AUTOMATION
Make the production process automated with a lightweight “on the edge” ML models, Inventory and Delivery management
Find better business models, new revenue streams with Machine Learning and Custom Artificial Intelligence Solutions like Virtual Assistants, Data-Driven Insights and Predictive Analytics.
Predictive Analytics
Predictive Maintenance
Anomaly Detection
Root Cause Analysis
Computer Vision
Image Processing
Video Analytics
NLP
Voice Recognition
Microsoft Azure ML
AWS ML
Google Cloud
IBM Watson
NumPy, Pandas, SciPy, sklearn
OpenCV, Rasterio, Keras, TensorFlow, PyTorch, mkl-dnn
NLTK, gensim, spaCy
Apache Spark MLlib
We will define: a problem, approach, data and business requirements, outcomes and next steps, brainstorm and present a proposed approach
Data Collection, Preparation, and Analysis; Feature Engineering. Model Selection and Training, Model Evaluation
Data Engineering: Collection, Integration, Preparation and Cleaning, Visualization and Analysis. Feature Engineering. Model Selection and Training, Parameters Tuning, Model Evaluation Model Deployment
Performance Monitoring and Debugging. Model Retraining
■ ~ 30000 topics a day processing to receive valuable business insights on the major industries: Finance, Healthcare, Investments to make confident decisions based on Intelligent, Data-Driven Insights
■ Manual data processing was reduced by 20%, which resulted in more than 240,000$ in annual savings
■ Reducing the number of missed events helped to find more precise and holistic information that attract more clients
■ Fully automated defects detection solution was developed
■ Input data were filtered to reduce false positives
■ Post-processing module to minimize false positive detections
■ Localizes defects with the dice score of 0.89255
■ 98.3% of accuracy in the classification of defects was achieved
■ Automate QA processes and reduce human labor