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July 27, 2021

MLOps: Your Guide To Sustainable Solutions At Scale

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The demand for AI continues to increase across private and public organizations and sectors. Bloomberg Government shows the projected growth of the AI market in the federal sector to be $4.3 billion by 2023. 

Additionally, in January, the U.S. Congress adopted the National Defense Authorization Act of 2021 (NDAA), which includes creating a National Artificial Intelligence Initiative Office. Their mission is to: 

  • Promote U.S. leadership in AI research and development 
  • Lead the world in the development and use of trustworthy AI in the public and private sectors 
  • Prepare the present and future U.S. workforce for the integration of AI systems across all sectors of the economy and society 

Increasing demand for ML and AI solutions means that MLOps will continue to grow in importance. 

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What is MLOps? 

MLOps encompasses the tools, technologies, and practices that allow end-to-end operationalization ofMachine Learning(ML) algorithms. Most recent advances in AI have been achieved by applying machine learning to very large data sets. Machine-learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. ​ 

You are probably familiar with DevOps and the advantages it brings to software development. MLOps is something very similar for AI and ML.  


Why is MLOps Necessary for Scaling AI? 

MLOps provides the framework that allows ML solutions to scale effectively. AI projects are challenging and don’t follow a linear, predictable path. We can’t sit down and say “we will use these data sources and apply this combination of algorithms in our model and then we’ll have these outputs with a specific level of accuracy” up front. There are many unknowns, and this means there is often a lot of exploration to discover how best to use the data and apply algorithms to it. Because of this, it’s very important that we approach this work in an agile manner.

MLOps introduces repeatability and automation into the processes that support AI solution training, testing, deployment, monitoring, and governance. It standardizes the infrastructure all the way from development to production. MLOps also builds security measures in from the beginning, and throughout development, to make sure that all the elements of the process meet our standards for data and information security. Automated routines for these processes allow teams to move faster, work more efficiently, and make fewer mistakes. 


What Can MLOps Do For You? 

With MLOps, your organization can democratize machine learning, empower a community of data practitioners, and maximize business impact. By making ML efforts more efficient and effective, MLOps allows them to scale so they can meet the largest challenges. It is essential to providing deeper, richer, and more consistent insights with your ML applications. It can be the bridge between the exploratory work of data scientists creating effective algorithms and the real solutions built on those algorithms for end users.  

The MLOps Approach: Learn about the 6 key areas  

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