Computing on the Edge: How GPUs are Shaping the Future

The ability to process and analyze vast amounts of information quickly and efficiently is no longer a competitive advantage—it’s a survival imperative. As data volumes continue to explode, many organizations are still grappling with the limitations of traditional CPU-based data processing. The digital landscape has reached a tipping point where traditional approaches to data processing and analytics are no longer sufficient. Organizations are now confronted with the need to accommodate ever-larger datasets, extract insights at unprecedented speeds, and scale seamlessly—all while managing costs effectively. This challenge is compounded by the rapid advancements in AI and machine learning, which demand more data processing, compute, manpower, infrastructure, and efficiency than ever before. More than just a technological issue, this represents a fundamental shift in what organizations could and should expect from their data projects. The emergence of GPU acceleration has ushered in a new era, enabling organizations to break free from the constraints of outdated systems and unlock the full potential of their data. The Paradox of Modern CPUs: Despite increases in CPU power, traditional CPU-based data processing architectures struggle to keep up with modern analytics workloads. These systems were not designed to handle the scale, complexity, and speed required by today’s data-driven enterprises. Organizations face latency, delayed insights, and missed opportunities—not only due to the technology itself but also because of the widespread acceptance of these bottlenecks as unavoidable. Outdated, batch-based systems frustrate customers and executives alike, wasting energy and infrastructure resources. These delays are especially detrimental to AI and ML projects, which take longer to develop or even risk failure due to their intensive computational demands. The emergence of Generative AI has further highlighted this tipping point, creating demands beyond what CPUs can handle efficiently or affordably. This urgent need for change has brought the parallel processing capabilities of GPUs to the forefront, offering a transformative approach that enables real-time/near real-time insights and supports complex AI initiatives. The Impact of GPUs: Massive Acceleration for Data, Data Science, Machine Learning, and AI The shift from traditional CPU-based processing to GPU acceleration marks a pivotal moment in data analytics. Despite the sequential processing strengths of CPUs, they are increasingly inadequate for today’s data-driven demands. The traditional ecosystem, developed over 40 years ago, struggles to manage the sheer scale and complexity of modern data sets. This often forces organizations to rely on large teams of engineers, deploy a plethora of disparate data tools, or incur outsized cloud costs to piece together solutions. These approaches are not only resource-intensive but also unsustainable in the long run. GPUs, with their inherent parallel processing capabilities, present a compelling alternative by effectively handling the high compute demands of modern analytics, machine learning, and AI applications. However, unlocking the true potential of GPUs requires more than just hardware; it demands advanced data processing solutions that can seamlessly integrate into existing data architectures. Innovations designed for GPU data processing offer a streamlined and efficient approach to data handling that reduces dependency on multiple tools and teams. This shift is not just an enhancement but a necessary evolution to meet the challenges of today’s data landscape efficiently and sustainably. By adopting GPU acceleration with such innovations, organizations can achieve these goals with smaller teams, less infrastructure, and dramatically lower cloud bills. Innovative Data Processing Solutions’ Role in GPU Acceleration Advanced data processing solutions like SQream, for example, are at the forefront of this transformation, utilizing GPUs not just for rendering but for executing SQL and processing large-scale data. This innovative approach enables organizations to harness the massive parallelism and computational power of GPUs while integrating seamlessly with existing CPU-based systems. The benefits of this GPU-accelerated approach are profound: Faster Time to Insight: GPU-powered solutions drastically reduce the time required to process and analyze data. For example, a large regional bank integrated GPU acceleration into their BI reporting, reducing the time required for Microsoft Power BI reports from 98 minutes to just 11 minutes—a time savings of over 88%. This dramatic decrease in latency enables organizations to gain insights much faster, facilitating quicker and more informed decision-making. Deeper Analysis: With the capacity to handle larger datasets and perform more complex analyses, cutting-edge data processing tools enable organizations to conduct more comprehensive data explorations. A notable example is a large global semiconductor company, which found that complex queries that previously took two weeks and often failed in their CPU-based environment were completed in just 30 minutes after moving to a GPU-accelerated solution. This breakthrough not only unlocked new insights but also empowered the team to tackle previously daunting analytics tasks with renewed confidence. Improved Productivity: The efficiency gained from GPU acceleration translates into increased productivity for both production and development. A global manufacturer, for example, reduced their