If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
MLOps, a compound of "machine learning" and "information technology operations," is a newer discipline involving collaboration between data scientists and IT professionals with the aim of productizing machine learning algorithms. The market for such solutions could grow from a nascent $350 million to $4 billion by 2025, according to Cognilytica. But certain nuances can make implementing MLOps a challenge. A survey by NewVantage Partners found that only 15% of leading enterprises have deployed AI capabilities into production at any scale. Still, the business value of MLOps can't be ignored.
Are these buzzwords hitting your newsfeed? Yes or no, it is high time to get tuned for the latest updates in AI-powered business practices. Machine Learning Model Operationalization Management (MLOps) is a way to eliminate pain in the neck during the development process and delivering ML-powered software easier, not to mention the relieving of every team member's life.
Allowing failure is one of the most basic prerequisites for innovation. If you are not prepared to fail, you will not be able to create anything new. As the German CTO of a Japanese IT service provider with a strong culture focused on innovation, I myself am deeply convinced of this. However, if only one of ten machine learning projects ever go live, something is definitely wrong. After all, machine learning is one of the central applications of artificial intelligence (AI) and the basis of numerous future technologies such as autonomous driving, smart cities, and the Industrial Internet of Things (IIoT).
Enterprises have struggled to collaborate well around their data, which hinders their ability to adopt transformative applications like AI. The evolution of DataOps could fix that problem. The term DataOps emerged seven years ago to refer to best practices for getting proper analytics, and research firm Gartner calls it a major trend encompassing several steps in the data lifecycle. Just as the DevOps trend led to a better process for collaboration between developers and operations teams, DataOps refers to closer collaboration between various teams handling data and operations teams deploying data into applications. Getting DataOps right is a significant challenge because of the multiple stakeholders and processes involved in the data lifecycle.
Dan Wright just became CEO of DataRobot, a company valued at more than $2.7 billion that is promising to automate the building, deployment, and management of AI models in a way that makes AI accessible to every organization. Following the release of version 7.0 of the DataRobot platform, Wright told VentureBeat that the industry requires a new era of democratization of AI that eliminates dependencies on data science teams. He explained that manual machine learning operations (MLOps) processes are simply not able to keep pace with changing business conditions. This interview has been edited for brevity and clarity. VentureBeat: Now that you're the CEO, what is the primary mission?
As organizations increasingly embark on their digital transformation journey, IT is turning into a profit center, rather than a cost center. CIOs (chief information officers) are more than often referred to as chief innovation officers. New roles like chief data officer and chief analytics officer are rising to prominence. Organizations on their digital transformation journey are facing increasing pressures due to the pandemic, remote workspaces and increasingly distributed applications. IT's ability to rapidly adapt to changing market needs is paramount to a successful digital transformation journey.
The rise of artificial intelligence has become omnipresent in recent years, state-of-the-art models are open-sourced on a daily basis and companies are fighting for the best data scientists and machine learning engineers, all with one goal in mind: creating tremendous value by leveraging the power of AI. Sounds great, but reality is harsh as generally only a small percentage of models make it to production and stay there. In this blog post, we'll explain how companies can unlock the business value of AI by adopting MLOps practices. Based on years of experience in applying machine learning at ML6, we share a set of best practices that have proven to work for us when it comes to MLOps. When it comes to a definition for MLOps, we believe Google's definition is spot on: "MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops)."
In response to automation and dislocation in the 19th Century, Mary Shelley gave us Frankenstein. Signals processing engineer, data scientist and now author S. B. Divya has imagined a whole world where boundaries between machine and human are blurred. Interestingly, according to reviews, the question of how work is performed by either human or machine drives a lot of the dramatic tension in Divya's imagined world. At Data Decisioning, we like that because work is the meaning of technology. When the CEO asks about ROI of a proposed AI investment, the justification is productivity, i.e. more work for less.
Challenges arise as the production of machine learning models scale up to an enterprise level. MLOps plays a role in mitigating some of the challenges like handling scalability, automation, reducing dependencies, and streamlining decision making. Simply put, MLOps is like the cousin of DevOps. It's a set of practices that unify the process of ML development and operation. This article serves as a general guide for someone looking to develop their next machine learning pipeline, delivering summaries of topics that will introduce topics of MLOps.
MLOps is the art and science of taking machine learning models from the data science lab to production. It's been a hot topic for the last couple of years, and for good reason. Going from innovation to scalability and repeatability are the hallmarks of generating business value, and MLOps represents precisely that for machine learning. Apache TVM is a key open source project in MLOps, used by the likes of Amazon, AMD, ARM, Facebook, Intel, Microsoft and Qualcomm. OctoML is the company set up by founding members of the TVM project to commercialize and scale it up.