DataOps: The Challenges of Operating a Machine Learning Model
Machine learning is one of the most innovative techniques in recent times to offer customised offers to customers. Data scientists find it easy to deploy a machine learning model through the data availability and open-source ML frameworks. The process seems very simple. All you need to do is to write machine learning code to train a basic, non-distributed model with sample data. However, it is not as easy as it seems, especially when you are scaling up to a production-grade system. With Dataops mechanism, you can operate the machine learning model with full efficiency though.
Some of the worrisome things are data cleansing, feature extraction, serving infrastructure and others. However, for each step of the machine learning process, there are some challenges that you will meet. We will discuss these steps and challenges that you will face during the machine learning process.
First of all, let’s discuss some of the steps involved in the process. They are:
Permitting distributed data storage and streaming
Formulating data for further analysis
Using machine learning frameworks to set up distributed training
Handling the trained models and metadata
Serving the trained models for inference
Before starting to write your machine learning code, you need to collect, clean and verify your data. Once you have created the machine learning model, you need to ensure that a serving infrastructure is in place. You need to monitor and update it as per the process.
What are the challenges?
When it comes to enabling distributed data storage and streaming, this step related to handling large amounts of data. You need to have the right mechanism to handle such a massive amount of data. However,tools such as HDFS, Apache Kafka, and Apache Cassandra can be used to perform this step.
Raw data cannot be used by a machine learning model immediately and must be cleaned and formatted to be used. You can use Apache Spark and Flink tools to perform the cleaning and formatting.
When you want to train a machine learning model, the framework must be installed on all machines and then you need to focus on resource isolation and allocation. Some of the tools that you can use here are TensorFlow, PyTorch, Apache Spark, and Apache MXNet.
When you have trained machine learning models and data sets in a large number, you will find it difficult to manage models. You can use tools such as HDFS, Google File System, MongoDB, and ArangoDB to handle this challenge.
When it comes to model serving, you need to ensure that deployments take minimum downtime. You can use Seldon Core and TensorFlow to achieve it.
When it comes to scaling up basic ML models without a platform, data scientists will surely find it difficult to achieve it. However, there are some advanced tools available for data scientists to simplify operations and help them to focus purely on deep machine learning things.