Machine learning is gradually becoming the driving force for every business. Business organizations, large or small trying to seek machine learning models to predict present and future demands and do innovation, production, marketing, and distribution for their products.
Business value concerns of all forms of value that decides the well-being of a business. It’s a much broader term than economic value encompassing many other factors such as customer satisfaction, employee satisfaction, social values etc. It’s the key measurement of the success of a business. AI helps you to Accelerate this business value in two ways. That’s through allowing to make correct decisions and innovation.
Remember the days when Yahoo was the major search engine and Internet Explorer was the Major web browser. One of the main reason for their downfall was their inability to make correct decisions. Wise decisions are made by analyzing data. More data you analyze, better decisions you make. Machine Learning greatly support in this cause.
There was a time, Customers accepted what companies were offering them. Things are different now. Demands of customers for new features are ever more increasing. Machine Learning has been the decisive factor behind almost every new innovation whether it be face recognition, personal assistants or autonomous vehicles.
Machine Learning in more details
First starts with learning what machine learning is. Machine learning enables systems to learn and make decisions without explicitly programming for it. Machine learning is applied in a broad range of fields. Nowadays, Almost every human activity getting automated with the help of machine learning. A particular area of study that machine learning largely exploited is data science.
Data science plays with data. Data must be extracted to make the best decisions for a business.
The amount of data that a business has to work with is enormous today. For example, social media producing billions of data every day. To stay ahead of your competitors, every business must make the best use of this data. That’s where you need machine learning.
Machine learning has invented many techniques to make better decisions out of large data sets. These include Neural networks, SVM, Reinforcement learning and many other algorithms.
Among them, Neural networks are leading the way. It improves consistently spawning child technologies such as convolutional and recurrent neural networks to provide better results in different scenarios.
Learning machine learning from the beginning, and trying to develop models from scratch is not a wise idea. That yields huge cost and demands a lot of expertise in the subject. That why someone should try to take the assistance of a machine learning vendor. Google, Amazon, Microsoft they all provides Machine learning services. Let’s take Microsoft as an example, and review what qualities we should look for when selecting a vendor.
Using cloud as a solution for machine learning
It simplifies and accelerates the building, training, and deployment of machine learning models. It provides with a set of APIs to interact with when creating models hiding all the complexity in devising machine learning algorithms. Azure has the capability to identify suitable algorithms and tune hyperparameters faster. Autoscale is a built-in feature of Azure cloud services which automatically scale applications. This autoscaling feature has many advantages. It allows your application to perform best while keeping the cost to a minimum. Azure machine learning APIs can be used with any major technologies such as C# and Java.
There are many other advantages you will have with cloud Machine Learning
- Flexible pricing. You pay for what you use.
- High user-friendliness. Easier to learn and less restrictive.
- More accurate predictions based on a wide range of algorithms.
- Fine tuning results are easier.
- Ability to publish your data model as a web service Which is easy to consume.
- The tool allows data streaming platforms like Azure Event Hubs to consume data from thousands of concurrently connected devices.
- You can publish experiments for data models in just a few minutes whereas expert data scientists may take days to do the same.
- Azure security measures manage the security of Azure Machine Learning that protects data in the cloud and offers security-health monitoring of the environment
Using Cognitive Services to power your business applications
We will go on to discuss how Azure cognitive service can be used power up a business application. Azure cognitive services are a combination of APIs, SDKs, and services which allows developers to build intelligent applications without having expertise in data science or AI. These applications can have the ability to see, hear, speak, understand or even to reason.
Azure cognitive services were introduced to extend the Microsoft existing portfolio of APIs.
New services provided by Azure cognitive services includes
- Computer vision API which provides with advanced algorithms necessary to implement image processing
- Face API to enable face detection and recognition
- Emotion API gives options to recognize the emotion of a face
- Speech service adds speech functionalities to applications
- Text analytics can be used for natural language processings
Most of these APIs were built targeting business applications. Text analytics can be used to harvest user feedbacks thus allowing businesses to take necessary actions to accelerate their value. Speech services allow business organizations to provide better customer services to their clients. All these APIs have a free trial which can be used to evaluate them. You can use these cognitive services to build various types of AI applications that will solve a complex problem for you thus accelerating your business value.
If you want to talk more about ML and AI, feel free to contact me: bjorn.nostdahl@gunnebo.com 🙂
