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No-Code
Machine Learning As A Service

Data experts spend about 65% of their time on data wrangling tasks, including cleaning, transforming and organizing data, according to a survey conducted by Anaconda.
With our Active Learning ML As A Service, you can recover 72% of your time.

Data Cleansing

automate machine learning with JYSNC





Service One
Safely Store Data
With JYSNC you will always have access to your raw data. It stores the original raw data on a secure server, then creates a backup and only the backup dataset is wrangled.
Service Two
Tidy Dataset
Data is the most valuable thing for Analytics and Machine Learning. JYSNC organizes the data in Tidy format for feature engineering.
Service Three
Clean Data
JYSNC's data cleaning routine gives data experts more time on more important things, model accuracy. Clean data provides accurate outcomed.
About Us

About Us

We focus on turning data into assets.
Sochative's team is a ML & Big Data expert who has helped companies move into the AI and Machine Learning with tailor-made solutions. Now we turn your data into valuable insights with our deep learning and machine learning solution, JYSNC.
Building a game-changing and reliable product that simplify Machine Learning.

What is Machine Learning as a Service?

Machine Learning As Service is an array of services that provide machine learning tools as part of cloud computing services. MLaaS helps clients benefit from machine learning without the increasing cost, time and risk of establishing an inhouse internal machine learning team. Infrastructural concerns such as data pre-processing, model training, model evaluation, and ultimately, predictions, can be mitigated through MLaaS.


Data
Transformation

Intelligent process with point and click functionality to transform data.

Predictive Analysis
OUT BOX

Analytics to get you started; drag and drop functionality to customize and improve your analytics.


Model Training and
Tuning

Tools to test, deploy, manage, and monitor ML models in real-world production.

Model
Deployment

Previously deploy ML model in production by using established DevOps practices.

Contact US