If the wheels of technology seem to be spinning faster than ever before, they are. But instead of separate circles, it’s more like the magic rings trick in which the magician links them all together to perform. In layman’s terms, that would be Machine Learning (ML) Ops. Rather than the unicorn employee – everything a company desires in an individual rolled into one – this unicorn practice employs and is built upon three technologies that are used to operationalize businesses to keep those wheels turning. If DevOps was the sheriff of 2021, then ML Ops is the new sheriff in town.What is Machine Learning Operations (ML Ops)?It seems self-explanatory as a combination of machine learning and operations, but the technology behind it delves a bit deeper. It is a collaboration of engineering practices designed by Data Scientists, Operations, and Data Engineers using machine learning, model development, deployment, and data management to wrangle the enormous amounts of Data businesses now must navigate to gain actionable insights for their business.But where ML Ops shines is in its deployment capabilities. After all, once the business makes their decision based on actionable insights from the Data, then the next step is to implement it and put it into play. It is the de facto operations product lifecycle, the wheel that keeps the business moving forward. Ultimately, its goal is to automate the Machine Learning lifecycle from modelling to implementation to retraining once new Data gets into the mix. Because there will always be new Data and the world is not one-size-fits-all.4 ML Ops Pipeline StepsOne of the elements of MLOps lifecycle systems automation is that of continuous learning and retraining. Think of it like this. If you know the movie War Games, J.O.S.H.U.A knows how to play the games, but he doesn’t understand them; the whys and the hows. When it comes to the end of the movie and the game (spoiler alert), he has ‘learned’ human behavior and must make the decision to stop the game. Granted, he was an 80s realized version of early Artificial Intelligence, but ultimately, he was a machine who learned. Machine Learning can expand its knowledge base to integrate Data and model validation, that is part of the draw of ML Ops. Not only can Data and Operations professionals address complexities of deployments but they can create predefined steps to emulate and consider factors such as company size, project, and Machine Learning capabilities and complexities.Data Ingestion – Takes Data in and determines through which pipeline it should continue. What Data will be used in training? Which for validation sets? And which should be combined into one multi-streamed dataset.Data Validation – Once the Data has been taken in, the role of Data validation is to see if there are any anomalies. This focus not only lets you know how your Data has changed over time.Data Preparation – Data is cleaned and prepared to fit into the right format so your model can follow it. At this stage, also, Data may be combined with domain knowledge featuring engineering to build new features and solutions.Model Training – At the core of the pipeline is Model training which uses ingested Data to help launch trainings in sequence or parallel to determine what might be needed for a production model as well. There are three ways to launch such models and they include:Embedded in an appOn and IoT deviceIn a specialized dedicated web service using remote procedure call (RP), for example.Where Do We Go From Here?MLOps isn’t the only technology predicated on learning and driving an automated pipeline that will free up Data professionals to focus on the bigger picture. Though built on DevOps principles and in collaboration with a long unsiloed team of both technical and non-technical professionals, ML Ops has grown in popularity over the years and shows no sign of slowing down any time soon. As Open-Source networks emerge and ML Ops teams get created to help navigate this new technology, it will only continue to grow and reach new heights. Businesses who understand and develop these strategies now will have their foot firmly in the future and head and shoulders above their competition.If you’re interested in Digital Analytics, Machine Learning, or Robotics just to name a few, Harnham may have a role for you. Check out our latest MLOps jobs or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 – 4999 or send an email to firstname.lastname@example.org. For our Arizona Team, contact us at (602) 562 7011 or send an email to email@example.com. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to firstname.lastname@example.org.