Analytics 360


Analytics360 accelerator helps companies realise the benefits of analytics and data science at pace and affordably. It delivers a vision, advanced analytics capability and a technical roadmap to drive out long term value from analytics that demonstrates the value of advanced analytics. We have leveraged our Artificial Intelligence and Machine Learning expertise to develop a platform for rapid deployment and digital product development.

Analytics360 is a cloud-based platform that operationalizes machine learning models allowing users to deploy and reuse at scale. Organizations are rapidly adopting advanced analytics to implement data-driven business decisions. As a result, the demand for data science experts is growing. However, there is a huge gap between the demand and supply. The annual demand for data scientists has grown by 12%, surpassing the annual supply of 7% of data science graduates, which will result in a shortage of approximately 250,000 data scientists by 2024. In such a situation, it is better to leverage services of an advanced analytics company who is an expert in Data Engineering, Data Science and Data Visualization.

Components of Analytics360

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Model Training

The two main approaches to model training are batch and real-time.

1. Batch Training

This is the most commonly used model training process, where a ML algorithm is trained in batches (or a batch) on the available data. Once this data is updated or modified, the model can be trained again if needed. This is mostly used in process industries.

2. Real-time Training

Real-time training involves a continuous process of taking in new data and updating the model’s parameters (e.g., the coefficients) to improve its predictive power. This can be achieved with streaming mechanism such as Spark Structured Streaming using StreamingLinearRegressionWithSGD.

Model Deployment

Under Model Deployment, we integrate a machine learning model into an existing production environment to make practical business decisions based on data. Depending on the clients requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data science process. Analytics360 has been developed to be able to accommodate simple and complex use cases with ease.

Model Validation and Model Monitoring

Once a model is deployed into production and providing utility to the business, it is important to validate the performance and continually monitor how well the model is performing. There are several aspects of performance to consider, and each parameter has its own metrics, thresholds, measurements that will have an impact on the overall life cycle of the model. Upon monitoring, the model can further be trained or retrained to improve its performance.

It is important to realize that models change and over time, their performance will decline. Any model that makes a prediction of a possible future event and identifies the root cause leading to that event is bound to change the system on which it is making predictions. This not only changes the data but also results in the model losing accuracy in its predictive power. What is furthermore important is knowing what model performance measurements matter to your business, how quickly the performance of the model is changing, and where to set the threshold to trigger the model retraining process.

Value Proposition

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Multiple domain and business cases understanding

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Expertise in hardware and software (immersive technologies & application development)

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Expertise in IoT, Data Science, Digital Twin, AI & ML

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IoT experience enablement through mobile apps and AR/VR

Cloud Computing

Proprietary digital accelerators in the form of Analytics360, PraEdge, MFGSuite & Anuva

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