The Essentials to a Successful MLOps Framework by Omdia Analysts
Overwhelmed by the number of enterprise MLOps solutions? Unsure where to begin in your MLOps journey? Then look no further. Omdia experts have released a comprehensive report evaluating the state of enterprise MLOps and delivering the fundamentals of MLOps practices. From evaluating enterprise MLOps solutions, exploring current technology trends, provider approaches, and future requirements, this report offers an objective overview of MLOps for the enterprise.
Who is this report for?
Enterprise AI practitioners and decision makers that want to become truly data or AI-driven will benefit from this report. It covers considerations for data leaders that have either just begun their AI journey, and offers exemplary information for enterprises that have investigated AI technology use cases but have yet to successfully scale it across business functions and units. As you’ll see in the survey highlighted in the full report, enterprises commonly find themselves in the phase between isolated AI experimentation and widespread implementation. This report is meant to give an assessment of enterprise AI, and an in depth overview of the emerging technology and solutions today – specifically enterprise MLOps solutions.
Why is this report important for data professionals?
MLOps (Machine Learning Operations) is a fairly new practice. Since Machine learning made its way into enterprise strategy, companies have faced challenges productionizing and operationalizing these machine learning models. In the beginning of 2020, many data professionals were pivoting to focus on machine learning operations – thus the term MLOps was born. In a highly technical field of scientists, data practitioners rightfully were skeptical of the hype of “MLOps”. Though in this comprehensive report, distinguished analyst Bradley Shimmin investigates the efficacy of MLOps, and its role in the scalability of AI and its overall success for the future. The Omdia report covers many of the questions facing AI decision makers, team leaders and data professionals such as:
- What is MLOps, and what does it solve?
- What is MLOps role in enterprise AI?
- What are the key challenges of building successful AI outcomes?
- What does an Enterprise ML workflow look like?
- What questions must be answered at each step in the ML lifecycle?
- What are the key features of an enterprise MLOps platform?
- What are the options for MLOps solutions?
- What is the criteria for buyers evaluating MLOps solutions?
- What are the architectural complexities and considerations for enterprise AI?
- Who from the organization should be involved in successful AI implementations?
- What is the best management model for enterprise AI?
- Where does AI decision making typically reside within an organization?
- What is next for MLOps?
Before you dive into this bountiful wealth of knowledge, here are our takeaways from Omdia’s report on the Fundamentals of MLOps. We hope this report will provide guidance for data leaders on their journey towards building effective MLOps solutions.
- Scalability is the leading problem facing enterprise AI
According to Omdia’s survey of 365 enterprises, only 7% say that they are successfully able to scale AI deployments across multiple business functions. Meanwhile, a whopping 40% are investigating AI technology and use cases. This highlights a dramatic problem facing the industry. While enterprises are actively investing in AI, few enterprises have managed to successfully scale it across the entire business functions.
2. The case for enterprise MLOps platform solutions
Omdia makes a strong case for enterprise MLOps platforms. According to their insight, Omdia analysts see MLOps platforms as a critical competitive advantage in operationalizing machine learning. As it states in the report, adopting an enterprise MLOps platform can “successfully apply DevOps principles to the task of operationalizing ML, despite numerous ML operational, collaborative, and infrastructure complexities.” If you are trying to evaluate next steps to achieve machine learning at scale, you will want to consider your enterprise MLOps platforms. Across the board enterprises have seen major benefits from AI once a proper MLOps solution is put in place. At this stage in ML, there are many solutions available to accommodate the operational complexities of enterprise machine learning at scale.
“Without investment in an enterprise MLOps platform, Omdia believes that enterprise practitioners may find themselves falling behind better-prepared rivals and unable to leverage AI as a means of optimizing and innovating across both business and operational concerns.”
3. What to look for in an MLOps Platform?
Not everyone is aware of the primary characteristics of an MLOps platform. What exactly can MLOps platforms solve? While it varies from solution to solution, there are some essential features and characteristics that a worthy enterprise MLOps platform should support. Luckily, Omdia has outlined the key requirements and features that any buyers should be looking for in the figure below.
Enterprises vary in infrastructure, and it is important for your MLOps platform to grow with you. This is why we suggest some additional criteria enterprise AI practitioners that are evaluating MLOps platforms and solutions should expect from the solutions they are evaluating. Any enterprise MLOps platform should support:
- Agnostic computing
- Asses and integrate a suite of solutions
- Establish measures for responsible AI
4. What’s next for MLOps?
MLOps is maturing quickly. AI leaders should already be thinking about the future. Omdia highlights some of the key areas they believe will be important for the future of MLOps.
- Computing infrastructure
One area that often goes undiscussed is the compute infrastructure powering the entire machine learning lifecycle. Omdia suggests that it is becoming increasingly important to recognizing the importance of matching an AI project with the correct AI acceleration hardware. While the report touches on this topic, we also have a few materials to help enterprises optimize machine learning server utilization.
Over the last year, MLOps practices have been somewhat of a wild west. Omdia predicts there will be a focus on standardization of MLOps platforms. In particular, MLOps platforms are beginning to come up with their own solutions to address transparency and explainability issues.
3. MLOps Platform as a Competitive Advantage
Omdia suggests that enterprises that adopt an MLOps platform can more readily scale beyond experimentation, and can make AI a core competency and competitive advantage. According to Omdia’s report, MLOps platforms have already been proven to help enterprises scale.
“Enterprise MLOps platforms can greatly reduce the burden that enterprise practitioners must carry in order to successfully create ML projects.”
You can access the full comprehensive Omdia report on the fundamentals of MLOps here. Our hope is that data professionals and decision makers can get the answers they need, no matter what stage you are. cnvrg.io is an MLOps platform that is transforming the way enterprises manage, scale and accelerate AI and data science development from research to production. Our solutions are built to be agnostic and offer the support to get your models to production. cnvrg.io has helped enterprises across industries scale AI solutions across business units, and can work with any infrastructure. The code-first platform is built by data scientists, for data scientists and offers unrivaled flexibility to run on-premise or cloud. From advanced MLOps to continual learning, cnvrg.io brings top of the line technology to data science teams so they can spend less time on DevOps and focus on the real magic – algorithms. To learn more about the cnvrg.io MLOps solution, you can schedule a one on one session with an ML specialist.