TENSOR FLOW DEVELOPMENT COMPANY

Expand your AI and machine learning capabilities with nearshore TensorFlow talent.

Our TensorFlow development services power advanced machine learning and AI solutions. We quickly assemble skilled teams, allowing you to deliver high-performance, data-driven models with speed and efficiency.

Tensorflow development

+200 companies rely on

our 1% Top Talent

Tensor Flow Development we provide

We offer versatile TensorFlow development services, from building machine learning models to integrating AI solutions into your systems. Explore our key offerings below:

Custom Machine Learning Models

We build custom machine learning models using TensorFlow to help your business extract valuable insights from data. Our developers design and deploy tailored models for applications such as predictive analytics, classification, and anomaly detection.

Neural Network Development

Our team specializes in developing advanced neural networks using TensorFlow. We create deep learning models for tasks such as image recognition, natural language processing (NLP), and recommendation systems, helping your business harness the power of AI.

TensorFlow Model Integration

We integrate TensorFlow models into your existing applications and systems. Whether it’s integrating AI into mobile apps, web services, or cloud platforms, our developers ensure seamless model deployment and execution.

TensorFlow for Computer Vision

We create powerful computer vision solutions using TensorFlow. From object detection and image segmentation to facial recognition, our models enable businesses to automate visual data processing tasks with high accuracy.

Natural Language Processing (NLP)

Our TensorFlow NLP models enable businesses to understand and process human language. We develop solutions for tasks like sentiment analysis, language translation, chatbots, and speech recognition using TensorFlow's deep learning libraries.

TensorFlow Maintenance and Optimization

Keep your TensorFlow models optimized and running efficiently with our ongoing maintenance and support services. We ensure that your AI models perform reliably by monitoring, updating, and tuning them for maximum accuracy and efficiency.

Why Choose Xpertsoft for TensorFlow Development

Nearshore Expertise

Our software developers are primarily based in Portugal, ensuring smooth collaboration with EU-based clients. With real-time communication and bilingual professionals, we provide seamless support and development services.

Broad Technical Expertise

Beyond TensorFlow, our team brings expertise in AI, data science, and cloud integration. We offer end-to-end solutions for building and deploying machine learning models that drive business value.

Tailored AI Solutions

We provide flexible TensorFlow development services that align with your business goals. Whether you need a single machine learning expert or an entire AI team, we adapt to your needs and scale our involvement to fit your project requirements.

The TensorFlow Ecosystem We Used in Previous Work

Machine Learning and AI Models

Leverage TensorFlow’s powerful libraries for building AI models:

  • TensorFlow Core
  • TensorFlow Keras (for deep learning)
  • TensorFlow Extended (TFX) for model deployment
  • TensorFlow Lite (for mobile and IoT devices)

Data Preprocessing and Transformation

Prepare and preprocess data efficiently using these tools:

  • TensorFlow Data API
  • Pandas (for data manipulation)
  • NumPy (for numerical computation)
  • TensorFlow Datasets (ready-to-use datasets)

Neural Networks and Deep Learning

Build complex neural networks using TensorFlow’s deep learning libraries:

  • CNNs (Convolutional Neural Networks for image processing)
  • RNNs and LSTMs (Recurrent Neural Networks for sequence modeling)
  • GANs (Generative Adversarial Networks for data generation)
  • TensorFlow Hub (for reusable model components)

Natural Language Processing (NLP)

Implement state-of-the-art NLP models with these TensorFlow tools:

  • TensorFlow Text (for text processing)
  • BERT (Bidirectional Encoder Representations from Transformers)
  • Word2Vec (for word embeddings)
  • Transformer models (for language translation and understanding)

Cloud and Edge Deployment

Deploy machine learning models to the cloud and edge devices:

  • TensorFlow Serving (for model serving in production)
  • TensorFlow Lite (for deploying on mobile and IoT)
  • Google Cloud AI Platform (for cloud-based model training and serving)
  • AWS SageMaker (for TensorFlow model deployment on AWS)

Model Evaluation and Optimization

Ensure high-performing models with these evaluation and optimization tools:

  • TensorBoard (for model visualization and performance tracking)
  • AutoML (for model optimization and hyperparameter tuning)
  • TensorFlow Model Analysis (for model validation and fairness)

Accelerate your roadmap with an experienced TensorFlow development company.

Key Facts to Know About TensorFlow Development

  • Benefits of using TensorFlow
  • 1. Scalable Machine Learning Framework

    TensorFlow is designed to scale, making it suitable for everything from small-scale models to large, complex neural networks. It can be deployed across multiple devices, from cloud servers to mobile devices, ensuring flexibility and scalability for a range of applications.

    2. End-to-End AI Solutions

    TensorFlow offers an end-to-end platform for machine learning, from data preprocessing and model building to deployment and monitoring. This allows businesses to create, train, and deploy AI models in one unified ecosystem, reducing the complexity of development.

    3. Support for Deep Learning and Neural Networks

    TensorFlow is widely used for building deep learning models, including neural networks for image recognition, natural language processing, and other AI applications. Its support for GPU acceleration allows for faster model training and execution.

