As an essential pre-deployment step, we optimize your model for computational efficiency and speed. Through targeted adjustments and specific optimizations, we enhance your model's performance and reduce latency. This process enables the model to deliver quick, accurate responses while saving valuable computational resources.
We deploy your machine learning / deep learning model as a web service that is accessible via HTTP requests. This means your model resides on a server, and upon receiving an API call with the necessary data, it processes this data and returns the predicted output. This method is versatile and can be integrated into virtually any software ecosystem.
We can integrate the ML/DL model into the backend of a web application, allowing your users to interact with the model through a graphical user interface (GUI). Deployment on web apps is your option if you want to democratize the use of machine learning / deep learning within your organization and reach non-tech-savvy users.
Deploying the ML/DL model within a mobile app is another option. We can embed the model directly into the app (on-device ML/DL) or set it up to make API calls to a server. On-device ML/DL offers offline functionality and low latency, while server-based models offer greater computational power and are easier to update.
Businesses that handle complex data tasks need strategic and efficient solutions. We deploy ML/DL models on your infrastructure or on the cloud computing services of your choice. This streamlines data management and offers low latencies and predictable pricing. With Tensorway, you can rely on seamless model deployment, regardless of data complexity.
We also deploy ML models on cloud-based serverless platforms like AWS Lambda or Google Cloud Functions. These platforms automatically scale to handle load fluctuations and only charge for the compute time used, making them a cost-effective option for models with inconsistent usage patterns.
— Python (with Flask and Django, FastAPI)
— Backend: Django, Flask, Ruby on Rails, Spring Boot
— Cross-Platform: React Native, Flutter
— SQL Databases: MySQL, PostgreSQL, Oracle Database
— NoSQL Databases: MongoDB
— Big Data: Apache Hadoop, Spark
— AWS Lambda
— Google Cloud Functions
— Microsoft Azure Functions
— Python (with TensorFlow, PyTorch, Scikit-learn, Keras)
— R (with its suite of packages for statistical analysis and ML)
— Serialization: Pickle, Joblib, ONNX
— Deployment: TorchServe, TensorFlow Serving, Kubeflow, MLflow
— Model optimization: ONNX-Runtime, Optimum
— Secure coding practices
— Data encryption techniques
— User access control management
— Version Control: Git
— Containerization: Docker
— CI/CD: Jenkins, GitLab CI/CD
We firmly believe that no AI that is meant to be accessible publicly can go without quality software, a mediator between the AI and users. The web app takes ages to load or constantly returns errors — people will likely use a weaker model but with a better application! What makes us so sure about it is Tensorway’s descent from custom software development company Anadea which delivered hundreds of solutions to all kinds of businesses. From our experience at Anadea, we know well what factors ensure the success of software for businesses. There’s really a lot to talk about, but we better leave it to our conversation!
Machine Learning, on the other hand, includes a range of algorithms and techniques that analyze and process data. These algorithms can be used for tasks such as classification, regression, clustering, and prediction and can be applied to various industries and applications.
Ultimately, the best approach will depend on the specific requirements of the task at hand.The best parameters of ML models are chosen under human supervision, meanwhile, DL models have more advanced optimization algorithms. ML model optimization involves selecting the best model parameters, whereas, in DL, only model hyperparameters are chosen, and then the model optimizes itself via a backpropagation algorithm.
Effective representations of visual, textual, or audio information allow deep learning models to be so effective in different tasks.
First of all, deploying your ML/DL model to production with Tensorway involves choosing the most suitable method based on your business needs.
API endpoint: This approach is versatile, enabling your model to process and return predicted outputs upon receiving API calls. It's great for diverse software ecosystems.
Web application: Ideal if you wish to make machine learning more accessible within your organization, especially for non-technical users, as it offers an intuitive graphical user interface.
Mobile application: For mobile-first businesses or applications with the need for offline functionality and low latency, deploying the model within a mobile app might be the best option.
Database: This method streamlines data management processes, perfect for businesses with complex data handling tasks or for accelerating predictions on large datasets.
Serverless platforms: Cost-effective for models with inconsistent usage patterns as they automatically scale and charge only for the compute time used.
Once the model is deployed, we continue to monitor its performance, handle updates, and manage scaling requirements, ensuring the model remains reliable and accessible.
We build software products to accommodate user growth by using technologies and architectures that adapt to increasing demands. At our AI software development solutions company, we also focus on creating systems that remain operative even in case of a component failure, providing uninterrupted service. Our team constructs software that integrates AI seamlessly, efficiently balancing loads and providing fault tolerance.