Anand Gandhi

Anand Gandhi

Subject matter expert,

Softweb Solutions, Inc.

Kruparth Thumar

Kruparth Thumar

Machine learning consultant,

Softweb Solutions, Inc.

About Webinar

In the manufacturing industry, new products are proliferating at an unprecedented pace, while the delivery windows are tightening. Manufacturers thrive on finding new ways to enhance product quality while still being able to take on short lead-time production runs. They are also focusing on improving time-to-customer performance. However, unplanned equipment failure, increasing downtime, and demand variation directly affects organizations’ ability to meet customers’ demands and expectations.

Machine learning presents businesses with an opportunity to drive innovation and stay ahead of the curve while maintaining the quality of produced goods and satisfying customer needs. Machine learning facilitates fault detection during early stages and helps manufacturers to constantly improve the machine performance, enhance business processes, and optimize supply chain. This webinar is aimed at company influencers and decision-makers. It presents the capabilities of machine learning to transform the way the manufacturing industry works. Our subject matter experts will discuss how machine learning impacts various areas of operations of the manufacturing industry.


  • Machine learning 101
  • Importance of machine learning
  • Real world implementation of machine learning
  • Supervised and unsupervised machine learning implementations
  • Machine Learning opportunity in the manufacturing sector
  • Industry specific machine learning use cases

Questions & Answers

The following are the answers to the questions that were asked during the live webinar.

Question 1: How to get started with machine learning? What do I need to start?

Answer 1: To be able to apply machine learning you need a problem to solve, and you need data relating to this problem. Your data can preferably be in a structured form, which could be within a database or multiple spreadsheets, or an unstructured form like emails or social data. Machine learning providers then guide you further by creating a machine learning model from a sample dataset, evaluating this model, and using it to make predictions.

Question 2: What amount of data is required to apply machine learning?

Answer 2: We get this question quite often. The amount of data is definitely important, but it is equally important to have quality data. A large volume of data with inconsistency will result in false or inaccurate predictions. Hence, I would say rather than focusing on quantity of data, it is essential to emphasize on data quality.

However, if you’re worried that your business hasn’t generated much data or if your business is new and you don’t have enough data, machine learning can still be applied on whatever data your business has generated so far, though the accuracy of the results may not seem as transformative.

But... that doesn’t mean you should wait to accumulate data. In such scenarios, data provisioning becomes more important because it provides a path for future data to be used in machine learning models. You should make sure that, going forward, all your data points should be mapped. This could include any number or type of data sources such as data from real-time, social media, as well as internal and external systems.

The point is that the sooner you start, the better it is going forward.

Question 3: How do we know which machine learning algorithm is better for us to solve our problem?

Answer 3:In order to apply appropriate algorithms, you first need to thoroughly understand the problem, figure out what specific area(s) you want to address, and exactly what output should your algorithm provide. If you are concerned about accuracy then you can test the data with different algorithms and cross-validate them to know whether you are getting good accuracy or not.

You can read our blog on the same to know more.

Question 4: Aren’t machine learning, artificial intelligence, and deep learning interchangeable terms?

Answer 4: As we have also mentioned in the webinar that AI is not equal to ML. I would say the same for deep learning. ML and deep learning, though work on same artificial neural networks, are different.

Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly complex ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data. You can get more details about deep learning in our blog.

Designing and developing algorithms according to the behaviors based on empirical data are known as Machine Learning.

In addition to machine learning, AI also covers other aspects like knowledge representation, natural language processing, planning, automation, robotics etc.

Question 5: How to integrate real-time data directly into the ML models?

Answer 5: Once the ML model is built, the data is automatically fed into the system from different integrated data sources like SAP or SQL databases and others. Real-time data generated by machines or any other sources can be directly stored in a data warehouse. ML models will directly be able to fetch data from that particular data warehouse. So you don’t need to update your systems or ML models, it will be automatically taken care of.