How to Choose, Utilize And Successfully Implement ML In Your Software Development Efforts and different types of Machine Learning algorithms
Machine Learning Tools for SaaS Application Development:
1. Algorithmia
2. Microsoft Azure Machine Learning Studio
3. Amazon Machine Learning
4. Google Prediction API
5. IBM Watson Machine Learning Services
What is Machine Learning?
ML is an endeavor that aims to study and understand statistical data in the form of algorithms that detect patterns.
It refers to a branch of Artificial Intelligence where intelligence is used to extract meaningful information from massive and large data sets in order to conduct new analyses.
This intelligence consists of a multitude of Machine Learning algorithms that could include: Advancements in the field of machine learning have led to remarkable advances in the application and development of AI in today’s applications.
We are witnessing more and more organizations and startups adopting this technology into their daily operations to predict potential problems, visualize human actions, and even create innovations and brand loyalty.
Why should you use Machine Learning in software development?
1. How ML works?
2. How do you set up ?
3. What is analytics?
4. Why is predictive analytics important ?
5. When does it make sense to use ?
6. Are you building a purely predictive system ?
7. If your system relies on statistics and probability, is predictive modeling right for your problem ?
8. What’s the difference between a predictor, a predictor filter, and a stochastic gradient descent model ?
What are the different types of Machine Learning algorithms?
1. Algorithmia
2. Microsoft Azure Machine Learning Studio
3. Amazon Machine Learning
4. Google Prediction API
5. Hopper AI
How to Choose the Right Tool for Your Needs
Browsing through the different ML tools, let’s find out which software is the best choice for creating a market-ready AI application for SaaS.
Before selecting a specific tool, consider the following issues:
How easy is it to implement the tool? Does the tool produce results that are ready for use or does it need to be enhanced with some additional algorithms or an additional library?
Does the tool have a variety of languages available in its SDK or will it only run on Java/JavaScript? The selection of the right tool varies from one organization to another.
If you are creating an Internet-based application, for example, you could use something as simple as Google’s open-source code library.
How to Utilize a Machine Learning Tool for SaaS Application Development
1 Background and Set of Challenges
2 What is ML?
3 Types of ML
4 Benefits and Risks
5 Types of Machine Learning algorithms
6 Types of Machine Learning algorithms Used for Cloud-Based Computing
7 Types of ML Algorithms in Use for On-Premises
8 Challenges in Modernizing the Architectural Picture for SaaS-Based ML
9 Challenges in Modernizing the Architectural Picture for On-Premises ML
How to Successfully Implement a Step-by-Step Process Machine Learning Tool For SaaS Application Development
01 Jupyter Notebook
02 Deeplearning4j
03 ML for SaaS Application Development: First Steps
04 Chart Of The ML Tool
05 Defining your goals
06 Building your model
07 Testing your ML model
08 Managing your model and getting updates from it
09 Recommendation, Recommendation Algorithms
10 Deep Learning
11 Random Forest
12 SVM 4.4. Random Forest Regression
13 Neural Networks
14 Adaptive Network
15 Decision Tree
16 ML in SaaS Applications
17 Product Recommendation Engine
18 Recommendation Algorithms in SaaS
19 Recommendation Algorithms for Cloud SaaS Applications
20 Recommendation Algorithms for Analytics
21 Recommendation Algorithms for Advertising
Conclusion
Hopefully, the discussion with the above-mentioned resources will make you have an idea about a few of the new-age technologies being used to help speed up your software development process.
These services have been available in the market for some time now.
For instance, you can have an idea of how easy it was to import a photograph into a Picasa Web Albums and how the process has changed. The application then automatically entered it into the specific album you selected.
The system will automatically provide you with the rest of the information such as the date, rating, etc., that was previously provided to you.
This was possible as the information was already entered in a unique format and machine learning technology was used to recognize the characteristics of that particular album.
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