data preparation time by over 90%, allowing them to expand their data science team from 80 to over 300. This remarkable increase in productivity eliminated backlogs and significantly enhanced factory yield. Additionally, advanced solutions empower enterprises to democratize data access with significantly less data engineering resources and hardware. By enabling widespread access to data, organizations can significantly boost the productivity of their knowledge workers, facilitating a more informed and agile workforce. Cost Efficiency: GPUs, combined with advanced data processing tools, offer a cost-effective solution by providing higher performance per dollar compared to traditional CPU-based systems. For instance, a leading financial services firm sought to lower their Snowflake costs and observed an impressive 80% savings on complex workloads by leveraging GPU acceleration. This efficiency helps organizations manage their hardware and cloud compute costs more effectively. GPU acceleration not only reduces cloud computing expenses but also delivers significant value for enterprises with their own data centers. A leading manufacturer compared traditional systems with GPU-accelerated solutions and found they could achieve their objectives with just one-tenth of the hardware required for a CPU-based strategy, using only 25 servers instead of 250. This dramatic reduction in hardware needs translates to lower infrastructure costs and greater operational efficiency. Additionally, the decreased hardware footprint reduces electricity usage, generates less heat, and supports more eco-friendly operations, aligning with sustainability goals and further enhancing the overall value of GPU acceleration. Key Advantages of GPU Acceleration with Advanced Data Processing Tools By integrating GPUs into existing data processing strategies, organizations can achieve significant acceleration for large or complex workloads or data pipelines. While CPUs are well-suited for handling many typical data processing tasks, GPUs excel in scenarios where high compute demands and large-scale data challenges are prevalent. This targeted application of GPU acceleration transforms the speed and efficiency of handling these intensive workloads, unlocking new levels of performance, value, and insight. Massive Parallelism: GPUs, such as NVIDIA’s L40s with over 18,000 cores per GPU, are designed for massive parallelism, far exceeding the core counts of high-performance CPUs, which typically range from 16 to 64 cores. This vast number of cores enables GPUs to tackle complex problems with unprecedented speed. Each core can process a segment of a query simultaneously, automatically breaking down queries into smaller parts for parallel processing. More impressively, GPUs can handle tens of thousands, if not hundreds of thousands, of queries simultaneously. This capability is especially beneficial for operations like joins, aggregations, and sorting. For example, during a join operation on large tables, the workload is distributed across thousands of cores, significantly accelerating execution time and achieving results much more efficiently than traditional CPU-based systems, both in terms of speed and cost-effectiveness. Scalability and Flexibility: Scaling up with advanced GPU-accelerated tools provides true linear scalability, a significant advantage over traditional CPU environments where performance often degrades as systems scale. Unlike legacy systems, which require extensive metadata coordination to manage data storage and partitioning, or data rebalancing to handle large data movements across the network, or even data engineering bottlenecks, GPU-accelerated tools effectively leverage GPUs to eliminate these inefficiencies. This results in seamless and efficient scaling. The parallel processing power of GPUs ensures that query performance remains consistently high, even as data volumes grow. This ability to scale linearly without a degradation in throughput is crucial for organizations dealing with big data, allowing them to maintain optimal data processing efficiency. High Data Throughput: Using advanced data processing tools, GPUs can process data up to 30 times faster than CPUs, dramatically reducing bottlenecks and enhancing overall performance. This high-speed throughput is crucial for managing large datasets or frequently changing data, as it enables rapid data movement through the pipeline and accelerates query results, even for the most data-intensive operations. While some might assume they can achieve similar results by simply scaling up compute resources in the cloud, this approach often falls short due to inherent limitations. Just one example is that network transfer speeds cannot be improved through parallelism alone. Instead, reducing data transfer time requires a different approach. Advanced data processing tools address this challenge by using GPUs to compress data up to 100 times faster than traditional methods, providing astounding acceleration. Moreover, these tools integrate seamlessly with existing data pipelines, offering substantial performance boosts without necessitating a complete system overhaul. This capability is particularly valuable when modernizing legacy systems, as traditional modernization efforts often involve costly and time-consuming rewrites. By complementing rather than disrupting current systems, advanced solutions enable organizations to modernize their data infrastructure with minimal effort and cost, avoiding the complexities and expenses typically associated with large-scale system overhauls. Efficient Data