eAxis Analytics

eAxis Analytics is available for a broad range of independent consulting and contracting services covering Big Data ETL pipelines, data processing, analytics, and modeling.

eAxis Analytics was founded by Michael Suesserman to develop novel AI models for automated stock analysis. Michael possesses a PhD in Electrical Engineering with a major in Digital Signal Processing that covers a broad range of advanced theoretical and applied mathematics. A passion for working with data underlies over a decade of experience processing and analyzing many types of structured and unstructured data with an emphasis on financial, ecommerce and biomedical fields. Complete details are available on LinkedIn:

linkedin.com/in/michael-suesserman-phd/

AWS Certification:

(verify)

Projects:

Recent projects focus on three general areas of Deep Learning:

Image Processing and Computer Vision: currently training a custom neural network to perform technical stock analysis by reading and interpreting images of stock charts as a means of predicting stock prices. Project utilizes OpenCV, CNN, Object Detection, Mask R-CNN, and Keypoint Detection.
Time Series Analysis: trained a Recurrent Neural Network (RNN) with LSTM to extract information from stock data for predicting prices. Also, built a Big Data ETL pipeline on AWS for ingesting and analyzing stocks in real-time. Project utilizes RNN with LSTM, Restricted Boltzmann Machines, Deep Belief Networks (DBNs) and AWS (Kinesis, Glue, Redshift & S3).
Natural Language Processing (NLP): analyzing public companies using NLP technologies for use in predicting stock prices. Project utilizes tokenization, NER, topic modeling, semantic analysis and sentiment analysis.

Contracting Services Include:

Data Engineering --> ETL, Data Pipelines and Data Lakes

AWS (Kinesis, EMR, Glue, Redshift & S3)
NoSQL (DynamoDb, mongoDB & Cassandra)
Apache Spark (Scala & Python)
Hadoop Ecosystem (HDFS, YARN, MapReduce, HBASE, …)
SQL (MySQL, Postgres and SQL Server)

Data Science:

Data Processing
Predictive Analytics and Machine Learning:
- Supervised --> regression and classification
- Unsupervised --> clustering

Deep Learning:

Supervised --> Artificial Neural Network (ANNs), Convolutional Neural Networks (CNNs), Faster R-CNN, Mask R-CNN, Recurrent Neural Networks (RNNs) with LSTM
Unsupervised --> Self-Organizing Maps, Boltzmann Machines, AutoEncoders, Deep Belief Networks (DBNs)

Artificial Intelligence:

Reinforcement Learning --> Deep Convolutional Q-Learning, A3C, LSTM-A3C
Natural Language Process (NLP) --> tokenization (POS, lemmatization, …), NER, topic modeling, Word2Vec, semantic analysis and sentiment analysis

Commonly Used Languages, Libraries and Platforms:

Primary Languages/Platforms (used daily): Python and Anaconda3 (including numpy, matplotlib, pandas, sklearn, tensorflow, keras & pytorch) on LAMP stack.
Other Languages/Platforms: ASP.NET/MVC, C#, C++, PHP, Node.js/jQuery and client technologies (JavaScript/JSON, HTML, CSS, XML)
Cloud Services: AWS including SageMaker and AI Services (advanced expertise) and Azure (basic experience)
Development Stacks: Microsoft and LAMP
Databases: SQL (MySQL, Postgres & MS SQL Server) and NoSQL (DynamoDb, mongoDB & Cassandra)
Microservices: Amazon AWS Lambda
Containers: Dockers (with Flask for web apps)
Visualization and Dashboards: matplotlib, Jupyter, Tableau and R-Studio
Computer Vision: OpenCV
NLP: Spacy, Gensim and NLTK

eAxis Analytics Web Sites

eAxis Analytics currently operates three separate sites all running in the cloud:

Main Site
(eAxis.com): this corporate site that summarizes our Data Science Consulting Services.
Serverless Dev Site
(dev.eAxis.com): a dev site that is used for developing and testing serverless microservices on AWS (Amazon Web Services). This entire site is created using static HTML that is globally available through the CloudFront edge network. Dynamic content is created using AWS Lambda and Lambda@Edge connected to the API Gateway.
Machine Learning Site
(ml.eAxis.com): a server-based site that is used for developing and testing machine learning, deep learning and AI models that are too complex to run as serverless apps. This site is scalable and highly available running on EC2 Linux servers with load balancing and auto scaling.

I’ll be adding a blog soon with lots of content including articles covering how I developed and deployed the above sites as well as detailing the public demos included on these sites.