extended. TF-Agents enables fast code iteration with good test integration and benchmarking. It includes a variety of agents such as DQN, PPO, REINFORCE, SAC, and TD3, each with their respective networks and policies. It also offers tools for building custom environments, policies, and networks, facilitating the creation of complex RL pipelines. TF-Agents supports both Python and TensorFlow environments, allowing for flexibility in development and deployment. It is compatible with TensorFlow 2.x and provides tutorials and guides to help users get started with training agents on standard environments like CartPole. -
8
SiMa
SiMa
SiMa offers a software-centric, embedded edge machine learning system-on-chip (MLSoC) platform that delivers high-performance, low-power AI solutions for various applications. The MLSoC integrates multiple modalities, including text, image, audio, video, and haptic inputs, performing complex ML inference and presenting outputs in any modality. It supports a wide range of frameworks (e.g., TensorFlow, PyTorch, ONNX) and can compile over 250 models, providing customers with an effortless experience and world-class performance-per-watt results. Complementing the hardware, SiMa.ai is designed for complete ML stack application development. It supports any ML workflow customers plan to deploy on the edge without compromising performance and ease of use. Palette's integrated ML compiler accepts any model from any neural network framework. -
9
Botify.cloud
Botify.cloud
Botify.cloud is an innovative platform designed to streamline and simplify cryptocurrency automation through a certified, all-in-one AI agent marketplace. With Botify.cloud, users can explore a diverse range of agent categories, including trading, volume management, social media, and utility agents. Our instant agent creation tool allows users to customize agents to their needs quickly and easily. It offers features such as agent creation, selling agents on the marketplace, Botify certification for every agent, diverse agent categories, and easy editing of agents' names and profiles. Users can also save their favorite agents for later use. For every agent that is sold, a token is created, and basically, in any transaction on the platform, users earn rewards. Building an agent is straightforward: simply choose a category, fill in the required fields, choose a large language model, and decide the temperature of your agent. -
10
TensorWave
TensorWave
TensorWave is an AI and high-performance computing (HPC) cloud platform purpose-built for performance, powered exclusively by AMD Instinct Series GPUs. It delivers high-bandwidth, memory-optimized infrastructure that scales with your most demanding models, training, or inference. TensorWave offers access to AMD’s top-tier GPUs within seconds, including the MI300X and MI325X accelerators, which feature industry-leading memory capacity and bandwidth, with up to 256GB of HBM3E supporting 6.0TB/s. TensorWave's architecture includes UEC-ready capabilities that optimize the next generation of Ethernet for AI and HPC networking, and direct liquid cooling that delivers exceptional total cost of ownership with up to 51% data center energy cost savings. TensorWave provides high-speed network storage, ensuring game-changing performance, security, and scalability for AI pipelines. It offers plug-and-play compatibility with a wide range of tools and platforms, supporting models, libraries, etc. -
11
NVIDIA DeepStream SDK
NVIDIA
NVIDIA's DeepStream SDK is a comprehensive streaming analytics toolkit based on GStreamer, designed for AI-based multi-sensor processing, including video, audio, and image understanding. It enables developers to create stream-processing pipelines that incorporate neural networks and complex tasks like tracking, video encoding/decoding, and rendering, facilitating real-time analytics on various data types. DeepStream is integral to NVIDIA Metropolis, a platform for building end-to-end services that transform pixel and sensor data into actionable insights. The SDK offers a powerful and flexible environment suitable for a wide range of industries, supporting multiple programming options such as C/C++, Python, and Graph Composer's intuitive UI. It allows for real-time insights by understanding rich, multi-modal sensor data at the edge and supports managed AI services through deployment in cloud-native containers orchestrated with Kubernetes. -
12
Qualcomm Cloud AI SDK
Qualcomm
The Qualcomm Cloud AI SDK is a comprehensive software suite designed to optimize trained deep learning models for high-performance inference on Qualcomm Cloud AI 100 accelerators. It supports a wide range of AI frameworks, including TensorFlow, PyTorch, and ONNX, enabling developers to compile, optimize, and execute models efficiently. The SDK provides tools for model onboarding, tuning, and deployment, facilitating end-to-end workflows from model preparation to production deployment. Additionally, it offers resources such as model recipes, tutorials, and code samples to assist developers in accelerating AI development. It ensures seamless integration with existing systems, allowing for scalable and efficient AI inference in cloud environments. By leveraging the Cloud AI SDK, developers can achieve enhanced performance and efficiency in their AI applications. -
13
NVIDIA NGC
NVIDIA
NVIDIA GPU Cloud (NGC) is a GPU-accelerated cloud platform optimized for deep learning and scientific computing. NGC manages a catalog of fully integrated and optimized deep learning framework containers that take full advantage of NVIDIA GPUs in both single GPU and multi-GPU configurations. NVIDIA train, adapt, and optimize (TAO) is an AI-model-adaptation platform that simplifies and accelerates the creation of enterprise AI applications and services. By fine-tuning pre-trained models with custom data through a UI-based, guided workflow, enterprises can produce highly accurate models in hours rather than months, eliminating the need for large training runs and deep AI expertise. Looking to get started with containers and models on NGC? This is the place to start. Private Registries from NGC allow you to secure, manage, and deploy your own assets to accelerate your journey to AI. -
14
Snorkel AI
Snorkel AI
AI today is blocked by lack of labeled data, not models. Unblock AI with the first data-centric AI development platform powered by a programmatic approach. Snorkel AI is leading the shift from model-centric to data-centric AI development with its unique programmatic approach. Save time and costs by replacing manual labeling with rapid, programmatic labeling. Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets. Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data. Version and audit data like code, leading to more responsive and ethical deployments. Incorporate subject matter experts' knowledge by collaborating around a common interface, the data needed to train models. Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators. -
15
Unremot
Unremot
Unremot is a go-to place for anyone aspiring to build an AI product - with 120+ pre-built APIs, you can build and launch AI products 2X faster, at 1/3rd cost. Even, some of the most complicated AI product APIs take less than a few minutes to deploy and launch, with minimal code or even no-code. Choose an AI API that you want to integrate to your product from 120+ APIs we have on Unremot. Provide your API private key to authenticate Unremot to access the API. Use unremot unique URL to connect the product API - the whole process takes only minutes, instead of days and weeks. -
16
Qualcomm AI Hub
Qualcomm
The Qualcomm AI Hub is a resource portal for developers aiming to build and deploy AI applications optimized for Qualcomm chipsets. With a library of pre-trained models, development tools, and platform-specific SDKs, it enables high-performance, low-power AI processing across smartphones, wearables, and edge devices. -
17
Lambda GPU Cloud
Lambda
Train the most demanding AI, ML, and Deep Learning models. Scale from a single machine to an entire fleet of VMs with a few clicks. Start or scale up your Deep Learning project with Lambda Cloud. Get started quickly, save on compute costs, and easily scale to hundreds of GPUs. Every VM comes preinstalled with the latest version of Lambda Stack, which includes major deep learning frameworks and CUDA® drivers. In seconds, access a dedicated Jupyter Notebook development environment for each machine directly from the cloud dashboard. For direct access, connect via the Web Terminal in the dashboard or use SSH directly with one of your provided SSH keys. By building compute infrastructure at scale for the unique requirements of deep learning researchers, Lambda can pass on significant savings. Benefit from the flexibility of using cloud computing without paying a fortune in on-demand pricing when workloads rapidly increase.Starting Price: $1.25 per hour