Saikiran Bellamkonda | The Blog Pros

November 14, 2022

Top 10 MLOps Platforms to Manage and Optimize Machine Learning Lifecycle

Businesses continue transforming their operations to increase productivity and deliver memorable consumer experiences. This digital transition accelerates timeframes for interactions, transactions, and decisions. Additionally, it generates reams of data with brand-new insights into operations, clients, and competition. Machine learning helps companies in harnessing this data to gain a competitive advantage. ML (Machine Learning) models can detect patterns in massive amounts of data, allowing them to make faster, more accurate decisions on a larger scale than humans could. This enables humans and applications to take quick and intelligent action.

As more businesses experiment with data, they realize that developing a machine learning (ML) model is only one of many steps in the ML lifecycle.

May 25, 2022

Azure Data Factory: A Contemporary Solution for Modern Data Integration Challenges

As more data is available, it becomes more challenging to handle. Investing in innovative services and tools lets you get more value from data. Modern businesses must embrace effective tools, technologies, and innovative methods to succeed.

This is where the Azure Data Factory comes into play. ADF allows you to orchestrate data processes, analyze the data, and gain insights.

September 28, 2021

8 Quick Tips to Improve Decision Making With Better Data Quality

The term "data quality" on the search engine results in six million pages, which clearly expresses the importance of data quality and its crucial role in the decision-making context. However, understanding the data helps classify and qualify it for effective use in the required scenario.

Understanding the Quality of Data

Good quality data is accurate, consistent, and scalable. Data should also be helpful in decision-making, operations, and planning. On the other hand, lousy quality data can cause a delay in deploying a new system, damaged reputation, low productivity, poor decision-making, and loss of revenue. According to a report by The Data Warehousing Institute, poor quality customer data costs U.S. businesses approximately $611 billion per year. The research also found that 40 percent of firms have suffered losses due to insufficient data quality.