The importance of Data Engineering in a successful AI implementation
If you’re already on-board the AI ship and you don’t want to sink – then make sure you have your “data engineer” lifeguard.
The implementation of Artificial Intelligence holds plenty of promises across different global industries. However, without data, there can be no AI. It is the principal factor in the successful design of an AI model. This data is usually in large volumes and needs to be sorted, processed, and prepared for it to be utilized in AI, and here is where Data Engineers & Data Scientists come trotting in! Andrew Ng last week talked about the importance of being more data centric in your models more on that in a bit, but first some context!
Step 1: Engineering your Data
This is the first and most important step of your AI implementation. It is the practice of cleaning, sorting, processing, and preparing data to be used for analytics, data science, and Artificial Intelligence. In non-technical terms, data engineering makes data useful. Data engineering transforms structured, semi-structured, and unstructured data from different storages and systems and renders it into aggregations of valuable, coherent data from which algorithms and applications can get insights and value. It takes people with the right expertise — data engineers — to sustain this data’s availability so that it remains readily accessible by others.
More often than not, organizations are always on the lookout for data scientists, not realizing that their actual requirement is that of a data engineer. One of the key findings of a study by Cognilytica is that 80% of an artificial intelligence project is spent on data engineering and data preparation activities alone!
Data engineering is centered around big data and distributed systems, with the knowledge and use of programming languages such as Python, Scala, Java, and scripting methods and tools. Using these programming skills, data engineers construct data pipelines at scale. Creating data pipelines is the principal scope of data engineering and involves the integration of big data technologies.
Step 2: Data Scientists Making Sense of your Data
Let’s take a step back & attempt to understand the raw meaning of “Data Science”. In essence, it is the extraction of valuable information from a pool of data. Contrary to popular belief, data scientists can’t be effective without access to large volumes of clean data. Artificial Intelligence functions by merging large volumes of data with fast, algorithmic operations and processes that allow the software to automatically assimilate sequences or patterns existing in the data.
These large volumes of data need extra work and clearly defined engineering measures to ready them. As data is usually collected in various formats and storages, it is of paramount importance to sort, clean, prepare, process, and transform it before moving it to an organized storage, such as a data warehouse. The engineered or “clean data” can then be used by data scientists for analysis and to develop data models.
Step 3: Real-time Data Pulling, Assimilation & Display
The effects of real-time streaming of data to the AI engine are profound. Adaptive learning with streaming data in data science is similar to how humans learn by regularly observing their environment. The process observes and picks up new changes made to the IO values and their related features. Adaptive learning from streaming data means continual learning and the adjustment of models based on the latest data, and sometimes applying special algorithmic processes to streaming data to concurrently enhance the prediction models while giving the best insights.
Fluid AI is one such plug-and-play AI firm that uses real-time streaming tools to provide organizations with swift insights into their data and allows for more precise and intelligent decision-making.
However, at times organizations tend to settle for conventional models that involve simple snapshot-based training that is not up to the task anymore. A conventional model involves two methods: training and prediction. The training method receives and assimilates data and the prediction method analyses the data to provide precise information and predictions. However, the conventional approach trains models based on past data and presumes that the world stays the same and that the same sequences, variations, and operations used in the past will occur in the future.
It’s simple, Data Engineering is the key, followed by the job done by data scientists. Couple that with real-time data streaming and looks like you’ll have a winning AI implementation. If it seems as though you need us to validate our point any further, take it from Andrew Ng, who explains in his latest YouTube video how AI systems are comprised of code and data. And to achieve the desired results we have to shift from a “model-centric to a data- centric” AI approach.
Having the right analytics workbench to ensure this, will be imperative to your model’s success, and for this Fluid AI is a strong fit as it enables smart pipelines & an analytics ETL to ensure you are data centric with much lesser effort.
Don’t get foxed out, #EngineerYourWay to a successful AI implementation.