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.
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