  • TensorFlow is primarily used for
  • TensorFlow is primarily used for building and deploying machine learning and deep learning models. It’s widely adopted in industries such as healthcare, finance, e-commerce, and autonomous systems for applications like image classification, predictive analytics, and natural language processing.

  • Reasons for TensorFlow's popularity
    • Extensive Community and Ecosystem: TensorFlow benefits from a large developer community and extensive resources, including pre-trained models, libraries, and educational materials that accelerate AI development.
    • Pre-Built and Reusable Models: TensorFlow Hub provides pre-built models that can be reused and fine-tuned, enabling faster development of machine learning applications without starting from scratch.
    • Cross-Platform Compatibility: TensorFlow supports deployment on a wide range of platforms, including cloud servers, mobile devices (with TensorFlow Lite), and even edge devices, making it ideal for a variety of use cases.
    • Robust Tooling for Model Monitoring and Debugging: TensorFlow offers built-in tools like TensorBoard for monitoring and debugging models, ensuring transparency and ease in fine-tuning model performance.
    • Integration with Major Cloud Providers: TensorFlow integrates seamlessly with major cloud platforms like Google Cloud, AWS, and Azure, enabling businesses to scale their AI applications easily and deploy models to production efficiently.
    • Real-Time Inference: TensorFlow’s architecture supports real-time inference, allowing businesses to deploy AI models for real-time decision-making, such as fraud detection, personalized recommendations, and real-time analytics.
  • Useful Links

1. Scalable Machine Learning Framework

TensorFlow is designed to scale, making it suitable for everything from small-scale models to large, complex neural networks. It can be deployed across multiple devices, from cloud servers to mobile devices, ensuring flexibility and scalability for a range of applications.

2. End-to-End AI Solutions

TensorFlow offers an end-to-end platform for machine learning, from data preprocessing and model building to deployment and monitoring. This allows businesses to create, train, and deploy AI models in one unified ecosystem, reducing the complexity of development.

3. Support for Deep Learning and Neural Networks

TensorFlow is widely used for building deep learning models, including neural networks for image recognition, natural language processing, and other AI applications. Its support for GPU acceleration allows for faster model training and execution.

TensorFlow is primarily used for building and deploying machine learning and deep learning models. It’s widely adopted in industries such as healthcare, finance, e-commerce, and autonomous systems for applications like image classification, predictive analytics, and natural language processing.

  • Extensive Community and Ecosystem: TensorFlow benefits from a large developer community and extensive resources, including pre-trained models, libraries, and educational materials that accelerate AI development.
  • Pre-Built and Reusable Models: TensorFlow Hub provides pre-built models that can be reused and fine-tuned, enabling faster development of machine learning applications without starting from scratch.
  • Cross-Platform Compatibility: TensorFlow supports deployment on a wide range of platforms, including cloud servers, mobile devices (with TensorFlow Lite), and even edge devices, making it ideal for a variety of use cases.
  • Robust Tooling for Model Monitoring and Debugging: TensorFlow offers built-in tools like TensorBoard for monitoring and debugging models, ensuring transparency and ease in fine-tuning model performance.
  • Integration with Major Cloud Providers: TensorFlow integrates seamlessly with major cloud platforms like Google Cloud, AWS, and Azure, enabling businesses to scale their AI applications easily and deploy models to production efficiently.
  • Real-Time Inference: TensorFlow’s architecture supports real-time inference, allowing businesses to deploy AI models for real-time decision-making, such as fraud detection, personalized recommendations, and real-time analytics.

Add top 1% devs to
your in-house teams

Tap into the expertise of our top 1%  developers. Staff augmentation lets you boost your in-house teams with specialized experts. Expedite timelines without sacrificing output quality.

Here’s how we augment your team

STEP 1

Discovery Call

Share your requirements, budget, and necessary skill sets. We will draft a working timeline and select top developers for your team.

STEP 2

Assembling Your Team

Withindays, we’ll find suitable developers that fit your requirements. We ensure they have the right technical expertise and would be a great cultural fit for your team.

STEP 3

Onboarding and Scaling

After onboarding them, our developers will integrate with your team. Scale your engagement as needed – we’re happy to accommodate your demands.

Get an
entire Team

Looking to bring on more than just a few .NET developers? We’ll assemble a complete crew to support you. Whether it’s full-cycle front and back-end web development, QA, DevOps, UX/UI, or something else. Monitor the team’s performance and manage them as you see fit.

Here’s how you can get a dedicated team

nearshoring a development team

STEP 1

Discovery Call

We’ll learn about your business, organization structure, objectives, budget, timelines, and resource requirements. Then, we can start identifying the ideal talent for you.

STEP 2

Team Assembly and Integration

Once we assemble your dedicated team, we’ll ensure a smooth transition as they integrate with your organization.

STEP 3

Project Kickoff

After onboarding, your team is at your disposal. You’ve now acquired the resources you need without the hassle and high cost that usually comes with recruitment

Looking for TensorFlow development at your organization?

See how we can help.