MLOps is essentially a compound of two terms - Machine Learning and Operations. MLOps is a practice wherein data scientists, especially the ones specializing in Machine Learning, collaborate and communicate with the Operations team of a company to manage Machine Learning software lifecycles to be put into production.
Using this practice, data science and operations teams can deploy, maintain, and govern all software production cycles.
The idea here is to not only increase the automation in the production cycles but also improve their quality. Optimized and highly automated Machine Learning is developed and developed with business and compliance requirements in mind.
To be able to do so successfully, the Machine Learning team requires a framework that can enable them to develop software by keeping business goals and objectives in mind. This is where Kubernetes and Docker come into play.
Why are Kubernetes and Docker good fits for Machine Learning?
Overall, Machine Learning has three progress steps - exploration, training, and deployment.
Kubernetes is a good fit for all three categories. The containers in Kubernetes allow Machine Learning scientists to packaging up libraries for a particular domain. These libraries are essential to create different Machine Learning models for specific problem statements. This way, Machine Learning scientists can also pinpoint the right data source and deploy algorithms in a stable and constant fashion.
Now, when it comes to training the data sets, one needs a framework that can withstand intensive computations. Since Kubernetes is a distributed cloud environment, computations and storage can be made separate. This categorization enables Machine Learning scientists to utilize storage space and eventually cut costs in relation to the business requirements and budget. Such independent scaling enables enhanced accuracy as well.
Lastly, Kubernetes serves as an excellent framework to deploy models effectively. Often, numerous Machine Learning models are combined in the production stage to serve multiple purposes in a single go. Now, using Kubernetes, one can deploy each of these models as independent and lightweight microservices. Machine Learning scientists can then reuse these microservices for other applications.
When it comes to Docker, it again is an excellent fit for Machine Learning. Docker is a complete and comprehensive development environment that suits numerous advanced needs of Machine Learning scientists.
When using Docker, Machine Learning scientists only need to specify what should be the constituents of the environment and its dependencies. Then, when one needs to fire up the environment to get going, they wouldn’t need to spend time and effort in setting up a complete environment. One simply needs to execute a few commands and the environment will be pulled right up.
Now that we understand why Docker and Kubernetes are such a good fit for Machine Learning, we need to be fully aware of which tools you need to keep handy. These tools are essential for Machine Learning scientists to use Docker and Kubernetes and thus optimize their production cycles in accordance with the business goals.
Tools to get started with Machine Learning using Docker and Kubernetes
Volcano is a native batch system that is built on the Kubernetes framework. Volcano is essential to run high-performance workloads and provide mechanisms that are not available in Kubernetes currently. These big data mechanisms include machine learning, bioinformatics, genomics, and deep learning. It also integrates with domain frameworks such as Spark, MPI, TensorFlow, and more. Volcano is also a project of the Cloud Native Computing Foundation.
Kubeflow is one of the most important tools you’ll need to start Machine Learning using Kubernetes. It provides a straightforward approach to deploying Machine Learning workflows on the Kubernetes framework. It works by making the entire process simple, scalable, and portable. Kubeflow also provides a customized TensorFlow training job operator which enables you to train your Machine Learning models easily.
If you are looking to start and run your Docker files with a fully-functional Machine Learning environment, FloydHub is the way to go. One of the most optimized tools, the deep learning frameworks provided in FloydHub comes with GPU and CPU support. Other specifications include TensorFlow, Caffe, OpenCV, Numpy, SciPy, Pandas, iPython, Torch, Jupyter notebook, MatPlotLib, and more.
One of the most crucial tools in this domain, Repo2Docker is essential for optimized and scalable operations. It works by fetching a git repository and building a container image in the Docker framework. This fetching and building are carried out based upon all configuration files found in the particular git repository.
A slim and up-to-date Docker image, this tool serves as a foundation for other Machine Learning projects and images. It can also cater to Deep Learning and other Data Science domains. One of the most commonly used tools, it also includes Conda, Nvidia CUDA, and TensorFlow.
NVIDIA-Docker is one of the most important tools to use if you are looking to run GPU-accelerated Docker containers. It comes as a complete and comprehensive NVIDIA Container toolkit. NVIDIA-Docker can automatically configure Docker containers by leveraging the NVIDIA GPUs. This is made possible because of the container runtime library.
Seldon is a comprehensive framework to package Machine Learning and operations. It comes with the power to manage at least thousands of Machine Learning production models. Deploying and monitoring these production machines is also made incredibly simple due to its straightforward and scalable approach.
Nauta is a Machine Learning tool developed by Intel. It is a distributed computing environment that runs Machine Learning and Deep Learning training experiments. The scalable processor-based systems in this are Intel Xeon. Nauta is also a multi-user tool and supports streaming and batch inference.
Docker and Kubernetes are powerful frameworks and tools that can help you in aligning your Machine Learning production cycles with the business operations requirements. Doing so helps a firm provide better business value and also cut costs. Plus, deployment and governance of all such production cycles are made a cakewalk as well. When you have the right tools in hand, you can easily build better Machine Learning models that provide extensive value to business operations and to the end-user.
 What is MLOps?