Machine Learning - eBook (EN)

Modernize machine learning development at scale

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Machine learning (ML) has become a core technology ingredient for organizations to drive real-world innovation. However, despite the rapid expansion of machine learning, the ability to harness its power comes with several challenges: The challenges of harnessing machine learning at scale Scaling infrastructure - To achieve business objectives with machine learning, customers need scalable infrastructure and purpose-built machine learning tools. The cost of investing in scalable infrastructure and integrating different tools can be excessive; the expenditure often exceeds the cost of developing the machine learning code itself. Responsible use of ML - The explosion of datasets has caused rapid growth in the number of people interacting or building with machine learning. From data security and privacy to bias mitigation in data, machine learning comes with an inherent responsibility for all those who harness its benefits. Putting data science best practices in place ensures the responsible use of machine learning within organizations. Difficulty in acquiring data science skill sets - A shortage of skilled data science professionals makes it difficult to acquire new talent or train existing talent for machine learning development. A machine learning service that caters to all data science skill levels, from beginners to experts, helps to avoid the one-size-fits-all approach often found within homegrown machine learning platforms. Managing costs - The escalating growth of machine learning within organizations necessitates managing costs, including maximizing the efficiency and utilization of resources such as GPUs and CPUs. 2 2

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