neural_net

Machine & Deep Learning

When you want to find a pattern which you're not sure even exists, the horsepower - CPU/GPU/TPU, to make this happen, is now accessible to everyone.

A short example, is Ambulance placement. Using DL to predict where emergencies are most likely to happen, placeing paramedics as close to predicted emergencies as possible.

A monetary example, is targeted advertising/promotion/marketing. Allowing Deep Learning to discover new demographics, discover what makes customers/demographics enage, and then execute. Once a successful process is built, you can use it as little, or as much, as you want. Revenue down for the quarter ahead of the ER? Use Deep Learning insights to aggressively flash-sale with with target promotions to specific demographics, or even specific users, with maximum profitablity.

Save the quarter, save the year, save the company, impress investors and the board,
and, take credit.

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ai-mind

Artificial Intelligence

When developing a process which is considering more than traditional structured/unstructured data, the use of Artificial Intelligence processes are the typical prescription. Specifically, tasks which are easy for humans, but hard for computers.

Do you want to build an X-Games trick skiing robot? Autonomous vehicles might be an obvious example, but how about an android with facial actuation which can interact in a hospital's neonatal unit, to not only interact & comfort newborns, but also detect their facial sentiment & document and alert nurses if necessary. Perhaps we're not ready for this intimate of interaction with machines.

But, following this concept, many endangered species imprint after birth or hatch, and as such, they are fed with puppets which mimic their species, in order to most easily identify with their species, when released back into the wild. Currently this is puppeteering is most often performed manually...But what if we trained an Artificially Intelligent animatronic, which feeds, plays, and socializes these endangered species? You could reduce the human workload, scale the recovery, reduce the cost, reduce risk of the species imprinting on a human, better prepare them for release into the wild, and quite possibly, literally save an endangered species from extinction.

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cdc_info

Analysis & Analytics

Smart monitoring, anomaly detection, trend analysis, predicting the future,
building -> leading -> controlling the future.

Combining multiple concepts in the field of Data Science, Machine Learning, Artificial Intelligence, General intelligence, Deep Learning, Supervised/Semi/Unsupervised/Adversarial Learning, Natural Language Processing, the tools are available & accessible. What remains is to build the process.

Will it be for Good? Will it be for Evil?
Will it be for Money? Will it be for Safety?
This, is up to you.

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Meet The X-Team

We are a lean team of mean (kind) data scientists and data engineers. We take our work seriously and are heavily seasoned; but we would probably still taste good with a little salted butter.

Sarah Herberger - Data Scientist

Sarah Herberger

Endangered Animal Statistics, Medical Prediction, Nuclear Analytics, Microsecond Sensor Anomaly Detection, Sarah Herberger is The Brain behind industry disrupting data science. Yes, we said disrupting, and we are 9001% certain about this statement.
We'd love the chance to talk about what we did, how we did it, and the value we might add for you.

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Baolin Liu - Data Scientist

Baolin Liu

Predictive Maintenance, Anomaly Detection, Object Recognition, Baolin The Data Shaolin is also our resident Data Scientist and also two-time university Hackathon 1st prize winner, along with our mutual friend, Eugene Huang.


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Rob Kelly - Data Engineer

Rob Kelly

The most intense on The X-Team, you can't stop this Data Train. Freelance by choice, building clients' success each stop, Rob The Train consistently pushes data engineering beyond conventional boundaries with a unique combination of client flexibility & bleeding edge technology.
If you have an interesting or unsolveable industry problem, let us help lead you to your solution, and then execute it together.







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Jesse Krizenesky - Data Engineer

Jesse Krizenesky

Data Engineer, 'Also A Train' Pipeline layer; they could call him "The Plumber", for the streaming data and ML pipelines he lays. GCP, AWS, Private/Hybrid, Realtime, Batch, Big Data, Biggest Data, Parallel, Secure, HA, Scaling, Self-Healing, Reliable, Stable, Supportable, and most importantly - Correct
When you want help, and when you need it to Just Work, you call Jesse.

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