Today’s world got crazy about Artificial Intelligence, which in my opinion is still artificial. But everyone seems to be on it. I recently came across an
Machine learning is helping because there are unsolved business problems. And technology is mature enough to provide a solution. There are several technological advancements that just work like distributed file service, in-memory databases, parallel processing. Business has tons of data and now it is the time to start using it wisely.
Machine learning has several use cases. Almost any business will benefit from having some kind of automation that is supported by ML. It is used in the
There is a
Let’s pick on three examples to give you an overview of that it is capable of.
Provides automated sales assistance. It is working like a human sales assistant, which is friendly and can be personalized to what you need. It can work across different CRM platforms. It can detect good lead from the bad one. Ultimately helping real representative in making a
Their offering is directed towards organizations that are building applications with artificial intelligence capabilities. Their software allows customizing your application to be smart. In the Banking sector, AI helps to automate processes. They provide insights that help in a better decision-making process. In capital markets, they try to predict what customer needs. In manufacturing providing, better forecast demand, optimize the supply chain and prevent breakdowns on the factory floor.
Their do magical staff with image processing. Using satellites or drone images to convert them into meaningful information. They can map locations that
They have created an open source platform, used by over 18000 organizations. They provide ready to use statistical model and machine learning algorithms. Their offering has an enterprise platform that helps data scientist around the world. Great benefits are provided by in-memory distributed platform based on Apache Spark.
It is a content platform, which is using Natural Language Processing and Computer Vision to extract features from each piece of content. This allows learning what readers enjoy in their social media content. It allows creating content like, articles images or videos that will personally match your preferences.
Their offering “smart data warehouse” is a replacement for the traditional ETL process. Extract Transform and Load takes time to process unstructured data. To make it useful for analysis you need specialists to manga data load. Panoply claims that its machine learning and natural language processing capabilities allow it to do most of that work on its own without any help from these experts. It handles schema building, data mining, complex modeling,
This list could go on and on…..
There are literally hundreds of companies introducing Machine Learning into their offering. In some cases, it is only an enhancement to the main offering. But in some cases, the AI is the offering. This is clear evidence that it works and delivers value to the business. Don’t wait until your competition will do it before you. To dominate in your market you must
How it works
ML is a way to teach a computer to learn by themselves. Instead of spending countless hours programming logic to manage data you can use an algorithm that will learn from data in an automatic manner. You want a computer to detect logic from data instead of telling him how to think. By using this technique we are automating the process of iterative learning. After we train our algorithm how to manage a specific data model it can do it on its own. You just give him input and once your algorithm will digest it. The output will contain the information that you need.
For example prediction of house prices. Main advantages that these algorithms have is the ability to detect patterns in data. The algorithm can manage hundreds of variables and join them together. By contrast, we humans can cope with just a few variables. The more data you will provide the more efficient the model becomes.
ML comparison with BI
To contrast, the use of machine learning to the closest one is Business Intelligence. BI delivers information based on data. The main difference is the way BI is used. It is based on structured well-understood data. All the logic is provided by the business and applied to data. It is done in an organized structured manner. A lot of energy is going into programming. The disadvantage of this method
We have to remember about very important requirement before start using ML. They are very sensitive to data quality. Like in life if you provide someone with incorrect information the most likely outcome will be an incorrect decision. This applies to business and life. To contrast, if you have correct data the decision should not cause any troubles to your business. You have to put a lot of pressure on processes in your business. Making sure at each level data is validated and verified. It must be top notch. You must be able to verify data based on accuracy and context.
Machine learning provides great benefits when integrated with applications. Essentially it needs to be part of data pipelines.
- Leverage data assets to gain a new level of understanding.
- Continually predict changes in the business so that you can plan better for the future.
- Allows the system to automatically learn from data rather than through explicit programming.
There will be two types of usage.
First ML may be a part of an application and real-time data processing. Enhancing your systems on the fly. Providing benefits to you or your customers when interacting with your product. Or it will be part of Data Science sitting at the end of the line and learning about your business. Helping you make better decisions. I would recommend using both scenarios as they will provide benefits to both parties, you and your customers. There is an
In many organizations, data will be coming from different places. From IoT devices, web applications, background systems, to manual entry. At each stage, machine learning models can automatically be adjusted based on changes in the data. On the fly providing predictions. Whether it is stock pricing or
You have to use it to learn from the past to be better prepared for the future. It will provide automation to better understand how various actions and events will impact outcomes. You need a way to build predictive models that can automatically react to a change. At first, start working on descriptive analytics trying to have a
Why things happened?
Why customers like this product?
When sales is the highest?
When you cover this the next step is to look for predictive analytics to prepare your business for the future events. Making sure they won’t be unexpected events.
To advance yourself with machine learning you need appropriate skills and tools. First, hiring a
Try to design a solution to work as a service for as many business parts as you can manage. You should try building models that are reusable. If you allow API on them. It will extend their usage in multiple parts of your business systems.
There are several machine learning services in Azure. Each of them can provide solution to a specific problem.
- Machine Learning Studio
- Batch AI
- Bot Service
- Bing Autosuggest
- Bing Custom Search
- Bing Entity Search
- Bing Image Search
- Bing News Search
- Bing Speech
- Bing Spell Check
- Bing Visual Search
- Bing Web Search
- Computer Vision
- Content Moderator
- Custom Decision
- Custom Speech
- Custom Vision
- Emotion & Face
- Language Understanding
- Linguistic Analysis
- QnA Maker
- Speaker Recognition
- Text Analytics
- Translator Speech
- Translator Text
- Video Indexer
- Web Language Model
- Microsoft Genomics
Wow that’s about 28, quite a lot. I would first concentrate on the most flexible one. Azure Machine Learning Studio as this is the most powerful providing complete toolbox for a Data Scientist. Other services are very powerful too but they are designed to solve specific problem. While Machine Learning Studio is the development platform for your projects.
Just a quick example of Machine Learning Studio, where I am trying to predict car prices.
What do you use machine learning for, what value do you drive from it ?