Handling and Dynamic Data Processing: GPUs are specifically designed for parallel mathematical operations, a capability that advanced tools leverage to process large volumes of data simultaneously. By handling operations such as filtering, aggregation, sorting, and joins in parallel, GPUs significantly enhance query execution speed and efficiency. This approach contrasts sharply with traditional CPU-based systems, which rely on less efficient, serial processing algorithms. The result is a substantial improvement in performance, enabling faster and more effective data handling for complex queries and large datasets. Notably, these gains in efficiency are achieved without requiring extensive teams of data engineers or additional hardware, making advanced GPU-accelerated tools a cost-effective solution for modern data challenges. The parallel architecture of GPUs also supports real-time updates and processing for frequently changing data. This capability is crucial for real-time analytics and applications that require immediate insights, as it ensures that the most current data is always available for analysis. Traditional CPU-based systems often struggle with the dynamic nature of real-time data, resulting in delays and missed opportunities. By leveraging GPUs for these tasks, organizations can ensure that their data remains up-to-date and actionable, facilitating timely decision-making and responsive operations. ML Ops Acceleration: Advanced solutions utilizing GPUs revolutionize machine learning operations by dramatically enhancing both individual and team productivity, particularly in data-intensive environments. The ability to process vast amounts of data rapidly accelerates model development, training, and deployment, enabling data scientists to work more efficiently and iterate more quickly. Accelerated Data Pipelines: In environments dealing with extensive data, GPU-powered tools can reduce data preparation time by up to 90%. This reduction allows data scientists to work up to 500% more efficiently, handling tasks traditionally managed by data engineers and eliminating bottlenecks. The outcome is a more agile, efficient, and responsive data science operation, which drives faster and more effective model development and deployment. Enhanced Team Collaboration: Advanced solutions facilitate large team collaboration by enabling the reuse of data products. For new projects, data scientists can leverage existing work, covering 50-60% of their needs, which is particularly beneficial when working with large datasets. Each new team member enhances overall effectiveness without adding to data engineering bottlenecks, resulting in exponential productivity growth. By enabling self-sufficiency and fostering synergy in data-rich environments, GPU-powered tools accelerate ML development with smaller teams, reduced infrastructure needs, and significantly lower cloud costs, making them indispensable for modern data science operations. Unlocking New Potentials with GPU Acceleration:Integrating GPU acceleration represents a revolutionary shift in data processing and analytics. Advanced solutions harness the power of GPUs to deliver unparalleled performance, allowing businesses to process and analyze large datasets with unprecedented speed and efficiency. By leveraging the massive parallelism, high memory bandwidth, and seamless scalability of GPUs, organizations can overcome the limitations of traditional CPU-based systems and unlock new levels of insight and productivity. As data volumes continue to grow and the demands of AI and machine learning evolve, GPU acceleration stands as a critical enabler of agile, efficient, and cost-effective data operations. Conclusion The rapid advancement in data volumes and the increasing demands of AI and machine learning have created a critical need for more efficient and scalable data processing solutions. As traditional CPU-based systems struggle to keep pace, GPU acceleration stands out as a transformative technology capable of addressing the most demanding data challenges. Advanced solutions harness the power of GPUs to deliver unprecedented speed, efficiency, and cost-effectiveness in data processing. By integrating GPUs with cutting-edge data processing tools, organizations can achieve remarkable improvements in performance across several key areas: faster time to insight, deeper and more complex analysis, and significantly enhanced productivity for data scientists and analysts. Additionally, this approach provides substantial cost savings by reducing the need for extensive hardware investments and lowering cloud compute costs. The ability to seamlessly integrate GPU acceleration into existing data pipelines—without requiring major system overhauls—further enhances the value of these solutions. This capability not only accelerates data handling and processing but also ensures a smooth transition that maximizes return on investment. As organizations continue to navigate the complexities of big data and AI, leveraging GPU acceleration presents a powerful opportunity to stay ahead of the curve. Embracing this technology allows organizations to optimize their data operations, achieve greater efficiency, and drive strategic success while managing costs effectively. Deborah Leff, SVP Data Science and Machine Learning, SQream The ability to process and analyze vast amounts of information quickly and efficiently is no longer a competitive advantage—it’s a survival imperative. As data volumes continue to explode, many organizations are still grappling with the limitations of traditional CPU-based data