• Auto ML
• A drag & drop tool called Designer
Machine learning is a form of artificial intelligence where, through software and algorithms a computer uses from historical data to make predictions about the future. These complex computer codes, also known as algorithms, are used to create complex models. These machine learning models use algorithms to find patterns or relationships in the data, which can then be used by a model to predict values based on new data.
Azure Machine Learning is Microsoft’s answer to the increasing demand for Machine Learning as a Service solutions. With Azure Machine Learning, Microsoft provides a cloud based machine learning environment within the Azure platform through which data science projects can be run and managed. The machine learning services seamlessly integrate with the other cloud computing services Microsoft offers through the Azure platform. Azure Machine Learning gives you the ability to develop models based on open source machine learning tools such as Pytoch, TensorFlow, scikit-learn and many other resources.
A major development in the field of data science in recent years has been the emergence of Auto ML. This development automates parts of the data science development process that were previously often repetitive and time-consuming. In fact, the most modern Auto ML solutions can perform almost the entire development process with well-structured data. However, in most cases, data is not so structured and prior work is still needed. Big data needs to be trained before it can be used in a model. Where Auto ML really adds great value is in training and testing different models. Previously, after cleaning and structuring big data, the data scientist had to determine which models to train and test. The process of training and testing different models is a time-consuming task and often requires a lot of computing power. This is one of the reasons why data scientists often choose to train and test only a limited selection of algorithms. Auto ML allows the data scientist to train and test a much larger selection of algorithms on scalable cloud computers. In this way, the data scientist does not have to train and test every algorithm himself. With Auto ML, the data scientist indicates which algorithms should be tested and trained and which ones should be compared according to which error metric and/or statistics results. As an analogy, think of it like cycling. Previously, a data scientist cycled around on a city bike without gears, but with the development of Auto ML, a data scientist gets an electric bike with pedal assistance. You still have to ride the bike, but there is support from technology that gets data scientists to their destination faster.
At Rockfeather, we are also aware of the advantages that Azure Machine Learning offers over conventional data science tools. Using Azure Machine Learning allows us to deliver better and faster results to our customers in the field of data science. One of the projects where we at Rockfeather have leveraged Azure Machine Learning effectively is predicting sales numbers for one of our clients. These predictions, or forecasts as it is officially called, is a branch of data science that uses historical data to make predictions about the future. Good forecasting is important to this client because it saves costs and waste. The challenge with this project was that there was a large assortment of different products and therefore sales numbers had to be predicted for all these products. Manually training and testing models for a large assortment takes a lot of time, which is why we applied Auto ML in this situation. Because of the scalability of Azure’s cloud based Machine Learning, we can train and test a selection of different algorithms per product to find the most effective algorithm for each product. These models are all stored in Azure Machine Learning and once trained can be used to actually make predictions about future sales numbers.
In this blog, we are looking at a common scenario in the world of data: Microsoft Excel versus PowerApps. PowerApps offers a great alternative to our traditional spreadsheets. We will explore why it sometimes might be a clever idea to switch from your familiar spreadsheets to the new world of PowerApps.
On November 14th, 2023, the Future of Data & Analytics took place in Sparta-Stadium ‘Het Kasteel’ in Rotterdam. Leading organizations told us their stories on business digitalization. This page will give you access to the slides of the different sessions.
Microsoft Fabric is a new tool from Microsoft that integrates existing components in the areas of Data Science, Data Engineering, and Data Visualization. This speeds up your business process and ensures that you don’t have time-consuming jobs writing data from one component to another. During this online Master Class, we explain how this works.