Use Case | Machine Learning

TickSmith’s GOLD Platform enables artificial intelligence and machine learning by harmonizing data

How to Train Your Algorithm

Machine learning (ML) is a method of training artificial intelligence (AI) machines and/or systems to learn automatically. Artificial intelligence is a buzzword that most people are familiar with today. While AI and machine learning are often used interchangeably, there is a difference between the two. Machine learning is a subset of AI, and involves a program or application understanding and improving on the task it’s performing, based on the data it receives.

Even the most sophisticated AI can only learn from training. To be trained, the ML systems require enormous volumes of data. When the algo model is run, it should continuously learn from previous experience and change the way it processes parameters or the parameters that it uses. As the model is rerun several times, the end result is the most accurate algorithm that comes from the data. 

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  • How Google uses machine learning for various applications. 
  • How Apple uses machine learning in their technology

Algos Feed on Data— A lot of it

One major difficulty with ML is getting the immense amount of data in a format that is usable by the machine. Those who have an AI project would need a separate infrastructure to harmonize the data. The question this leads to is:  ‘Is it better to build or to buy a data management platform?’ Ultimately, the answer depends on the effort, cost, and time available. Add into the equation the time it takes for the actual development for the ML project, and you’ve got a simple mathematical algorithm that will match you to TickSmith.

Why TickSmith?

TickSmith’s GOLD Platform is built with the intention that firms can put their own ML algorithms or programs on top of the data, with as little infrastructure cost and responsibility as possible. Our GOLD Platform is built with the ability to use Big Data query engines and ingest usable files. This would allow for a ML model or program to be built on top of it, leveraging the functionalities and capabilities of the Big Data file format.

Due to the amount of data needed for ML, it’s impossible for algorithms to run on a traditional platform, such as a SQL database. Our expertise lies in both building Big Data platforms and the capital markets industry. Our GOLD platform, which allows for several use cases in the capital markets industry, is already deployed for enterprise firms in need of a Big Data management system. 

Machine Learning Through GOLD—How Does it Work?

The data is normalized into parquet files, then layered with Impala, which sits on top of the parquet files. This enables several use cases for the data, such as analytics, data monetization and machine learning. When it comes to alternative datasets, the GOLD Platform is able to ingest the data and format it to be usable into a standard data model that connects all the different types of data ingested.

The whole end-to-end system is built with transparency in mind and firms are able to extend the system’s capabilities with various modules, even leveraging services from third-parties. For example, ML can be enabled as a plug-in computer with the expertise of a third-party service. The data is leveraged from the platform and the files can be formatted into whatever format needed for ML.

The Benefits of Machine Learning for Capital Markets

Financial institutions are adopting AI and ML to automate business processes that previously required large operational costs. Beyond automation, ML can help businesses with strategic data-driven improvements such as providing granular insights into their market. Banks could use machine learning to enhance their trading strategies. For example, The National Bank of Canada extensively does backtesting using ML algorithms to make better trading decisions.

The future is AI, and those taking advantage of the benefits now will grow their business into unchartered territory, ahead of their competition.

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