The Database Management and Data Modeling landscapes have evolved much in the past few years, from the traditional relational model to now include non-relational models as well.
Review of Article
Advantages of Data Modeling
- Provides clear framework for development projects
- Enables high performance
- Corrupt datasets are found quickly and cleaned before using
- Offers tested models for building software
- Outlines scope and risks during development
- Includes detailed documentation which helps with future maintenance
- Wide variety of machine facts to include Internet of Things (IoT)
- Scale and speed of data increasing along by machine learning
- Demand of aggregated data is increasing
- Public, private on-site Cloud storage
- Huge amounts of data collected into what is Data Lakes (unchanged data) for data scientists to analyze later
- Automated data modeling (algorithms)
- Predictive modeling with advanced machine learning
- Semantic data models
SQL Database Trends 2018
- Adoption rates will differ as companies due to security concerns
- Cloud adoption
- AI capabilities are increasing
- SQL Servers for Linux
- There are new schedules for software updates (handled by the vendors themselves, not Microsoft like before)
- Use of Data Vaults
It sounds pretty exciting doesn’t it? So many changes to look forward to trying out for your business.
IBM today unveiled a new cloud offering called Cloud Private Data that’s designed to help organizations utilize data science and machine learning techniques to generate insight from data, and then engineer AI products that put those insights into use. Separately, Big Blue announced the creation of a new consulting…
This is more FYI for us all. I find it pretty cool that IBM is starting to help out with the creation of the application that helps with data science and engineering. It’ll make life and work so much easier for everyone. Its main purpose is for use by data scientists. It collects data, cleans and categorizes it. Machine learning algorithms developed here build models and put them into production.
I think it would be a good thing to check out just because it might help companies to grow. Think of all the information you could gather from using this and how it could put you ahead of the rest.
Not every problem can be solved by machine learning, and not every company is poised to apply AI. Here’s how to know whether your IT organization is ready to reap the benefits of artificial intelligence.
Now this is pretty interesting. I would’ve thought that all companies were ready to use AI. But now I am finding that isn’t true. Here’s what I have found. Each of the 10 points below have pluses and minuses for consideration.
- There’s plenty of data – You have all the information that you need. The question is, will you use the right model to find the answer to your questions?
- There are enough data scientists – Are you hiring people who say they can really do it?
- The company can track/acquire factors that matter – Is all the data there or is some missing from the models?
- The company has ways to clean and change the data when needed – Do you really have everything that you need to clean the noise and change when needed?
- Statistical analysis has already been completed as exploration – Use the right analysis after finding out the information, not before. It can lead up to overdetermined systems.
- You use many different approaches to find the best model to find the data you need – Do not use what you are comfortable using.
- The company has the capacity to train deep learning models – It takes more time as the system has to learn more.
- The machine learning (ML) models outperform the statistical models – If the statistical model cannot raise the bar for the ML model problems can arise.
- The system can deploy predictive models – It has to run on the cloud, server, PC or smart phone.
- It can update models from time to time – It has to update as data always changes. If updating isn’t completed there will be errors until fixed.
Intriguing isn’t it? Just make sure that you follow the suggestions so that your company is ready for AI implementation.
Data is being generated faster than at any other time in history. Is your business properly utilizing the power of machine learning?
This is a very interesting topic that I never paid much attention to before. I’ve seen a lot of conversations about it recently and have decided to do some research on it to help me better understand what everyone is talking about. I have to say that I think it’s interesting how computers can take information out of big data now and make decisions about what to do with it on their own, using the right algorithms.
I can also see how some people might be afraid of it as it can seem like computers might take over the world like in Terminator. So I can understand both Elon’s and Marc’s options and how they differ so much. I don’t know what side I’m on, I’d say somewhere in the middle. I can see pluses and minuses for machine learning and AI. It’ll be interesting to see how it all turns out in the future. What do you think?
Definition of Machine Learning
Machine learning, what is it? Basically it is learning to do better in the future from what has been done in the past. This is done through algorithms that make the computer learn to complete actions through automatic means. In other words, the computer learns to do stuff on its own, not through getting help from people. It’s a part of artificial intelligence, a sub-menu.
Examples of Machine Learning
- Optical character recognition
- Face detection
- Spam filtering
- Finding topics
- Understanding spoken languages
- Medical diagnosis
- Customer segmentation
- Detecting fraud
- Predicting weather
Goals of Machine Learning
- Make general purpose algorithms
- Learning algorithms
- Find prediction rules easy for people to understand
Very interesting article on learning more about machine learning.
Artificial intelligence poses a range of hidden and unknown dangers for enterprises deploying the technology. Here’s how to guard against the legal and compliance risks of AI.
Now who would have expected this? I sure didn’t. It’s a wake up call when you have to consider regulations and possible legal ramifications of using AI in certain industries. “Context, ethics, and data quality are issues that affect the value and reliability of AI, particularly in highly regulated industries,” says Dan Farris, co-chairman of the technology practice at law firm Fox Rothschild, and a former software engineer who focuses his legal practice on technology, privacy, data security, and infrastructure matters. “Deploying AI in any highly regulated industry may create regulatory compliance problems.” Source: CIO
- Have a thorough grasp of how the machine makes its decisions
- Design systems to gather reasonings of decisions made
- Make sure you’re in regulations
- Develop industry specific rules
- Knowing when it is safer to use Artificial Intelligence to human brain power
Very interesting information.
Evolutionary algorithms are the living, breathing AI of the future
This is so interesting and exciting! Think of everything that can and will be able to be done in the future. It’s open wide with almost no barriers. Can’t wait to see what the future brings.
AI and Machine Learning
Predictions put Artificial Intelligence (AI) and Machine Learning as becoming the game-changers of the coming decade.
This is so interesting! Consider what will happen in the next few years with all the new technologies being created and built now. Think about what AI and machine learning will do for us, situations where it can help and how it helps now.
There is so much going on to consider in every industry. So many ideas of how to improve what is already in the office, like building robotic assistants and self-teaching algorithms for handling the new Intelligence Analytics systems. Think of what the creating and building that will happen because of this. So exciting and a lot to think about isn’t it?