AutoML Platform Escalates Small And Centralised Data Science Team For Potential Outputs

The AI projects incorporated by different organizations are extremely capital intensive, demand for better hiring and retaining of skilled data scientists who are still rare species to be found. Data science teams in a large firm are generally centralized, working for various business unit. These teams are usually small in size consisting of 5 to 15 members only for the whole organization. Therefore, considering the investment made in several AI projects, it has become quintessential to maximize the outputs these teams can achieve. The maximization process would require satisfying needs of every single unit while creating innovative data projects with the use of AutoML platforms.

AutoML (Automated Machine Learning) Platforms in Support of Maximising the Potential of Data Science Teams

AutoML is a set of data science tools and software enabling machine learning engineers and experts to automate repetitive tasks in the creation of AI projects. In other words, it is applying ML to improve construction and enhancement of ML projects.

AutoML assists data scientists run AI projects at great speed while keeping a record of every step of the project. The platform is also capable of reducing the time taken to develop AI models from the very beginning, simultaneously automating certain steps.

Challenges

As aforementioned, centralized data science team for several units demands extreme hard work from data scientists. They are consumed in developing new data models to resolute challenges in different business units while significantly maintain the prevailing solutions they already developed to account for fresh data.

Where data scientists are employed in leveraging specialized knowledge to solve complicated problems against regular data science maintenance work of dealing with data, testing certain scenarios, building models along with being able to maintain those models, the AutoML enters the picturesque.

The platform can help with finding the correct algorithmic model enabling most accurate predictions. Simultaneously, AutoML platforms are also capable of building customer AI models and tailoring them to the specific data at hand.

Although the word is 'Auto' yet humans are required in the loop for successful implementation of AutoML platforms. It can help to attain a project success and execute at a fast pace whereas humans are necessary for result interpretation and validation.

Functionalities of Platform

This AutoML platform might assist new data science teams hit the ground running and learn and develop models faster.

How Financial Firm's AutoML Helped the SparkCognition

The AI-based company SparkCognition claims that they worked with a financial firm which helped it generate the most accurate model to categorize financial market reign to behold multiple asset classes across a wide time window. The case study depicts that the financial firm used Darwin AutoML platform to develop a predictive model structure over historical financial data. The company used Darwin for data cleaning and management, feature generation and model building in the data science process.

Despite being a great choice for AI projects, AutoML platforms are not a solution for themselves. AutoML allows companies to accelerate the capabilities of their data scientists while supporting them to develop new projects along with cost, time and effort reduction involved in AI projects.

Since the inbuilt software of AutoML platform can assist with the task, the process would not require much human experience and expertise in the development process.

Not only for companies like SparkCognition and financial firms, the companies who can discover potential business insights hidden in their data are efficient enough to employ AutoML.