processing. The digital landscape has reached a tipping point where traditional approaches to data processing and analytics are no longer sufficient. Organizations are now confronted with the need to accommodate ever-larger datasets, extract insights at unprecedented speeds, and scale seamlessly—all while managing costs effectively. This challenge is compounded by the rapid advancements in AI and machine learning, which demand more data processing, compute, manpower, infrastructure, and efficiency than ever before. More than just a technological issue, this represents a fundamental shift in what organizations could and should expect from their data projects. The emergence of GPU acceleration has ushered in a new era, enabling organizations to break free from the constraints of outdated systems and unlock the full potential of their data. The Paradox of Modern CPUs : Despite increases in CPU power, traditional CPU-based data processing architectures struggle to keep up with modern analytics workloads. These systems were not designed to handle the scale, complexity, and speed required by today’s data-driven enterprises. Organizations face latency, delayed insights, and missed opportunities—not only due to the technology itself but also because of the widespread acceptance of these bottlenecks as unavoidable. The Paradox of Modern CPUs Outdated, batch-based systems frustrate customers and executives alike, wasting energy and infrastructure resources. These delays are especially detrimental to AI and ML projects, which take longer to develop or even risk failure due to their intensive computational demands. The emergence of Generative AI has further highlighted this tipping point, creating demands beyond what CPUs can handle efficiently or affordably. This urgent need for change has brought the parallel processing capabilities of GPUs to the forefront, offering a transformative approach that enables real-time/near real-time insights and supports complex AI initiatives. The Impact of GPUs : Massive Acceleration for Data, Data Science, Machine Learning, and AI The Impact of GPUs The shift from traditional CPU-based processing to GPU acceleration marks a pivotal moment in data analytics. Despite the sequential processing strengths of CPUs, they are increasingly inadequate for today’s data-driven demands. The traditional ecosystem, developed over 40 years ago, struggles to manage the sheer scale and complexity of modern data sets. This often forces organizations to rely on large teams of engineers, deploy a plethora of disparate data tools, or incur outsized cloud costs to piece together solutions. These approaches are not only resource-intensive but also unsustainable in the long run. GPUs, with their inherent parallel processing capabilities, present a compelling alternative by effectively handling the high compute demands of modern analytics, machine learning, and AI applications. However, unlocking the true potential of GPUs requires more than just hardware; it demands advanced data processing solutions that can seamlessly integrate into existing data architectures. Innovations designed for GPU data processing offer a streamlined and efficient approach to data handling that reduces dependency on multiple tools and teams. This shift is not just an enhancement but a necessary evolution to meet the challenges of today’s data landscape efficiently and sustainably. By adopting GPU acceleration with such innovations, organizations can achieve these goals with smaller teams, less infrastructure, and dramatically lower cloud bills. Innovative Data Processing Solutions’ Role in GPU Acceleration Innovative Data Processing Solutions’ Role in GPU Acceleration Advanced data processing solutions like SQream, for example, are at the forefront of this transformation, utilizing GPUs not just for rendering but for executing SQL and processing large-scale data. This innovative approach enables organizations to harness the massive parallelism and computational power of GPUs while integrating seamlessly with existing CPU-based systems. The benefits of this GPU-accelerated approach are profound: Faster Time to Insight : GPU-powered solutions drastically reduce the time required to process and analyze data. For example, a large regional bank integrated GPU acceleration into their BI reporting, reducing the time required for Microsoft Power BI reports from 98 minutes to just 11 minutes—a time savings of over 88%. This dramatic decrease in latency enables organizations to gain insights much faster, facilitating quicker and more informed decision-making. Faster Time to Insight Deeper Analysis : With the capacity to handle larger datasets and perform more complex analyses, cutting-edge data processing tools enable organizations to conduct more comprehensive data explorations. A notable example is a large global semiconductor company, which found that complex queries that previously took two weeks and often failed in their CPU-based environment were completed in just 30 minutes after moving to a GPU-accelerated solution. This breakthrough not only unlocked new insights but also empowered the team to tackle previously daunting analytics tasks with renewed confidence. Deeper Analysis Improved Productivity : The efficiency gained from GPU acceleration translates into increased productivity for both production and development. A global manufacturer, for example, reduced their data preparation time by over 90%, allowing them to expand their data science team from 80 to over 300. This remarkable increase in productivity eliminated backlogs and significantly