AutoML and Relevance of Xceed

AutoML and it's importance

Given the current rise in the techonlogies and open source platforms created in ML and Data Science space it becomes difficult to keep track of any of them. So, this is where AutoML comes in to the picture. Say , A data scientist is using a jupyter notebook to create a model . That jupyter notebook will be enough to only create any model because the start of an ML project itself would be wrong if one starts with Jupyter notebooks. Machine learning is an absolute mess if an organisation does not think about deployment from the start. The requirements would keep bloating if you are serious about deploying any Machine learning model and the only solution to that is to automate most at least 70 % of your work . The open source tools out there are are not countable and every organisation's data scientists are spoiled for choices. Most of the work in Machine learning space is still and was always in Feature creation and Model deployment.

AutoML is here to stay and will continue to only make a Data scientist/ML engineer's job easy .

The below table is just a small what is actually required if one wants to create and deploy a model. The requirements might keep growing and anyone working on a single ML Model will be stuckin all of these tools and doing all the manual work to keep their models into production.

In reality , Giant companies like Google ,Apple , Uber and all have a perfect Infrastructure to have any models deploy and run in real time but the problem comes when a meat sized company tries to do analytics and get stuck in all of the circus the world has to offer.

Relevance of Xceed

All of the requirements table is a just a small part of the actual required tools. Everything in the space of ML and datascience is a mess right now and the reason for that is that there are only point solutions out there in the market . if you need an end to end there are literally no tools which can help you get any business value and most of the tools that consider end to end or even selling point solutions are expensive so you will find yourself in the calculations of ROI before you start doing any analytics. This is where Xceed comes in .

Xceed not only has end to end solutions for any Ml Models but also for the data integrations, catalogs , versioning , workflow scheduling , a No Code designer for any of your Non-ML pipelines and also backed by a self service BI(business Intelligence) Service. In many companies that we see there are people hired for these roles seperately so usually deploying a ML Model is collective effort with numerous hours and effort being used recklessly. Xceed will take away all the man hours and effort and let you go into production within hours of effort .

Coming back to the side of AutoML and how Xceed is relevant , Starting from getting your data sources to deploying your model everything is automated and a 100% of it can be done in a NO-CODE environment .

Refer to Data Connectors , Data preparation and Xceed ML for more information .

The views of ML or any of the other transforms are very easy and anyone get through the model creation and evaluation phase with basic knowledge.

Xceed as of now supports Regression , Classification and Time series Forecasting Models