What are MLOps?

How is AI beginning to impact Industry and Business Sectors

While AI is impacting our creativity and productivity of professionals at work, for instancing impacting how businesses and organizations hire, do marketing, customer relations and customer service, do cybersecurity and do research & analytics, A.I. is also fundamentally evolving.

ML Ops refers to Machine learning operations. With Machine Learning Model Operationalization Management (MLOps), this refers to the end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.

Now in 2022, we are at a stage where almost every other organisation is trying to incorporate AI/ML into their product. MLOps, are best practices for businesses to run AI successfully with help from an expanding smorgasbord of software products and cloud services.

When you think about it in the real world, MLOps is a collaborative function, often comprising data scientists, devops engineers, and IT.

AI’s Impact on Business is Becoming Ubiquitous

Hiring

With the future of work trending towards digital and remote, HR managers are overwhelmed with tons of resumes and applications. Thanks to advanced AI algorithms, they no longer have to spend endless hours identifying the right candidates from a large applicant pool.

Marketing

The key to achieving maximum marketing success now depends on marketers' ability to deliver highly personalized, relevant buying experiences. And that's where artificial intelligence comes in.

The adoption of artificial intelligence in digital marketing enables marketers to gain a more comprehensive understanding of their target audiences.

Research and Analytics

With people spending more time on their smartphones, data collection through mobile-friendly surveys seems to be the most efficient means of research. But there's another challenge with this research method: How do you match people to the right surveys at the right time? Well, AI takes care of that for you.

Using ML-trained recommendation models, market research can personalize market surveys more efficiently by presenting the relevant questions when the audience is most available and receptive.

Customer Service

In many instances, companies combine AI with human creativity such that issues beyond the capacity of the bot are transferred to a human agent. What makes artificial intelligence superior in the customer-service division is its ability to target specific consumers and cater to their specific tendencies.

Cybersecurity

Cybersecurity professionals are already using this tech to identify new types of malware and protect sensitive data for organizations. The beauty of implementing AI systems in a cybersecurity strategy is that they learn as they analyze more data, so they get better at their jobs with new experiences.

New Kinds of AI Companies and Startups are Receiving Funding in the 2020s

As we look ahead into 2022, I think it’s safe to expect artificial intelligence tools to continue to dominate. Even in venture capital and data related startups the ecosystem is dividing into three primary categories:  

• Layer 1 – Base platform companies: Algorithms; frameworks; infrastructure and workbenches for creating ML systems   

• Layer 2 – Cross-industry capability companies: Turnkey machine learning-based systems that solve specific problems spanning multiple industries (e.g., cybersecurity)  

• Layer 3 – Industry-specific companies: Applications powered by prediction or classification systems that target specific, niche use cases in one domain    

Why ML Ops is Relevant

A growing subsection of AI tools that can significantly increase companies’ likelihood of succeeding at their project goals in 2022 is likely: Machine learning operations, or MLOps. 

MLOps tools can improve a machine learning pipeline from start to finish by helping gather, manage and label data, experiment and test the model selection, deploy multiple models in production at once and protect against model and data drifts and attacks. 

As Nvidia jokes, MLOps may sound like the name of a shaggy, one-eyed monster, but it’s actually an acronym that spells success in enterprise AI.

The utility of MLOps perhaps has never been greater since enterprise adoption of A.I. is now high.

MLOps is a useful approach for the creation and quality of machine learning and AI solutions. By adopting an MLOps approach, data scientists and machine learning engineers can collaborate and increase the pace of model development and production, by implementing continuous integration and deployment (CI/CD) practices with proper monitoring, validation, and governance of ML models.

• Strategy

• Collaboration

• Implementation

Productionizing machine learning is difficult. The machine learning lifecycle consists of many complex components such as data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and much more. It also requires collaboration and hand-offs across teams, from Data Engineering to Data Science to ML Engineering.

• Data ingest

• Data prep

• Model training

• Model tuning

• Model deployment

• Model monitoring

• Explainability

Databricks my favorite IPO of 2022 actually thinks a lot about MLOps.

So does Nvidia and a host of other BigTech companies close to the A.I. life cycle.

In certain cases, MLOps can encompass everything from the data pipeline to model production, while other projects may require MLOps implementation of only the model deployment process.

MLops vs. DevOps

Holistically, the goal is to improve communication and collaboration between data scientists, data engineers and business analysts through the machine learning life cycle, similar to how DevOps tools help improve communication in the software development life cycle.

Machine learning is an iterative process with constant feedback loops and the need for continuous monitoring, which makes MLOps tools to manage this process even more critical. 

Being an emerging field, MLOps is rapidly gaining momentum amongst Data Scientists, ML Engineers and AI enthusiasts.

As I continue to write about data science more, we’ll cover MLOps again soon. If you enjoyed this article you might enjoy my A.I. Newsletter as well.

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