These are The Best Machine Learning Software for Engineers

Since we have in recent times seen the capabilities of Technology, especially its benefits  and recent trends in Construction Technology, we have decided to also talk about Machine learning, this time we are focusing on Machine Learning Software for Engineers. Technology has proven to be one of the most dynamic things that man has ever created. The concept of Machine Learning is solely to make users do better at their craft and make the process more straightforward than the standard techniques. So what does Machine Learning entail?

Machine Learning is a field of inquiry designed to understand and build methods that leverage big data to improve performance on some tasks. It is a technology under the popular Artificial Intelligence. You might be surprised to know how this model of Artificial Intelligence works.

Algorithms for Machine Learning are responsible for their working technique. They build a model based on sample data, sometimes called training data. These algorithms allow the Machine to make predictions or decisions, even without programming. In essence, Machine Learning is not a kind of programming language which tells a computer what to do.

The algorithms for Machine Learning cans be applied to a wide range of fields, such as medicine, email filtering, computer vision, construction, speech recognition, and even engineering. These are fields of areas where it seems impossible to develop conventional algorithms to perform the required tasks.

Applications of Machine Learning in Engineering

As I mentioned earlier, Machine Learning has a variety of applications in different fields. One of the fields where it is beneficial is Engineering. Want to know the application of ML in Engineering? Well, let’s find out.

The truth is, Machine Learning has some disadvantages when applied in Engineering. First, it might cause engineers’ tasks to evolve, and at the same time, it can allow them to do things they could not before.

When it comes to Machine Learning in Engineering, artificial intelligence involves hardware and software components. Machine Learning will help support intelligent production lines, complex manufacturing tasks, designing, and improving functions as time goes on. The whole process requires little or no human intervention at all.

roles of machine learning in construction technology
Roles of machine learning in construction technology

One of the applications of Machine Learning in Engineering is in Automobile Engineering. For some time now, Automobile manufacturers have used Artificial Intelligence robots with Machine Learning Algorithms for many tasks. Those tasks include simple engineering tasks and precision moves needed for some of the most complex parts of the manufacturing process.

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Engineers are involved in various tasks, from design to simulation, and can be improved with the help of these artificial intelligence tools. For example, the popular Computer-Aided Design (CAD), a fundamental part of engineering today, was once just a supplemental tool in the process.

Tools like Computer-Aided Design and Machine Learning will improve the possibilities and capabilities of engineers to explore new designs and weight-saving options.

There are also other ways that Artificial Intelligence and Machine Learning can significantly impact Engineering. ML is also involved in breaking down bulk data between separate departments and helps to manage data to obtain detailed insights from it effectively.

In addition, Artificial Intelligence and ML can give room for engineers to leave low-value tasks and focus on high-value functions. The low-value processes are left to AI and ML to take care of. To conclude, machine learning is used in discovering patterns of the data to help with engineering judgment.

What is the Best Machine Learning Software in Engineering?

1. Azure Machine Learning: Best for connectors and free add-on products

Azure Machine Learning is software designed by Microsoft to help users effectively build, develop, train and deploy various machine learning models. This software will have access to automated machine learning that identifies suitable algorithms and hyperparameters more quickly.

A reason why engineers mostly prefer Microsoft Azure is because of its free accentuating services, including event processing, certain app services, and automation lasting up to 500 minutes of usage. For the paid features, Azure Machine Learning costs $0.33/hour and offers 12 months free with a $200 credit for the initial 30 days of usage.

Pros:

  • Easy-to-use for users with little or no coding experience
  • Featuring excellent pre-trained models
  • The software has an extensive suite of free product add-ons
  • It gives users a lengthy free trial and credits

Cons:

  • The software can be quite challenging to integrate python code and run
  • Connection to Tableau is a bit difficult
  • There are limitations to the way the program handles heavy statistical models.

2. IBM Machine Learning: Best free, open-source Machine Learning Software

With the IBM Machine Learning software, you can integrate and match various products, including IBM Watson Machine Learning, IBM Watson Openscale, IBM Watson studio, and IBM Cloud Pak for Data.

