Methodologies of data science, in some way, come from machine learning, and both are often associated with mathematics, statistics, algorithms and data wrangling.

Data scientists make data models that need to run in production environments. And most DevOps practices are germane to production-oriented data science applications, but these practices are typically unheeded in data science training.

Many organizations may not be ready to invest in data science platforms, or maybe they have small data science teams for only basic operations. In this case, companies must apply DevOps best practices to data science teams instead of picking and orchestrating a platform.

To do so, several agile and DevOps paradigms being utilized for software development teams can be employed to data science workflows with some significant tunings.

DevOps encompasses infrastructure provisioning, configuration management, continuous integration and deployment, experimenting and monitoring. The teams in DevOps have been closely working with the development teams to manage the applications’ lifecycle efficiently.

 

Applying DevOps to Data Science:

• Data science teams add extra responsibilities to DevOps. And data engineering, a niche domain which deals with multifaceted pipelines to transform the data, demands the close collaboration of data science teams with DevOps.
• Additionally, operators are also anticipated to supply highly available clusters of Apache Hadoop, Apache Kafka, Apache Spark and Apache Airflow to address data extraction and transformation.
• Data scientists discover transformed data to explore insights and correlations. They embrace a diverse set of tools like Jupyter Notebooks, Pandas, Tableau and Power BI to visualize data. So, the DevOps teams are expected to support data scientists by creating environments for data exploration and visualization.

 

The Current State of Machine Learning in DevOps:

• More and more next-generation tools in the DevOps stack support machine learning and data science to some extent or other, but these tools are often black boxes operating system as isolated data silos.
• Company With DevOps teams still too busy putting lots of out fires, and with a lack of DevOps practitioners who truly and more understand machine learning, predictive analytics, and AI, the overall impact of this tools on comprehensive and data-driven automation is still limited.
• Monitoring and deployments products that are doing features machine learning typically do not provide visibility into how the most of algorithms are works, leaving data scientists skeptical as to whether or not its conclusions are correct. The black box approach also runs counters to the normal machine learning procedures that enable the analyst to adjust the algorithm in an iterative fashion until it becomes sufficiently accurate and effective.
• Furthermore, & perhaps are very important, even when the vendor does provide lots of network visibility, adjusting the machine learning as per the business need requires knowledge that ordinary programmers lack.
• DevOps engineers today are required to know how the infrastructure works, how to code, and how to utilize DevOps in the cloud. Adding machine learning to this set of skills is a huge if not impossible challenge since most DevOps engineers are simply not mathematicians.
• DevOps methodology and applications are increasingly generating a large and diverse set of data across the entire applications lifecycle from development, to deployment, to application performance management, and only a robust monitoring and analysis layer can truly harness this data for the ultimate DevOps goal of end-to-end automation and data science.

 

Looking into the future:

Despite the more challenges and obstacles, machine learning adoption is only going to grow as high salaries push more IT engineers and developers into this space.

The main reason for future growth though is that algorithms will become easier to understand and implement due to the proliferation of many frameworks. Google, Facebook, and so many others companies continue to develop and give away frameworks that allow data scientists and Big Data programmers to do more easily what only a PhD-level researcher could do before.

“Data science and machine learning are also often associated with mathematics, statistics, algorithms and data wrangling. While these skills are the core of the success of implementing machine learning in an organization, this is one of the functions that is gaining importance – DevOps for Data Science and machine learning.”