enhanced factory yield. Additionally, advanced solutions empower enterprises to democratize data access with significantly less data engineering resources and hardware. By enabling widespread access to data, organizations can significantly boost the productivity of their knowledge workers, facilitating a more informed and agile workforce. Improved Productivity Cost Efficiency : GPUs, combined with advanced data processing tools, offer a cost-effective solution by providing higher performance per dollar compared to traditional CPU-based systems. For instance, a leading financial services firm sought to lower their Snowflake costs and observed an impressive 80% savings on complex workloads by leveraging GPU acceleration. This efficiency helps organizations manage their hardware and cloud compute costs more effectively. Cost Efficiency GPU acceleration not only reduces cloud computing expenses but also delivers significant value for enterprises with their own data centers. A leading manufacturer compared traditional systems with GPU-accelerated solutions and found they could achieve their objectives with just one-tenth of the hardware required for a CPU-based strategy, using only 25 servers instead of 250. This dramatic reduction in hardware needs translates to lower infrastructure costs and greater operational efficiency. Additionally, the decreased hardware footprint reduces electricity usage, generates less heat, and supports more eco-friendly operations, aligning with sustainability goals and further enhancing the overall value of GPU acceleration. Key Advantages of GPU Acceleration with Advanced Data Processing Tools Key Advantages of GPU Acceleration with Advanced Data Processing Tools By integrating GPUs into existing data processing strategies, organizations can achieve significant acceleration for large or complex workloads or data pipelines. While CPUs are well-suited for handling many typical data processing tasks, GPUs excel in scenarios where high compute demands and large-scale data challenges are prevalent. This targeted application of GPU acceleration transforms the speed and efficiency of handling these intensive workloads, unlocking new levels of performance, value, and insight. Massive Parallelism : GPUs, such as NVIDIA’s L40s with over 18,000 cores per GPU, are designed for massive parallelism, far exceeding the core counts of high-performance CPUs, which typically range from 16 to 64 cores. This vast number of cores enables GPUs to tackle complex problems with unprecedented speed. Each core can process a segment of a query simultaneously, automatically breaking down queries into smaller parts for parallel processing. More impressively, GPUs can handle tens of thousands, if not hundreds of thousands, of queries simultaneously. This capability is especially beneficial for operations like joins, aggregations, and sorting. For example, during a join operation on large tables, the workload is distributed across thousands of cores, significantly accelerating execution time and achieving results much more efficiently than traditional CPU-based systems, both in terms of speed and cost-effectiveness. Massive Parallelism Scalability and Flexibility : Scaling up with advanced GPU-accelerated tools provides true linear scalability, a significant advantage over traditional CPU environments where performance often degrades as systems scale. Unlike legacy systems, which require extensive metadata coordination to manage data storage and partitioning, or data rebalancing to handle large data movements across the network, or even data engineering bottlenecks, GPU-accelerated tools effectively leverage GPUs to eliminate these inefficiencies. This results in seamless and efficient scaling. The parallel processing power of GPUs ensures that query performance remains consistently high, even as data volumes grow. This ability to scale linearly without a degradation in throughput is crucial for organizations dealing with big data, allowing them to maintain optimal data processing efficiency. Scalability and Flexibility High Data Throughput : Using advanced data processing tools, GPUs can process data up to 30 times faster than CPUs, dramatically reducing bottlenecks and enhancing overall performance. This high-speed throughput is crucial for managing large datasets or frequently changing data, as it enables rapid data movement through the pipeline and accelerates query results, even for the most data-intensive operations. High Data Throughput While some might assume they can achieve similar results by simply scaling up compute resources in the cloud, this approach often falls short due to inherent limitations. Just one example is that network transfer speeds cannot be improved through parallelism alone. Instead, reducing data transfer time requires a different approach. Advanced data processing tools address this challenge by using GPUs to compress data up to 100 times faster than traditional methods, providing astounding acceleration. Moreover, these tools integrate seamlessly with existing data pipelines, offering substantial performance boosts without necessitating a complete system overhaul. This capability is particularly valuable when modernizing legacy systems, as traditional modernization efforts often involve costly and time-consuming rewrites. By complementing rather than disrupting current systems, advanced solutions enable organizations to modernize their data infrastructure with minimal effort and cost, avoiding the complexities and expenses typically associated with large-scale system overhauls. Efficient Data