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As a user of this program, you can be able to develop AI models by making use of open-source tools and deploying and monitoring AI models. IBM Machine Learning software gives users an extensive free lite plan for 20 CUH and a maximum of 2 parallel decision optimization batch jobs per deployment.

IBM Machine Learning costs $0.50/CUH and offers a free lite plan with 20 capacity unit hours. Paid plans also offer a free 30-day trial.

Pros:

  • The software offers unlimited modeling without overages
  • It has an easy-to-use API that is also well documented
  • Features a drag-and-drop prep, blending, and modeling
  • Provides an option of text analytics for unstructured data

Cons:

  • Users must put each service/feature in separate tabs. It does not offer a single overview
  • Implementation takes a lot of time
  • The IMB Machine Learning program has some restrictions when adjusting results and parameters while training models

3. Google Cloud AI Platform: Best No-code Machine learning framework

The Google Cloud AI Platform is one of the most popular in engineering. It integrates Auto Machine Language, Artificial Intelligence platform, and Machine Language Ops for a comprehensive user experience. This AI platform scales to the skill level of users, offering both point-and-click data science. The process uses AutoML and advanced model optimization.

A fantastic feature of the Google Cloud AI platform is that it provides code-based and No-code tools to ensure the engineers and users have a basic and simple machine learning experience. The Google Cloud AI platform costs $0.19/hour. It also offers users a free $300 credit for the first 90 days of usage.

Pros:

  • The program offers AI explanations and “What-If” validation testing
  • It has a strong connection with the Tensor flow and TPUs
  • It has an easy to use and understand interface
  • The Machine Learning program also offers good Out-of-the-box CV algorithms as well as video processing modules

Cons:

  • It isn’t well designed for deployment relating to hybrid cloud
  • The program has a limited library support
  • It is limited to running 25 models in parallel

4. Neural Designer: Best for High GPU Training Speed

Did you know that with Neural Designer, you can skip writing code and building conventional block diagrams by just undergoing a series of drag-and-drop, point-and-click tools? All of that is possible with the Neural Designer Machine Learning software.

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This software program boasts a record-breaking GPU training speed of up to 417k samples every second. This feature makes it the fastest among other ML software platforms. The program used in Neural Designer is entirely C++, which might be a little complicated to use, but offers more incredible performance speed instead.

It costs $2495 per year to get the Neural Designer program. The platform offers a free analysis of up to 1,000 data rows.

Pros:

  • It offers a fantastic presentation of data processing results
  • The program also has good memory management for importing large sets of data
  • High-speed calculations using GPU and CPU optimization
  • The Neural Designer platform handles problems relating to parameter optimization quite well

Cons:

  • There is no cloud-based tool available on the Neural Designer platform
  • Users might experience some limitations during automation
  • The pricing of Neural Designer is not flexible

5. Anaconda Machine Learning: Best Machine Learning Software for businesses and freelancers

As the Google Cloud AI Platform, Anaconda is another popular machine learning platform most suitable for businesses and freelance engineers. It was designed for the MLOps life cycle and is used by big companies, including Toyota, AT&T, Goldman Sachs, and American National Bank, to mention a few.

Standard features of Anaconda include Conda Package Manager, Unlimited and flexible commercial packages and bandwidth, a mirrored or cloud-based repository, and an environment manager.

The platform has individual subscriptions that make it the best for freelancers. It is easy and free to use and features thousands of open-source libraries and over 7500 Conda packages. Anaconda Machine Learning software costs around $14.95 per month and offers a free Individual Edition.

Pro:

  • Anaconda ML Program offers an extensive open-source freeware package
  • It is friendly with various versions of Python and its libraries
  • Customer environments are managed effectively
  • The program pre-installs the most valuable libraries and packages at the get-go

Cons:

  • The program does not include Atom, Docker, and PyCharm
  • It is not integrated with Git software. An integration could help the software improve
  • Source-controlled environments and custom repositories are limited to Enterprise Edition

Written by Nwachukwu Richard

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