ML Foundation

Tuesday, March 2, 2021


Machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time. Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time wit hout being programmed to doso.

Indata science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on newdata. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.

Perhaps the most interesting thing about AI is that, while it can automate and do “work” at greater efficiency, it uses machine learning to“think” and “learn” over time, strategizing, designing, recognizing patterns,and making decisions. If that sounds a lot like a human brain, it’s because deep learning, one of the most important methods of machine learning, is based on the idea of a neural network, modeling the structure and function of the human brain.

And the impact of all this is huge. Some estimate a staggering $13 trillion in global economic activity by 2030 thanks toAI. And millions of new jobs are expected in the coming years. 

Some examples:

  • Financial services are now running real-time logs of thousands of transactions per second, parsing them through machine learning algorithms.
  • Retailers are grabbing data from receipts and loyalty programs, then passing it to AI engines to determine how to sell products better.
  • Manufacturers are using predictive technology to know what stresses their machinery and to predict when it is likely to break down or fail. 

Data is more valuable than ever—some have even said that “data is the new oil.” Reams of data are vital to making AI engines work, enabling them to learn. So some traditional businesses, including in manufacturing and agriculture, are leveraging their businesses to provide data as a service. 

How machine learning works

Step 1: Select and prepare a training dataset

Step 2: Choose an algorithm to run on the training data set

Step 3: Training the algorithm to create the model

Step 4: Using and improving the model

Cloudprisma implemented successfully the machine learning model in 14 steps – start to End flows

Benefits of Machine Learning:-

  1. Increased Automation
  2. Increased Productivity
  3. Smart Decision Making
  4. Solve Complex Problems
  5. Repetitive Tasks

Types of Machine Learning:-

CloudPrisma expertise in Microsoft technologies are privileged to enable leading-edge customers and partners who are taking advantage of Azure cloud and Machine learning artificial intelligence and applying it to their businesses in novel ways and,in doing so, creating leading-edge intelligent business solutions for our digital age.

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