Machine Learning Services

Natural language processing (NLP) lets you extract, organize, and understand information from written text. NLP solutions are especially useful when faced with information overload.

NLP can be used to improve text grammatically, automatically paraphrase, deliver smart replies, and translate content into multiple languages.

NLP techniques we apply include:

• Question answering
• Named entity recognition (NER)
• Abstractive or extractive summarization
• Sentiment analysis
• Semantic similarity search

Modern deep learning methods allow you to understand the content’s overall context with amazing accuracy. The efficiency of searches in your system is improved when you can quickly match a phrase’s context, not just its literal use.

Our machine learning engineers build customized systems for the classification, detection, and location of objects on images or video. 

Image segmentation and analysis can be used to automatically monitor an environment; tracking the condition of power lines, pipelines, nature, pollution, etc. Based on a detailed analysis of the results, a solution can be built that quickly responds to the current conditions.

Visual searches can be integrated into your system to allow users to look up information based on images. The search function presents users with visually similar results. This can be used with a diverse range of images including clothing, furniture, machine parts, and even lost animals.

We build customized computer vision solutions using a range of techniques—from standard numerical methods to deep learning. Our models are deployed in the cloud or on a mobile device.

A good product recommender system can lead to happier customers and more sales. But, relying only on sales history doesn’t leverage your data’s full potential.

When creating recommender systems, our teams follow an in-depth analysis of product features, uses, and correlation with factors related to user characteristics. This allows you to build a better training set for data-learning algorithms which leads to more effective recommendations.

We use an experimental and results-based approach. Results are measurable based on sales results and user satisfaction. Our specialists can help you develop and improve the process by continuing to measure the quality of recommendations and adding relevant analytics.

Develop repeatable and reliable processes for your machine learning models.

We can help you automate training processes, implement standards, and support you with model monitoring and fine-tuning through:

• Collection and transformation of data
• Automatic quality assessment
• Training or re-training of models
• Evaluation of test sets 
• Automatic implementation in production.

Continuous monitoring ensures the quality of the predictions served. In quickly changing environments, monitoring is essential to guarantee efficient results.

To ensure the well-organized monitoring of a model, it is important to optimize the network (through pruning, quantization, distillation model, etc.), choose the way of serving (GPU vs CPU), and develop a scaling methodology. Our ML Ops specialists will be there to offer continued support and maintenance help during deployment and beyond.