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Machine learning (ML) is transforming the way organizations approach problem solving and product development. It allows machines to perceive, learn from, abstract, and act on data. Yet until recently, this discipline was accessible only to specialized data scientists in a process known as analytical ML鈥攊n which specialists created custom machine learning models to aid human-driven decision making.

The emergence of MLOps has provided a shift from analytical ML to operational ML. MLOps is a set of practices that allows for standardized collaboration between data scientists and operations, so organizations can manage the lifecycle of ML engineering and deployment. It has changed the game by allowing organizations to scale and standardize the power of AI, deliver trusted decisions in real time, and more efficiently develop products and solve problems.

While analytical ML answers a specific question (鈥淚s there an animal in this picture of a campsite?鈥�), operational ML can answer broader questions (鈥淚s there an animal in this video feed that could pose an immediate danger to people?鈥�). Operational ML can therefore inform action (in this example, a 鈥測es鈥� answer could trigger an alert to nearby campers).

MLOps enables machine-made decisions using real-time input data, speeding deployment and providing direct business impact. By managing models throughout the lifecycle, tracking and monitoring performance to identify and correct errors, and implementing security in every phase, MLOps delivers multiple benefits:

• Improves the usefulness and relevance of data in the real world, allowing for fast, versatile problem solving and ML-powered services (real-time fraud detection, intelligent chatbots, etc.)
• Empowers teams to scale and re-use models across the enterprise, providing fast, cost-effective models that can be quickly tailored for urgent needs
• Integrates with open source architectures, simplifying modernization of legacy systems and enabling fast upgrades for changing priorities
• Embeds security, quality, and compliance into every part of the ML pipeline

So what does this mean for your organization? Here鈥檚 the short list of points to consider:

1. MLOps is ushering in the age of enterprise AI. Now is the time to learn how AI can best drive value for your organization. Integrate teams with AI tools early so you won鈥檛 be left behind.
   
2. Through governance and automation, MLOps lowers vulnerabilities. The Department of Defense has declared AI a national security risk. By providing strict governance and requiring less technical talent outside government, MLOps mitigates operational risk.
3. MLOps makes it easier to augment human capabilities with AI. Operationalizing ML expands each team member鈥檚 impact and efficiency so your organization can offer more services at less cost.
4. Businesses will soon be able to build ML-based products. The industry can expect a shift from ML supporting products to ML being the products. MLOps lays the foundation for businesses to build these products.
5. MLOps will disrupt the market and create a competitive edge. To explore adoption and integration, collaborate with companies tracking the future of ML to map its impact to specific use cases and enterprise operations.