AI, Machine Learning

Data Engineering Services for AI projects

At QData, we are proficient in creating data platforms both in-house (on-prem) and in cloud environments using the appropriate technologies for the various data types, analytical requirements, and business processes. We are familiar with the specific needs of analytic workloads and how they differ from the operational workloads that most information systems are designed to handle. We also know what the best technologies are for storing and manipulating data.

Example methods and projects:

Computer Vision (CV):

● Image Classification

● Object Detection (YoLo)

● Instance segmentation / Semantic Segmentation (Mask R-CNN)

● Optical Character Recognition (OCR)

Natural Language Processing (NLP):

● Text classification (including Sentiment Analysis, Aspect-Based Sentiment Analysis)

● Named Entity Recognition (NER, automatic extraction of relevant entities, like names, locations, dates, etc)

● Chat-Bots (Deep Learning or Rule-Based chat-bots for various domains)classification and regression tasks,  time-series prediction (ARIMA, …)

and many others…

Data platform engineering

Our highly skilled data engineers have expertise in the technologies commonly used in both cloud and on-premises data architectures.

We have the capability to create a data architecture from scratch or enhance an existing data platform with additional features, such as designing data storage layers (data lakes and warehouses), implementing data pipelining (ETL jobs or advanced batch data processing), or integrating analytical components (BI tools and deploying AI models).

Application Technology and programming languages
  • Health - Narrative models, classification, and statistical analysis.
  • WebCrawlers - NLP based Search Engines
  • IT Security - Classifier models for malicious commands, Behaviour analysis, and prediction.
  • Robotics - Quadcopters, Modeling, Simulation, Reinforcement Learning, Embedded Software, Computer Vision
  • Math - Solver Warmup Routines based on Machine Learning Models


Frameworks: SpaCy, HuggingFace, Transformers, NLTK, Annotation platforms: Doccano, ProdiGy, Clarifai Architectures: BERT, LongFormer, GPT-3


Frameworks: PyTorch, TensorFlow, Keras, DarkNet, Detectron, Detectron2, Annotation platforms: CVAT, Clarifai Domains: Medical Imaging, CCTV (surveillance), NFT, LegalTech (document analysis)

We have Technical skills:

R, Python, Javascript, Go, Java, Spring Boot, Gradle, Node.js, Angular, Tensorflow, Keras, NumPy, Serverless Frameworks, Docker, Terraform, Ansible, Kubernetes, Jenkins, Spinnaker, GitOps, DataOps, AWS. GCP, Azure, JAMstack, GraphQL, MEAN Stack, React, Redux, ES6, Express, MongoDB, Redis, Linux, Etcd, Consul, Kinesis, Redshift, DynamoDB, Spark, Hadoop, Kafka, PostgreSQL, MySQL, ELK, Sass, Webpack, Gulp, Git, Ethereum, Solidity, OpenZeppelin, Truffle, Flask, and Django.

Estimate my project

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