Oracle Corporation
09/09/2024 | Press release | Distributed by Public on 09/10/2024 01:36
Breaking boundaries in ML development: SQream on OCI
Now is an exciting time to be developing artificial intelligence (AI) and machine learning (ML) models. As technology continues to advance at an unprecedented pace, companies are racing to deploy ML models that advance innovative ideas and maintain a competitive edge. Yet, despite the urgency, many organizations still struggle to transition their AI projects from concept to production. The challenge often lies in efficiently handling and preparing massive amounts of data-a critical step that dictates the pace and success of the entire development process.
Breakthrough technologies like SQream on Oracle Cloud Infrastructure (OCI) are improving outcomes by transforming legacy processes by accelerating data preparation and reducing development cycles by over 90%. With these advancements, organizations can streamline their workflows and expedite AI deployments, ultimately enabling them to achieve their strategic objectives more effectively.
ML model development is a complex process, often bogged down by manual, time-consuming steps and a heavy reliance on multiple teams to provide the necessary data and infrastructure. A recent study shows that 98% of companies experienced ML project failures in 2023.
Top factors contributing to the failure of ML Projects in 2023.
Overcoming these challenges requires a strategic approach that addresses both the technological and collaborative aspects of the process. With SQream on OCI, we're witnessing transformative improvements that are reshaping the entire landscape of data science. This specialized, patented GPU-accelerated technology doesn't just speed up data preparation. It redefines the entire process, enabling quicker access to actionable data and insights. By alleviating the bottlenecks that typically stifle team productivity, SQream mobilizes both individuals and groups to accomplish more in less time. On a broader scale, SQream on OCI provides invaluable strategic flexibility and cost efficiency, helping enterprises quickly adapt to the ever-evolving demands of the digital age. These enhancements collectively pave the way for more efficient and effective ML project execution, helping ensure that the company can fully capitalize on its data investments.
Transform data preparation
Architecture diagram of a deployment of SQream on OCI.
The initial stages of ML development, particularly data preparation, are often the most time-consuming and complex. This phase involves extensive tasks, such as data collection, cleaning, integration, and transformation, each of which can significantly delay projects when handled on traditional CPU infrastructure. GPU acceleration revolutionizes the following processes, harnessing the power of massive parallel processing to dramatically enhance speed and efficiency:
Data preparation (collection, cleaning, integration, and transformation): Traditional data preparation involves labor-intensive, manual processes that are time-consuming, prone to errors, and often require multiple iterations. From manual scripting for data collection, to painstaking efforts in data cleaning, and complex custom scripting for integrating and transforming disparate datasets, these manual processes can lead to significant project delays. SQream on OCI has a dramatic impact on these tasks, streamlining and automating the processes by leveraging GPU-accelerated technology. This innovation reduces errors, reducing data preparation by as much as 90%, and enables real-time processing of large datasets, eliminating the need for manual chunking, staging areas, or preprocessing steps. The ability to handle these tasks in real-time without the need for preprocessing or intermediate steps is crucial for timely and effective data analysis and model development.
Feature engineering and data validation: Feature engineering and validation are critical steps in ML development because they directly impact model accuracy and performance. However, these processes are typically iterative and time-consuming, requiring multiple rounds of experimentation to identify the most effective features. With SQream on OCI, data scientists can quickly experiment with different feature sets and validate their effectiveness much faster. The platform's GPU acceleration allows for faster processing of complex calculations, significantly shortening the cycle from development to production. This capability is key for identifying the most impactful features for model accuracy and ultimately leads to more robust and reliable ML models.
These enhancements not only accelerate the crucial early stages of ML projects but also help ensure that the data fed into models is of the highest quality. This foundational improvement sets the stage for more successful outcomes, as data scientists can spend less time preparing data and more time deriving actionable insights.
Empower teams and boost productivity
While efficient data preparation is crucial, optimizing team collaboration and productivity to maximize the value of ML projects is equally important. In typical ML development environments, team dynamics can often be stymied by inefficiencies that hinder both progress and collaboration. Data scientists depend heavily on data engineers for numerous tasks, especially data preparation. This reliance creates a bottleneck, as data scientists must often wait for data engineers to prepare and provision the data, which delays the entire development process.
