Hyper-Converged Infrastructure, Application Infrastructure

Softchoice Achieves Amazon Web Services (AWS) Migration Competency Status

Softchoice
Softchoice has achieved Amazon Web Services (AWS) Migration Competency status. To receive the designation, solution partners must have AWS expertise and deliver solutions seamlessly on AWS.

The AWS Migration Competency recognizes Softchoice’s excellence in helping customers move to AWS through all phases of complex migration projects: discovery, planning, migration and operations. It further strengthens Softchoice’s offering as an AWS Partner with proven technical proficiency and customer success and adds to Softchoice’s other AWS competencies, which include:

  • Nonprofit Consulting
  • Storage Consulting
  • Microsoft Workloads Consulting

“Softchoice is proud to have earned the AWS Migration Competency Status. The agility, breadth of services, and pace of innovation that AWS offers are pivotal to our vision and value proposition. “As a strategic partner, our team’s focus is to help customers succeed in their digital transformation agendas. This competency is an endorsement of our ability to strategize and execute cloud migrations for customers across sectors and stages of maturity.”

Chris Woodin, Vice President, Cloud Business Unit at Softchoice

AWS enables scalable, flexible, and cost-effective solutions for organizations ranging from startups to global enterprises. The AWS Competency Program supports the integration and deployment of these solutions by helping customers find AWS partners, like Softchoice, with deep industry experience and expertise to support AWS-led transformation projects. Softchoice recently completed a critical AWS migration for Nortek, a leading manufacturer of HVAC systems for residential, commercial and data center locations.

“Softchoice’s performance, reliability, security, and technical expertise are ahead of every partner we evaluated. Their understanding of and relationship with public clouds like AWS were also a major draw for us to work with them,” says Brad Green, Group Vice President and CIO of Nortek. “Softchoice’s solution-first mindset, consulting capabilities, and deep technical expertise with AWS were the pillars of our cloud migration. The team offered critical operational support, cloud management capabilities, and valuable advice at every step of our migration, helping us secure and modernize our systems and redesign our IT architecture for growth.”

With AWS, Softchoice helps organizations advance their digital transformation agendas, including developing and delivering new, differentiated digital customer experiences. Moving infrastructure, data, and application services to the cloud is a crucial enabler of this offering, and the AWS Migration Competency validates organizations’ trust in Softchoice’s approach and experience to follow best practices while delivering business value.

About Softchoice
Softchoice is a software-focused IT solutions provider that equips organizations to be agile and innovative, and for their people to be engaged, connected and creative at work. That means moving them to the cloud, helping them build the workplace of tomorrow, and enabling them to make smarter decisions about their technology portfolio.

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Business Wire | September 21, 2023

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PRWeb | October 12, 2023

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GlobeNewswire | October 19, 2023

