Reducing costs of Azure Databricks (FinOps in practice)
Big data is what powers companies. Lots of data mean lots of insights, which enables better decision-making. Having ...
Companies that have already built their data warehouses, now have the perfect momentum and a good technical base to make their data more valuable and better support their decisions. After the Business Intelligence boom, it’s time for more advanced and compounded solutions, including machine learning. And with tools such as Azure Machine Learning, you can make smarter, more precise and timely decisions.
There are in fact a few crucial fields of business operations that this mysterious analytical concept has changed. Today, it delivers huge value in diverse applications, such as demand or sales forecasting, failure and anomaly detection, online recommendations, advertisement targeting, but also in e.g. cognitive science.
And by embedding Machine Learning into their enterprise systems, various organizations can improve customer experience, reduce the risk of systematic failures, grow revenue and make significant cost savings.
However, building Machine Learning systems (on your own) is slow, time-consuming and error-prone. In such circumstances, lack of specific background and resources may cause a chronic frustration and will make you tear your hear out until you get bald.
Building a system based on Machine Learning algorithms requires deep expertise in statistics, econometrics, and artificial intelligence fields. Commercial Machine Learning systems are very expensive to deploy and maintain.
And that’s actually how and why the idea of Azure Machine Learning by Microsoft was born.
We made use of it and a while ago started to adopt a bunch of new BI skills into our competence map. Wanna know how it might affect your business?
Well, Microsoft created this service to allow you to build your own ML flows that cover cleansing the data, edit metadata, process, feature engineering and train your machine learning models. So why not use it and make a profit?!
When I started my journey with Azure Machine Learning, I was hugely surprised since I didn’t need any software to install, no hardware to manage and no development environments to grapple with.
Azure Machine Learning is a comfortable environment to work with because you can log on to Azure and start developing Machine Learning models from scratch – in any location and on any device (based on an easy to use drag, drop and connect paradigm).
The service offers a wide range of data sources that you can connect, which is what really matters in data-driven/ big data word. Machine learning from Microsoft Azure supports connection directly with e.g.: Hive Query, Azure Blob Storage, Azure SQL and on-premises data sources (preview feature).
As everything, Azure Machine Learning has its limitations, which I’ve also discovered quickly. What are they?
Azure Machine Learning – in comparison to its competitors – has a large collection of the best-of-breed algorithms developed by Microsoft Research to solve regression, clustering and predictive scenarios.
You can also extend your experiment with custom algorithms in R using over 350 open source supported R packages.
Data science offers organizations a real opportunity (with the right tool like Azure Machine Learning) to make smarter, more precise and timely decisions. They are no longer based on guesses or intuition, but on all the data they collect.
I mean, really, ALL OF IT (internal or external sources included, like weather conditions, social media updates, customer demographic and spatial data).
Wondering what specific data you can use in your predictions? Or how to set things up without messing around and making your analytical team’s life harder?
Don’t get flooded – we can help you find areas in which Machine Learning can improve your decision making and make your analysis more thorough. Ready to start?
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