MLOps stands for machine learning operations. The term MLOps is derived from DevOps (Development Operations). It is used to streamline the machine learning process from development to deployment. The MLOps include training machine learning models, experiment tracking, model optimization, creating ML pipelines, saving and serving models, and monitoring and maintaining models in production. In short, you are automating all the processes from development to deployment, and you are constantly monitoring the logs, metrics, and performance.

The null hypothesis is that the samples are drawn from the same distribution, which means that a low p-value is indicative of different distributions. In this example, we see that drift was detected for all of our features. In addition to statistical tests, there are other approaches you can take to tackle distribution shifts, such as visually inspecting histograms and distribution charts for individual features, which can be useful to confirm the disparity between distributions. In a more general topic, setting rule-based data validation is key in ensuring the quality of your data, which includes distribution changes, be it from external factors or systemic errors such as pipeline errors or missing data. For a more in-depth view on this topic, you can sign up for my upcoming workshop at ODSC Europe this June "Visually Inspecting Data Profiles for Data Distribution Shifts". In the workshop, we will also see how to visually inspect histograms and distribution charts and how to do data validation with whylogs' constraints. We will dig deeper into the concept of distribution shift and explore other popular packages in order to detect data shifts.

Welcome to the most comprehensive Data Analytics course available on Udemy! When you become a Data Analyst, there are two things that you should be skilled at in order to be a master data analyst, Python and Tableau! This course is a great choice for beginners looking to expand their skills in Data Analytics. You also create a solid portfolio of your work online and can link it on your resume. At 11 hours, this Python and Tableau course will teach you the core principles of Data Analytics at every stage in the pipeline.

Consciousness And Light Are Explored. The Inter Mind Bridges The Gap Between The Physical Mind And The Conscious Mind.

You might decide to follow me after reading this article. I've previously told you about some of the best side hustles, the only book/course to learn python, and now I'm going to tell you about one of the best AI classes I've found online that is free, doesn't require any additional app to install, and is taught by Google experts. I had been learning and practicing this course on YouTube for the previous two months. I just finished it a few days ago. I thought, why don't I tell you about it?

In this course, you will learn NLP (natural language processing) with deep learning. This course will teach you word2vec and how to implement word2vec. You will also learn how to implement GloVe using gradient descent and alternating least squares. This course uses recurrent neural networks for named entity recognition. Along with that, you will learn how to implement recursive neural tensor networks for sentiment analysis. Let's see the topics covered in this course-

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science. Will this course give you core python skills? There is a range of exciting opportunities for Python developers. All of them require a solid understanding of Python, and that's what you will learn in this course. And this course is designed to give you those core skills, fast.

How often do you test ML models in a Jupyter notebook, get good results, but still cannot convince your boss that the model should be used right away? Or maybe you manage to convince her and put the model in production, but you do not see any impact on business metrics? Luckily for you, there are better ways to test ML models in the real world and to convince everyone (including you) that they add value to the business. In this article you will learn what these evaluation methods are, how to implement them, and when should you use each. We, data scientists and ML engineers, develop and test ML models in our local development environment, for example, a Jupyter notebook.

Welcome back to yet another interesting improvement of the Transformer (Attention is All You Need) architecture -- Compressive Transformers. This particular architecture has a lower memory requirement than Vanilla Transformer and is similar to the Transformer-XL that models longer sequences efficiently. The below image depicts how the memory is compressed. We can also say that this is drawing some parallels to the human brain -- We have a brilliant memory because of the power of compressing and storing information very intelligently. This sure seems interesting, doesn't it?

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done.