Handling and Dynamic Data Processing : GPUs are specifically designed for parallel mathematical operations, a capability that advanced tools leverage to process large volumes of data simultaneously. By handling operations such as filtering, aggregation, sorting, and joins in parallel, GPUs significantly enhance query execution speed and efficiency. This approach contrasts sharply with traditional CPU-based systems, which rely on less efficient, serial processing algorithms. The result is a substantial improvement in performance, enabling faster and more effective data handling for complex queries and large datasets. Notably, these gains in efficiency are achieved without requiring extensive teams of data engineers or additional hardware, making advanced GPU-accelerated tools a cost-effective solution for modern data challenges. Efficient Data Handling and Dynamic Data Processing The parallel architecture of GPUs also supports real-time updates and processing for frequently changing data. This capability is crucial for real-time analytics and applications that require immediate insights, as it ensures that the most current data is always available for analysis. Traditional CPU-based systems often struggle with the dynamic nature of real-time data, resulting in delays and missed opportunities. By leveraging GPUs for these tasks, organizations can ensure that their data remains up-to-date and actionable, facilitating timely decision-making and responsive operations. ML Ops Acceleration : Advanced solutions utilizing GPUs revolutionize machine learning operations by dramatically enhancing both individual and team productivity, particularly in data-intensive environments. The ability to process vast amounts of data rapidly accelerates model development, training, and deployment, enabling data scientists to work more efficiently and iterate more quickly. ML Ops Acceleration Accelerated Data Pipelines : In environments dealing with extensive data, GPU-powered tools can reduce data preparation time by up to 90%. This reduction allows data scientists to work up to 500% more efficiently, handling tasks traditionally managed by data engineers and eliminating bottlenecks. The outcome is a more agile, efficient, and responsive data science operation, which drives faster and more effective model development and deployment. Accelerated Data Pipelines Enhanced Team Collaboration : Advanced solutions facilitate large team collaboration by enabling the reuse of data products. For new projects, data scientists can leverage existing work, covering 50-60% of their needs, which is particularly beneficial when working with large datasets. Each new team member enhances overall effectiveness without adding to data engineering bottlenecks, resulting in exponential productivity growth. Enhanced Team Collaboration By enabling self-sufficiency and fostering synergy in data-rich environments, GPU-powered tools accelerate ML development with smaller teams, reduced infrastructure needs, and significantly lower cloud costs, making them indispensable for modern data science operations. Unlocking New Potentials with GPU Acceleration:Integrating GPU acceleration represents a revolutionary shift in data processing and analytics. Advanced solutions harness the power of GPUs to deliver unparalleled performance, allowing businesses to process and analyze large datasets with unprecedented speed and efficiency. By leveraging the massive parallelism, high memory bandwidth, and seamless scalability of GPUs, organizations can overcome the limitations of traditional CPU-based systems and unlock new levels of insight and productivity. As data volumes continue to grow and the demands of AI and machine learning evolve, GPU acceleration stands as a critical enabler of agile, efficient, and cost-effective data operations. Conclusion Conclusion The rapid advancement in data volumes and the increasing demands of AI and machine learning have created a critical need for more efficient and scalable data processing solutions. As traditional CPU-based systems struggle to keep pace, GPU acceleration stands out as a transformative technology capable of addressing the most demanding data challenges. Advanced solutions harness the power of GPUs to deliver unprecedented speed, efficiency, and cost-effectiveness in data processing. By integrating GPUs with cutting-edge data processing tools, organizations can achieve remarkable improvements in performance across several key areas: faster time to insight, deeper and more complex analysis, and significantly enhanced productivity for data scientists and analysts. Additionally, this approach provides substantial cost savings by reducing the need for extensive hardware investments and lowering cloud compute costs. The ability to seamlessly integrate GPU acceleration into existing data pipelines—without requiring major system overhauls—further enhances the value of these solutions. This capability not only accelerates data handling and processing but also ensures a smooth transition that maximizes return on investment. As organizations continue to navigate the complexities of big data and AI, leveraging GPU acceleration presents a powerful opportunity to stay ahead of the curve. Embracing this technology allows organizations to optimize their data operations, achieve greater efficiency, and drive strategic success while managing costs effectively. Deborah Leff, SVP Data Science and Machine Learning, SQream Deborah Leff, SVP Data Science and Machine Learning, SQream Deborah Leff , Deborah Leff SVP Data Science and Machine Learning, SQream