More than just a machine learning library

what is slicematrix-io?

SliceMatrix-IO is an end-to-end (e2e) computational ecosystem which enables users to build machine intelligence systems with speed, agility and scalability on demand. What makes SliceMatrix-IO extremely powerful is that there is MINIMAL CODING required to get STARTED! With a FEW LINES OF CODE, users can build machine intelligent models and systems.

Diagram 1 Note that in the diagram 1 the end-user is referred to as a ‘Quant Conductor’ because IO enables the end-user to orchestrate the design, development and execution of machine intelligent systems with speed and ease that the end-user is able to do the task of a team of data scientist.

Diagram 1 Note that in the diagram 1 the end-user is referred to as a ‘Quant Conductor’ because IO enables the end-user to orchestrate the design, development and execution of machine intelligent systems with speed and ease that the end-user is able to do the task of a team of data scientist.

Real-Time Persistence: Build Once & Repurpose

Once a model or pipeline are built, it is stored in the cloud, meaning it can be re-used multiple times by different processes in parallel. This saves the end-user valuable time and effort when building models & analyzing multiple datasets across many projects. Hence, making the firm agile and flexible.

SliceMatrix-IO is a computational software platform that delivers powerful machine learning algorithms such as manifold learning, neural networks, network graphs, and much more. In addition to a deep and rich suite of machine learning solutions, one of the key benefits of SliceMatrix-IO is it’s ease of use. With minimal code users can build a model and begin processing data immediately.

However, SliceMatrix-IO is much more than a machine learning library and to only focus on IO’s algorithms is to neglect powerful aspects of its design such as built-in communication network and distributed computational architecture.

How does it work?


Users can build end-to-end machine intelligent solutions in Python with a few lines of code!
There are 2 core innovative concepts that allows users to build powerful machine learning with minimal code:

1. The Analytical Pipeline(AP) can be thought of as assembly lines of code which can train machine learning models. AP’s are reusable, meaning you can use the same AP to train multiple machine learning models using different input data.

2. The Model
The model are where the real magic happens. After the Pipeline trains a Model, the Model live’s on SliceMatrix-IO’s machine learning infrastructure in the cloud. That means you can access your model after you create it in a distributed fashion. You can also train models in parallel subject to your usage plan’s request / throttling limits.

Multi Analytical Pipeline
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Shrink your API Deployment Time with SliceMatrix-IO Computational API

Move seamlessly from prototyping to
production. Scale up or down on
demand, based on business needs
and requirements.

 
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slicematrix-io(beta) + quant trading

 
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analytical pipelines & models up close

The diagram below is a hedge ratio workflow that begins by training an Isomap model, a manifold learning algorithm which learns the hidden connections within the dataset. We can use the methods contained within this model to find the best hedges for a given stock, say AAPL.

Then we use another model called the Kalman Filter. This is an example of a class of algorithms known as Bayesian Filters. These models can learn new information as time passes, and so are ideal for generating  hedge ratios in a pairs trading strategy.

 
 

slicematrix-io(beta) powerful machine intelligence algorithms