Moreover, the traditional division of labor between data scientists and data engineers can lead to duplicated efforts and miscommunications, adding further delays and frustrations. Teams might find themselves in a repetitive cycle of requesting data adjustments and waiting for these to be processed, weighing down productivity even further. The disconnect between data science and data engineering teams often results in longer development cycles, project overrun costs, and missed opportunities for innovation.
SQream on OCI revolutionizes team dynamics in the following ways:
Enhanced collaboration: By utilizing SQream's GPU-accelerated data processing capabilities, data scientists can independently perform many of the data preparation tasks that previously required data engineering support. Data scientists gain direct control over data manipulation, allowing them to make real-time adjustments and iterations, which accelerates the development cycle and reduces the potential for miscommunication.
Boosting morale and productivity: With SQream on OCI, removing common bottlenecks like slow data processing and dependency on external resources allows data science teams to operate more autonomously and efficiently. This autonomy boosts both productivity and team morale as members see quicker results from their efforts and can focus on more creative and strategic activities, rather than waiting on data provisioning. When the team is empowered to work more efficiently, they're more motivated and can deliver the high-impact results that drive business success.
Optimized human resource allocation: As data scientists take on more of the data handling tasks, data engineers can redirect their focus towards optimizing data infrastructure, improving data storage solutions, and implementing advanced data governance practices. Optimizing role and resource allocation enhances the team's overall efficiency and allows each member to contribute more effectively to their area of expertise. By reducing the reliance on a single team for critical tasks, organizations can ensure that their data operations are more resilient with a dramatically reduced cost profile.
Cost efficiency: Reducing total cost of ownership
One of the most compelling benefits of SQream on OCI is the dramatic reduction in total cost of ownership (TCO) for data operations. Traditionally, scaling data analytics capabilities involves significant investments in both human resources and physical infrastructure, including the procurement and maintenance of high-performance servers and storage solutions either on-premises or in the cloud. This setup can be prohibitively expensive, especially for large-scale projects or for organizations looking to rapidly expand their data capabilities. So, SQream on OCI offers the following benefits:
Optimized hardware utilization: SQream's architecture, designed to maximize the processing power of GPUs, allows more data to be processed at faster speeds, greatly reducing the reliance on physical hardware. Utilizing OCI's scalable cloud resources means that organizations can scale their data processing capabilities without corresponding increases in physical infrastructure. The ability to scale operations efficiently without incurring prohibitive costs is a significant advantage for companies looking to expand their data initiatives.
Reduced operational costs: By moving more data operations to the cloud, companies minimize the ongoing costs associated with data center operations, including power, cooling, and space. Furthermore, the efficiency of SQream's GPU acceleration reduces the compute time and energy consumption compared to traditional CPU-based systems, which translates into lower cloud processing costs. Organizations can redirect these savings toward other strategic initiatives, allowing them to invest more in innovation and growth.
The future of ML development with SQream on OCI
In an era where rapid AI and ML development is essential for maintaining a competitive edge, SQream on OCI emerges as a transformative force that addresses the critical challenges of data preparation and team productivity. By drastically reducing the time and complexity associated with data collection, cleaning, integration, and transformation, SQream empowers data scientists to move from concept to production with unprecedented speed and accuracy. The 90% data preparation time reduction isn't just a statistic. It's a catalyst for innovation, enabling organizations to unlock new levels of efficiency and success.
SQream on OCI empowers data scientists to take control of tasks that once required extensive support, reducing dependencies and enabling real-time adjustments. By streamlining workflows and eliminating bottlenecks, SQream enhances collaboration and accelerates time-to-market. This transformation extends beyond time savings because using GPU acceleration and cloud-based infrastructure empower organizations to scale data processing capabilities without the need for extensive hardware investments. Combined with lower operational costs, this structure frees up resources for strategic initiatives, fostering a culture of innovation and growth.
Together, these changes underscore the profound impact of SQream on OCI, not just on individual projects but on the overall agility, efficiency, and resilience of the organization. As companies continue to navigate the complexities of AI and machine learning, the ability to streamline processes, empower teams, and optimize resources will be key to sustaining a competitive edge. SQream on OCI isn't just a technological advancement. It's a strategic enabler that empowers organizations to fully capitalize on their data assets, drive meaningful outcomes, and thrive in the rapidly evolving digital landscape.
To discover how SQream and Oracle can revolutionize your ML development, visit our comprehensive suite of resources or schedule a meeting with our team. To learn more about how SQream on Oracle Cloud Infrastructure can accelerate your ML development journey, contact us today.