MLOPS and Azure Machine Learning Service

<p>As a data engineer on Azure who works closely with data scientists and in data science projects, it&rsquo;s my job to find and deploy the best tools for machine learning projects. Data science happens in notebooks, starts with a small idea and over the years has become more easy to use than ever before. What used to be a notebook full of code building custom models has (luckily for all of us) developed into a selection of import statements and open source models data scientist can use on demand.</p> <p>Feed a model your labeled and prepped data and your are ready to go. So why do we need anything more than Jupyter and Python to do it?</p> <p>A model in a notebook is a good start but not something you want to deploy into production. This is where MLOPS and all the wonderful machine learning tools we currently have come into play.</p> <h1>MLOPS</h1> <p>MLOPS stands for Machine Learning Operations and is the process of getting a model ready for production. It&rsquo;s basically all the steps you need to take after the notebook phase.</p> <p>&nbsp;</p> <p>ML-Dev-Ops</p> <p>Imagine you have a promising model and a good idea of what you need to do with the input data. The next step now is to automate the process of training, validating and deploying your model.</p> <p><a href="https://medium.com/@marthalasia/mlops-and-azure-machine-learning-service-ed61a8c220e">Read More</a></p>