With machine learning (ML) and artificial intelligence (AI) applications becoming more business-critical, organizations are in the race to advance their AI/ML capabilities. To realize the full potential of AI/ML, having the right underlying machine learning platform is a prerequisite.
Today’s machine learning platforms are undergoing rapid, fundamental innovations at an architectural level. Meanwhile, organizations are facing a multitude of challenges – data silos, fast-growing training data, underutilization of expensive compute resources, lack of elasticity and flexibility. Legacy data platforms just aren’t up to the task.
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In an ideal world, you will break down disparate data silos, have an efficient model training pipeline, achieve high ROI, and scale easily. To help achieve these goals, below are some considerations when choosing a machine learning platform.
End-to-end machine learning pipelines consist of several steps – data preprocessing, cleansing, model training, inference. The training phase is the most time-consuming and resource-intensive, typically utilizing CPUs for fetching data and preprocessing and GPUs for computation. However, with the advancement of computation technology, data access has become the bottleneck and is often overlooked.
Areas that require significant attention are read latency, write performance, and I/O throughput. Examine these metrics and optimize I/O to continuously feed data to training on GPU instances without idle cycles.
Manage data access by parallelizing data loading, data preprocessing, and training. This parallelization maintains efficient resource utilization and will reduce end-to-end training time by mitigating the I/O bottlenecks. By optimizing your data access, you will benefit from shorter time-to-value and higher ROI because of increased GPU utilization.
Machine learning is all about data. The more data the model ingests, the closer it comes to generating valuable insights. Data silos scattered across the organization remain a problem for ML initiatives. A machine learning platform needs to be able to pull data from on-premises, cloud, and edge sources and keep a single source of truth. The main challenges are the overhead of managing data copies, integration headaches, privacy concerns, and latency issues.
Centralizing your data was a best practice decades ago, but such a paradigm is no longer a referral choice. Moving data across silos is time-consuming, expensive, and error-prone, and it poses unnecessary security risks. Having a single source of truth for data doesn’t mean pulling together data from disparate silos. Instead, virtualizing allows you to manage data across silos: Create virtual views of your data by abstracting data access across storage systems, and presenting the data to machine learning applications. In addition, you can enforce security controls and authentications to your data. As a result, data will no longer be siloed, but rather accessible across the entire organization, from edge to cloud, without having to be moved. It makes things a lot easier for the team managing the platform and for the data consumers at the same time.
More enterprises are migrating machine learning workloads to multiple public or private clouds as the cloud offerings and toolsets mature. The goal is to modernize with the right mix of hybrid- and multi-cloud to optimize cost, performance, security, and agility. You can protect existing investments and benefit from the cloud’s productivity advantages while keeping your data assets under control.
Grow your machine learning business by embracing the hybrid and multi-cloud model. Build a roadmap and prepare for infrastructure to be spread across an on-premises data lake and a public cloud. Start by moving some busy workloads from an on-premises data lake to the cloud with the right cloud migration toolset. As cloud vendors constantly innovate and compete with differentiated capabilities, pick the solution that can simplify your data management and provide consistent capabilities across hybrid environments on-premises and in the cloud – both private and public. You will be able to get the best of both worlds, enjoy elasticity and agility in the cloud while maintaining tight control of your on-premise assets.
Armed with the ability to break down disparate data silos, achieve high ROI and efficient model training, scale easily, and remain infrastructure-agnostic, organizations can focus on unlocking ML’s full potential. By leveraging a powerful machine learning platform, you will enhance the customer and employee experience, provide more innovative products and services, and optimize operations to reduce costs, gain efficiencies, and gain an edge over your competitors.