Adding Business Value and Intelligence
The concept of business value and intelligence is aimed at more productive measures through the utilization of various tech application and analytical tool for the assessment of raw data. Business intelligence makes use of activities like data mining, analytical processing, querying and reporting. Companies take advantage to improve their operationalization, as well as accelerate their decision making. Business intelligence is also useful in the aspect of reducing cost and expenses and also identifying new business opportunities.
A lot of experts have shared their ideas and spoken on various aspect of business values and intelligence relating to AI in Redmond. Notable speakers include Jennifer Marsman, Maxim Lukiyanov, Martin Wahl, and Noelle LaCharite. The aspects that they extensively spoke on is a machine and learning fundamentals, introduction to new azure machine learning service, using cognitive services to power your business applications, and how to solve business problems using AI, respectively.
Machine and Learning Fundamentals
The fundamentals of machine learning have to do with understanding both the theoretical and programming aspect. it is also important to be up to date with the latest algorithm and technology that is being implemented by the various programming tools for machine learning. The there simplest explanation of the term machine learning is that the operation of the machine in such a way that it would be able to perform various tasks.
Algorithms can learn how to perform these tasks in various ways, and this brings us to the different types of machine learning. They include supervised learning which is carried out to enable the machine to identify and differentiate between various data. Unsupervised learning, on the other hand, does not have to do with a specific data or structure that the machine is supposed to produce. Another type of machine learning is reinforcement learning.
The importance of a machine model’s accuracy cannot be understated. The accuracy is what really determines how effective a machine can be for the operationalization of a company. machine models are estimated or measured mainly by prediction making and putting them to work in the real world sense. In the business world, a model cannot be accepted until it has been tested against the real world and the results are satisfactory. Measuring a machine model depends on the characteristics of such a particular model, and the circumstances the model is needed in the real world.
Two vital aspects of Machine learning are CNN and RNN. CNN is convolutional neural networks, while RNN is recurrent neural networks. For CNN mainly generate free size outputs, and are used for minimal amounts of reprocessing. RNN on the other hand functions on random inputs and outputs. They can also be sued for the processing of random sequences. So in basic terms, CNN is built such that they can be able to recognize images while RNN, on the other hand, recognizes sequences.
Furthermore, Jennifer Marsman helped in the description of various methods that are related to artificial intelligence, and they include the following.
- Search and Optimization
The use of a search engine and search optimization helps to rank AI algorithms. Explaining the role of AI for search and optimization purposes on search engines could be very technical. Machines are also taught on how to work with these to rank algorithms.
Logic also plays a major role in AI. The application of Logic in Ai could be as an analytical tool, as a knowledge representation formalism, and also a method of reasoning. Logic can also be used in the aspect of programming language. With this, it can explore both the prospects and the problems of the success of AI.
- Probabilistic Methods for Uncertain Reasoning
One of the most widely artificial methods for representing uncertainty is a probability. A lot of certainty factors have been utilized for quantifying uncertainty for alternative numerical schemes over the years.
- Classifier and Statistical Learning Methods
Classifiers associated with AI includes Naive Bayes, Decision trees, perceptron, amidst a host of others. There are also various statistical learning methods and theories that are in used to evaluate the uncertainties of AI. However, there are limitations to these statistical models, and this is where logic comes.
- Artificial Neural Networks
This is the impact of the earlier mentioned RNN and CNN on the concept of AI. A typical explanation of ANN in a natural language processing AI which can be used in the interpretation of human speech.
- Evaluation Progress in AI
This is imperative in the estimation of the progress of the concept of AI across all sectors including business models. Three evaluation types include human discrimination, peer confrontation, and problem benchmarks.
An Introduction to New Azure Machine Learning Service
Maxim Lukiyanov spoke about the working principle of the new Azure machine learning service. The service helps to simplify and accelerate building, training, as well as the development of various machine learning models. Furthermore, the automated machine can be utilized in such a way that algorithms that are needed are easily identified, and the hyperparameters are tuned faster.
New Azure Machine Learning Service also helps to improve productivity and reduce costs with auto-scaling compute methods, as well as develops for the machine learning procedure. New Azure Machine Learning Service also have the advantage of storing the data easily on the cloud. Using the latest programming language is also a seamless operation with the New Azure Machine Learning Service, with open source frameworks like PyTorch, TensorFlow, and scikit-learn.
Maxim also spoke further on some benefits of the New Azure Machine Learning:
- Easy and flexible pricing method, as you will have to pay a=for only the features that you use.
- The machine learning is very easy to understand, and the tools that come with it are not in any way restrictive.
- With the various data and algorithm of the tool, there will be more accurate predictions
- The tools from the machine make it very easy to import data, and as well as fine-tune the results.
- A lot of other devices can be connected easily to the platform with the aid of the tolls
- Data models can be easily published as a web service
- The time scale for the publish of experiments is only a matter of minutes. This is a very major upgrade when compared to expert data scientists that take days.
- There is adequate security from the Azure security measures. And this is very useful for the storage of Data in the cloud.
Using Cognitive Services to Power your Business Applications: An Overview and Look at Different AI Use Cases
Martin Wahl explained that with Azure cognitive services, customers are set to benefit from AI with developers. With this, they will not even need the service of a data scientist, which is a major advantage to saving both time and costs. This is done by building this machine in such a way that the learning models, pipelines and infrastructure needed are packaged up on cognitive service for important activities such as vision, speech, search, processing of text, understanding languages, and many more operations. This means that anyone who is capable of writing a program at all can make use of the machine learning to improve the application.
Customers who have patronized this service are already benefiting from cognitive services such as face container, text container, custom vision service support for logo detection, language detection, in-depth analysis and many more.
Martin Wahl finally explained that with Azure service, more value is added to the business, and the implementation of artificial intelligence is easier than ever.
How to Solve Complex Business Problems Using AI Without Needing a Data Scientist or Machine Learning Expert.
With the possession of basic skills like python coding, data visualization, Hadoop platform, apache spark etc. complex business problems can be solved, even without being a machine learning expert or a data scientist. All of these are made possible through the help of AI and all that is needed is just dedication and willingness. Some procedure to go about this include:
- Understanding the basics: This has to do with acquiring general knowledge on the basics, both theoretically and practically.
- Learning Statistics: Statistics is core to solving business problems, and some of the aspect to be looked at include Sampling, data structures, variable, correlation, and regression etc.
- Learning Python
- Making attempts on an explanatory data analysis project
- Creation of learning models
- Understanding the technologies that are related to big data
- Exploring deeper models
- Completing a complex business problem.
Finally, Noelle LaCharite gave a vivid explanation of how a PoC was made and I did one myself in Delphi in 30 minutes with the aid of Azure AI.