TensorFlow Development
We process complex parallel computations and build advanced neural network models with TensorFlow framework
TensorFlow is an open-source flexible framework, which makes the complex processes easy to visualize and optimize numerical analysis. TensorFlow Development makes deployment of complex ML applications faster, easier and flexible.
Our Expertise Around TensorFlow Tools and Libraries
CoLab
Write and Execute code on the free Jupyter notebook environment from your browser.
TensorBoard
It provides tools need for machine learning experimentation and visualization.
ML Perf
It is a broad ML suite for measuring the performance of ML hardware, software, and services.
TensorFlow Research Cloud
(TFRC) the program enables researchers to apply for access to a cluster of more than 1,000 Cloud Tensor Processing Units(TPUs).
Machine Learning Intermediate Representation (MLIR)
It unifies the infrastructure for high-performance ML models in TensorFlow.
Accelerated Linear Algebra
Domain-specific compiler for linear algebra that can accelerate TensorFlow models
TensorFlow Development Services
Machine Learning
We develop high-performance numerical computation across a variety of platforms.
Natural Language Processing
We leverage TensorFlow’s module functionality to handle responses as classifiers within a state machine.
Chatbot Development
We leverage TensorFlow’s sequence-to-sequence module functionality to handle the user’s response in the chatbot development.
Image Processing
We make use of AI-module and repurpose for image classification. We have successfully leveraged Image Processing for our AI IoT Tea Making Robot.
Complex Numerical Computations
We build Single Page Applications that are equipped to execute heavy numerical computations on a continuous basis making use of data flow graphs.
Outcome Predictions
Our programmers make use of large-scale linear models for data crunching on complex data sets to identify pricing and multiclass categorization for eCommerce business.
Our TensorFlow Development Process
We Are a Team of AI Developers With Expertise in TensorFlow Development
At LeewayHertz, we use TensorFlow for computation across machines and large data sets. With its better computational graph visualizations, seamless performance, we have designed complex data models at scale.
Our AI Application Portfolio
Hi Arya!
World’s First Robotic Tea Maker
Arya is the First Chai making robot having the capabilities of AI. It can detect a user’s face using computer vision and reply back with exact recipe name by predicting the user’s behavior using Machine Learning(ML). It uses Speech recognition and NLP to interact with the user to take the next order.
AttendanceCloud
AI-based Roster Management and Time-Tracking App
AttendanceCloud uses AI and facial recognition to manage time and attendance records of employees. We integrated the AI-based attendance tracking system with the client’s existing cloud-based human resource management.
AttendanceCloud
AI-based Roster Management and Time-Tracking App
AttendanceCloud uses AI and facial recognition to manage time and attendance records of employees. We integrated the AI-based attendance tracking system with the client’s existing cloud-based human resource management.
Big Brands Trust Us
FAQ for TensorFlow Development
What are the advantages of TensorFlow technology for businesses?
Following are some of the advantages of TensorFlow for businesses:
- Voice Recognition
Sound/Voice-based applications are one of the most known uses of TensorFlow technology. Neural networks can understand audio signals efficiently with the proper data feed. Using TensorFlow, it is possible to develop the applications, including voice recognition, sentiment analysis, flaw detection and voice search. - Text-based Applications
Text-based applications are also one of the popular business uses of TensorFlow. These applications include Fraud Detection (Finance and Insurance), Thereat Detection (Government, Social Media) and Sentimental Analysis (Social Media, CRM). - Time Series
TensorFlow Time Series algorithms are used to analyze time series for extracting meaningful statistics. They allow predicting non-specific periods to create alternative versions of the time series. The use case for Time Series is Recommendation. You might have heard of this use case from Facebook, Netflix and Amazon, where they analyze custom behavior and determine what the customer is likely to watch and purchase. - Image Recognition
Image Search, Motion Detection, Face Recognition, Photo Clustering and Machine Vision are mostly used in Healthcare, Aviation and Automotive Industries. Image Recognition aims to identify and recognize objects and people in images. Object Recognition Algorithms by TensorFlow identify and classify arbitrary objects within larger images. It is mainly used in engineering applications for identifying shapes for modeling purposes.Also, they can be used by social media channels for tagging photos, for example, Facebook’s Deep Face. Image Recognition has also started to expand in the healthcare industry, where TensorFlow algorithms can spot many patterns and process more information than human counterparts.
What tools do we use for TensorFlow Development?
To develop TensorFlow-based applications quickly and efficiently, we use the following set of tools:
- CoLab
CoLaboratory is a free Jupyter notebook environment that does not require any setup and runs in the cloud, enabling the execution of TensorFlow code in the browser with a single click. - What-If Tool
It is a tool for code-free probing of machine learning models used for model understanding, fairness and debugging. This tool is available in Jupyter and TensorBoard or Colab notebooks. - XLA (Accelerated Linear Algebra)
It is a domain-specific compiler for linear algebra, used to optimize TensorFlow computations. It helps improve speed, portability on mobile and server platforms and memory usage. - TensorBoard
TensorBoard provides the tooling and visualization required for experimentation of machine learning. Its main functionalities are: visualizing and tracking metrics, displaying images, profiling TensorFlow programs and viewing histograms of biases, weights or other tensors.
Where to look for TensorFlow developers?
If you are looking for TensorFlow developers for your project, you can either hire freelance developers available on websites like Toptal or Upwork, find a TensorFlow Development Company with a team of Tensor developers or build your team of in-house developers. As compared to hiring freelancers or an in-house team of developers, it is recommended to hire a third-party TensorFlow Development agency. We have explained why below:
- Though freelancers can be available at lower costs, it is not very easy to manage different freelancers. Also, they cannot be accountable for your project. They may also not provide you post-development services that include maintenance and upgrades.
- On the other hand, building an in-house team of developers is ideal for only large projects. It is not ideal to hire them for small to medium projects because managing the resources, their amenities and space could be expensive for your organization.
Therefore, it is good to work with a team of developers from a TensorFlow Development Company who are skilled in different technologies and frameworks.
Get Started Today
1. Contact Us
Fill out the contact form protected by NDA, book a calendar and schedule a Zoom Meeting with our experts.
2. Get a Consultation
Get on a call with our team to know the feasibility of your project idea.
3. Get a Cost Estimate
Based on the project requirements, we share a project proposal with budget and timeline estimates.
4. Project Kickoff
Once the project is signed, we bring together a team from a range of disciplines to kick start your project.
Hire TensorFlow Developers
Get on a call to discuss your project idea and requirements. We keep all information confidential.
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