Building a State-of-the-Art Data Science Platform
In the real world, the Data Science process flows beyond the steps of processing some data and training a predictive model. In a production system, the Data Science process is interested in data and model delivery, auditing, and preparation. Data Science is highly elaborate and getting it right is a critical factor in determining whether a business will succeed in its Data Science projects or not.
In this article, we are going to discuss the different components required to build a highly effective and robust DS platform.
What do we want to get out of this platform?
The main reason why businesses fail in their Data Science services is grossly related to their inability to properly quantify their DS requirements. Most are unable to answer “why do you need DS?”.
Unfortunately, several businesses jump on the hype train of trendy and buzz words at the time and feel obliged to do Data Science. At the end of the day, it’s what sells, no? There are three main possible scenarios of this:
What often ends up happening is failing to deliver on that hype. Failing to deliver thus results in a tainted reputation, and of course, business harm!
Modern-day businesses always have a use case for a Data Science platform. Data is key in today’s world. It is the world’s most valuable asset after all. However, what most fail to understand is the differentiation between a Data Science platform and an Artificial Intelligence (AI) platform. Data Science flows beyond the confinements of AI.
Data Science is the beating heart of the data body. Data Science is the medium through which any organization can make sense of its raw data. Data Science is, in its simplest form, a toolset for enabling the delivery of analytical intelligence.
So, why do you need a Data Science Platform? What do we want to get out of this platform?
Quite simply, a Data Science platform renders you capable of truly understanding your data.
We all experience physical relationships, and I am sure that all of us, at one point in time, wished that they knew what the other party was thinking. Well, as an organization, you are in a relationship with your clients and stakeholders. And Data Science does just that! Enables you to know and understand what your partners are thinking. It equips you with the right knowledge to assess what is working and what is not. Helps you decide which buttons to push.
Before we dive deep into the winning formula of the Data Science platform, I want to point out 2 incredibly important considerations:
So — let us start discussing the key components of a solid Data Science platform.
Data Flow Diagram of the Platform Components. Image by the Author
Part 1: Any data-related product needs to start with handling the transfer of data. How are we going to get our data inside our platform? We have two main options really:
I strongly believe that real-time data streaming is the way to go in almost every scenario; however, this is heavily dependent on the infrastructure and use case. If real-time streaming is your choice, then Apache Kafka is your friend. Kafka is simply a message-passing solution. There are various other solutions such as Amazon Kinesis, TIBCO Spotfire, and RabbitMQ. Again, just pick your poison.
Part 2: The data storage aspect. Selecting the database system to go for is heavily dependent on the following characteristics:
This is a huge domain for discussion and expands way beyond the objectives of this article, so I won’t be going into it much more.
Part 3: Data Processing and Feature Engineering. Once we start getting in our data streams, we can start processing this data on the fly. The ultimate goal for this is to create Feature Stores (i.e. specific feature pre-calculated). The Feature Stores become more relevant with the integration of Machine learning solutions within the Data Science platform. In production, any machine learning models running with constantly read from the Feature Stores to retrieve their data.
