As companies are deploying more and more machine learning models into their systems, a variety of frameworks (some open-source, some not) have come up over the years to make this deployment faster and more efficient. Some of the popular frameworks include TensorFlow, Amazon SageMaker, IBM Watson Studio, Google Cloud AutoML, and Azure Machine Learning Studio, among others. Tensorflow, by far, takes one of the top spots when it comes to machine learning frameworks that technologists depend on. Recently, Pycaret, a low-code machine learning library in Python, has also become increasingly popular among ML practitioners. Let us take a look at how both of them work and what makes them different from each other. Recently completing six years, TensorFlow was developed by the Google brain team at first for internal use.
Last week we have announced PyCaret 2.0, an open source, low-code machine learning library in Python that automates machine learning workflow. It is an end-to-end machine learning and model management tool that speeds up machine learning experiment cycle and helps data scientists become more efficient and productive. In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs. PyCaret is an open source and free to use Python library that comes with a wide range of functions that are built to work within Power BI. Power BI is a business analytics solution that lets you visualize your data and share insights across your organization, or embed them in your app or website.
If you are looking for a Python library to train and deploy supervised and unsupervised machine learning models in a low-code environment, then you should try PyCaret. From data preparation to model deployment, PyCaret allows all these processes in minimum time using your choice of notebook environment. PyCaret enables data scientists and data engineers to perform end-to-end experiments quickly and efficiently. While most of the open-source machine learning libraries require complex lines of codes, PyCaret is a useful low-code library that can increase the performance in complex machine learning tasks with only a few lines of code. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, spaCy, and many more.
For developers, data scientists, and business analysts looking to improve the outcome of their machine learning projects, let's look at how low code platforms are playing a big role in meeting their needs and speeding up ML projects. Is your data scientist team looking to speed up your machine learning projects? Or spend more time experimenting with different ML algorithms and less time maintaining or debugging code? Or, are you considering integrating ML into your business processes yet don't have the resources to hire a slew of data scientists and engineers? Then, you may be ready to consider low code machine learning platforms for your next project.
Some of my most popular blogs on Medium are about libraries that I believe you should try. In this blog, I will focus on low-code machine learning libraries. The truth is that many data scientists believe that low-code libraries are shortcuts and should be avoided. I'm afraid I have to disagree! I think that low-code libraries should be included in our pipeline to help us make important decisions without wasting time.