As machine learning is no longer an experimental field and has been embraced by most industry-leading enterprises, there is a growing need for companies to scale their data science capabilities to support their entire suite of business goals. The success of these initial projects often depends on the tools and infrastructure available, which are frequently fragmented across regions, business units, and cloud/on-premises environments. Companies can overcome these challenges by embracing a hybrid-cloud approach, which allows for future-proofing, cost optimization, and data sovereignty, and enables teams to leverage the end-to-end set of tools and infrastructure necessary to unlock data-driven value everywhere their data resides. To be successful in implementing a hybrid-cloud strategy for machine learning, enterprises need to provide hybrid-cloud support for full data science life cycle coverage, agnostic support for data science tooling, and scalable compute infrastructure. According to a recent Forrester survey, organizations that adopt a hybrid-cloud approach to AI development experience fewer challenges in deploying and scaling their models than companies relying on a single cloud strategy.
Unlocking the Power of Machine Learning with Hybrid Cloud
The application of machine learning (ML) in enterprises has transformed from mere experiments into significant successes. Business executives recognize that data science is essential to maintain and extend competitive advantage. The data science career field is expected to grow by 36% from 2021 to 2031, according to the US Bureau of Labor Statistics.
Companies seek to scale their data science capabilities across all business goals and embed ML-based processes and solutions into every part of their operations. However, executing data science projects across regions, organizational and technological divisions poses significant challenges. Data is rendered useless without the proper tools and infrastructure to use it effectively, and both are often fragmented across various regions, business units, and cloud and on-premises environments.
Even when data scientists gain access to the data in other parts of the business, they may lack the effective tools and hardware necessary to leverage it. This results in suboptimal hardware, expensive data storage, and unnecessary data transfers that delay productivity and increase costs. In some cases, projects fail or never initiate.
Successful enterprises are overcoming these challenges through hybrid-cloud strategies that integrate on-premises and cloud environments. A hybrid-cloud approach enables companies to leverage the best of both worlds. They can scale their data science capabilities and develop and place ML models at the core of many business processes.
Vanguards within data-centric industries, including pharmaceuticals, finance, insurance, aerospace, and others, are investing heavily in assembling teams of data scientists with varied backgrounds and expertise. They are using hybrid-cloud strategies to develop and deploy ML-based solutions across all aspects of their businesses.
In conclusion, unlocking the power of machine learning through hybrid-cloud strategies can help businesses scale their data science capabilities to support their entire suite of business goals. The successful execution of data science projects across the regional, organizational, and technological divisions is made possible with the integration of on-premises and cloud environments. Hybrid-cloud strategies enable businesses to leverage the best of both worlds, leading to significant successes and a competitive edge.
Utilizing Hybrid Cloud to Maximize Machine Learning Capabilities
In order to fully maximize machine learning (ML) capabilities, successful organizations understand that their data science operations must be hybrid cloud by design. Hybrid cloud enables organizations to arbitrage the inherent advantages of different environments while preserving data sovereignty and providing the flexibility to evolve as business and organizational conditions change.
A recent Forrester survey of AI infrastructure decision-makers found that 71% of IT decision-makers consider hybrid cloud support important for executing their AI strategy, and 29% believe it’s already critical. Additionally, 91% of those surveyed said they plan on investing in hybrid cloud within two years, and 66% said they had already invested in hybrid support for AI workloads.
Hybrid-cloud platforms allow companies to direct workloads to the most cost-effective infrastructure and optimize utilization of an on-premise GPU cluster, mitigating rising cloud costs. As the size of ML workloads grows due to increasing numbers of use cases, larger volumes of data, and the use of computationally intensive deep learning models, cost optimization becomes even more critical.
Aside from cost optimization, there are three key drivers that are accelerating the trend towards hybrid-cloud strategies for data science. The first is data sovereignty, where regulatory requirements like GDPR force companies to process data locally with the threat of heavy fines in more and more parts of the world. The EU Artificial Intelligence Act goes a step further than fines, triaging AI applications across three risk categories and calling for outright bans on applications deemed to be the riskiest. Gartner predicts that 65% of the world’s population will soon be covered by similar regulations.
Moreover, hybrid cloud enables teams to leverage the end-to-end set of tools and infrastructure necessary to unlock data-driven value everywhere their data resides. It allows them to take advantage of the flexibility of cloud environments, the cost benefits of on-premises infrastructure, and the ability to select best-of-breed tools and services from any cloud vendor and machine learning operations tooling.
In conclusion, hybrid-cloud strategies for data science are becoming increasingly vital to enable organizations to execute data science projects and implement ML solutions anywhere in their business. It is essential to implement end-to-end ML platforms that support hybrid cloud natively and provide integrated capabilities that work seamlessly and consistently across environments.
Achieving Scalable Machine Learning with a Hybrid-Cloud Approach
A hybrid-cloud approach to machine learning (ML) provides flexibility to future-proof businesses for inevitable changes in operations and IT strategy. This allows companies to address changes in tech stack or geography, and adapt to new cloud vendors becoming significant competitors. However, implementing a hybrid-cloud strategy for ML is challenging. For instance, no public cloud vendor offers more than token support for on-premises workloads or support for a competitor’s cloud. The range of tools and infrastructure that data science teams need also scales as businesses grow their data science rosters and undertake more ML projects.
Here are three essential capabilities that every business must provide hybrid-cloud support for to scale data science across the organization:
Full Data Science Life Cycle Coverage
Enterprises need data science tooling and operations to manage every aspect of data science at scale. From model development to deployment to monitoring, full data science life cycle coverage is necessary.
Agnostic Support for Data Science Tooling
Businesses need to provide hybrid-cloud support for major open-source data science languages and frameworks, as well as proprietary tools. They also need to support the host of new tools and methods that are constantly being developed. Agnostic support for data science tooling is necessary due to the variety of ML and AI projects and the differing skills and backgrounds of data scientists across distributed enterprises.
Scalable Compute Infrastructure
More data, use cases, and advanced methods require scalable compute infrastructure with distributed compute and GPU support. However, this also requires support for multiple distributed compute frameworks since no single framework is optimal for all workloads. For instance, data engineering may work perfectly with Spark, but ML model training at scale may require a data-science-focused framework like Ray or Dask (or even OpenMPI).
Embedding ML models throughout core business functions is the heart of AI-based digital transformation. Organizations must adopt a hybrid-cloud or equivalent multicloud strategy to expand beyond initial successes and deploy impactful ML solutions everywhere. Data science teams need end-to-end, extensible, and scalable hybrid-cloud ML platforms to access the tools, infrastructure, and data they need to develop and deploy ML solutions across the business.
In conclusion, hybrid-cloud strategies for ML provide regulatory, cost, and flexibility benefits, and are necessary for businesses to expand beyond initial successes and deploy impactful ML solutions everywhere. With full data science life cycle coverage, agnostic support for data science tooling, and scalable compute infrastructure, businesses can provide hybrid-cloud support to scale data science across the organization.
Hybrid Cloud: The Key to Successful AI/ML Development
According to a recent Forrester survey, organizations that adopt a hybrid-cloud approach to AI development are experiencing 48% fewer challenges in deploying and scaling their models than companies relying on a single cloud strategy. This suggests that the vanguard of companies who have already invested in their data science teams and platforms are pulling even further ahead using hybrid cloud.
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