Alluxio, the data platform company for all data-driven workloads, today introduced Alluxio Enterprise AI, a new high-performance data platform designed to meet the rising demands of Artificial Intelligence (AI) and machine learning (ML) workloads on an enterprise’s data infrastructure. Alluxio Enterprise AI brings together performance, data accessibility, scalability and cost-efficiency to enterprise AI and analytics infrastructure to fuel next-generation data-intensive applications like generative AI, computer vision, natural language processing, large language models and high-performance data analytics. To stay competitive and achieve stronger business outcomes, enterprises are in a race to modernize their data and AI infrastructure. On this journey, they find that legacy data infrastructure cannot keep pace with next-generation data-intensive AI workloads. 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Alluxio Enterprise Data is the next-gen version of Alluxio Enterprise Edition, and will continue to be the ideal choice for businesses focused primarily on analytic workloads. Accelerating End-to-End Machine Learning Pipeline Alluxio Enterprise AI enables enterprise AI infrastructure to be performant, seamless, scalable and cost-effective on existing data lakes. Alluxio Enterprise AI helps data and AI leaders and practitioners achieve four key objectives in their AI initiatives: high-performance model training and deployment to yield quick business results; seamless data access for workloads across regions and clouds; infinite scale that has been battle-tested at internet giant’s scale; and maximized return on investments by working with existing tech stack instead of costly specialized storage. With Alluxio Enterprise AI, enterprises can expect up to 20x faster training speed compared to commodity storage, up to 10x accelerated model serving, over 90% GPU utilization, and up to 90% lower costs for AI infrastructure. Alluxio Enterprise AI has a distributed system architecture with decentralized metadata to eliminate bottlenecks when accessing massive numbers of small files, typical of AI workloads. This provides unlimited scalability beyond legacy architectures, regardless of file size or quantity. The distributed cache is tailored to AI workload I/O patterns, unlike traditional analytics. Finally, it supports analytics and full machine learning pipelines - from ingestion to ETL, pre-processing, training and serving. Alluxio Enterprise AI includes the following key features: Epic Performance for Model Training and Model Serving - Alluxio Enterprise AI offers significant performance improvements to model training and serving on an enterprise’s existing data lakes. The enhanced set of APIs for model training can deliver up to 20x performance over commodity storage. For model serving, Alluxio provides extreme concurrency and up to 10x acceleration for serving models from offline training clusters for online inference. Intelligent Distributed Caching Tailored to I/O Patterns of AI Workloads - Alluxio Enterprise AI’s distributed caching feature enables AI engines to read and write data through the high performance Alluxio cache instead of slow data lake storage. Alluxio’s intelligent caching strategies are tailored to the I/O patterns of AI engines – large file sequential access, large file random access, and massive small file access. This optimization delivers high throughput and low latency for data-hungry GPUs. Training clusters are continuously fed data from the high-performance distributed cache, achieving over 90% GPU utilization. Seamless Data Access for AI Workloads Across On-prem and Cloud Environments - Alluxio Enterprise AI provides a single pane of glass for enterprises to manage AI workloads across diverse infrastructure environments easily. Providing a source of truth of data for the machine learning pipeline, the product fundamentally removes the bottleneck of data lake silos in large enterprises. Sharing data between different business units and geographical locations becomes seamless with a standard data access layer via the Alluxio Enterprise AI platform. New Distributed System Architecture, Battle-tested At Scale - Alluxio Enterprise AI builds on a new innovative decentralized architecture, DORA (Decentralized Object Repository Architecture). This architecture sets the foundation to provide infinite scale for AI workloads. It allows an AI platform to handle up to 100 billion objects with commodity storage like Amazon S3. Leveraging Alluxio’s proven expertise in distributed systems, this new architecture has addressed the ever-increasing challenges of system scalability, metadata management, high availability, and performance. “Performance, cost optimization and GPU utilization are critical for optimizing next-generation workloads as organizations seek to scale AI throughout their businesses,” said Mike Leone, Analyst, Enterprise Strategy Group. “Alluxio has a compelling offering that can truly help data and AI teams achieve higher performance, seamless data access, and ease of management for model training and model serving.” “We've collaborated closely with Alluxio and consider their platform essential to our data infrastructure,” said Rob Collins, Analytics Cloud Engineering Director, Aunalytics. “Aunalytics is enthusiastic about Alluxio's new distributed system for Enterprise AI, recognizing its immense potential in the ever-evolving AI industry.” “Our in-house-trained large language model powers our Q&A application and recommendation engines, greatly enhancing user experience and engagement,” said Mengyu Hu, Software Engineer in the data platform team, Zhihu. “In our AI infrastructure, Alluxio is at the core and center. Using Alluxio as the data access layer, we’ve significantly enhanced model training performance by 3x and deployment by 10x with GPU utilization doubled. We are excited about Alluxio’s Enterprise AI and its new DORA architecture supporting access to massive small files. This offering gives us confidence in supporting AI applications facing the upcoming artificial intelligence wave.” Deploying Alluxio in Machine Learning Pipelines According to Gartner, data accessibility and data volume/complexity is one the top three barriers to the implementation of AI techniques within an organization. Alluxio Enterprise AI can be added to the existing AI infrastructure consisting of AI compute engines and data lake storage. Sitting in the middle of compute and storage, Alluxio can work across model training and model serving in the machine learning pipeline to achieve optimal speed and cost. For example, using PyTorch as the engine for training and serving, and Amazon S3 as the existing data lake: Model Training: When a user is training models, the PyTorch data loader loads datasets from a virtual local path /mnt/alluxio_fuse/training_datasets. Instead of loading directly from S3, the data loader will load from the Alluxio cache instead. During training, the cached datasets will be used in multiple epochs, so the entire training speed is no longer bottlenecked by retrieving from S3. In this way, Alluxio speeds up training by shortening data loading and eliminates GPU idle time, increasing GPU utilization. After the models are trained, PyTorch writes the model files to S3 through Alluxio. Model Serving: The latest trained models need to be deployed to the inference cluster. Multiple TorchServe instances read the model files concurrently from S3. Alluxio caches these latest model files from S3 and serves them to inference clusters with low latency. 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