دانلود کتاب مقیاس پذیری یادگیری ماشین با Spark

In the field of machine learning development and data science, it is complex to record, integrate and build models collaboratively while experimenting with a combination of features, standardization techniques, and hyperparameters. In addition, it’s a complete task to track experiments, reproduce results, package models for deployment, store and manage models without distorting the company’s core goals Due to the above-given reasons, it is obvious there is a need to evolve ML development and make it a more robust, predictable, standardized, and filtered software development. To go further with this, many organizations have started to build internal machine learning platforms to manage the ML lifecycle, and yet they still face challenges like most of the ML platforms often support only a small set of built-in algorithms of ML libraries which are bound to each company’s infrastructure. Moreover, these platforms are usually not open source, and users cannot easily leverage new ML libraries or share their work with others in the community. Managing ML Experiment lifecycle can be also referred to as MLOps, which is a combination of machine learning, development, and operations. Machine learning is all about the experiment itself, training, tuning, and finding the optimal model. Development is about developing the pipelines and tools to integrate and take the machine learning model from the development / experimental stage to staging and production. And lastly, operations is about the CI/CD tools, monitoring, and managing the models at scale.