AI Technology
Things to check if your business utilizes AI
Do I need to COMPRESS my AI model? : the short answer is “YES” — and here’s why.
As the AI revolution quickly reshapes our business landscape, an increasing number of organizations are implementing a state of the art AI model. Even non-tech companies in domains such as healthcare, security and marketing are integrating AI into their business architecture to enhance decision making and further expand business opportunities.
Despite the eager ambition of companies to incorporate the new technology into their industries, when it comes to utilizing and operating an AI model in the long term, there is a lengthy checklist of things to consider.
Here are some essential questions your team should be asking before moving forward:
- What complications will the AI model create?
- What are the expected operational costs?
- Is information privacy attainable when deploying an AI model?
- How can we assure the quality of the AI model throughout the whole operation process?
To paint a detailed scenario of what these questions mean, let’s look at the key problems your company is likely to face.
Latency in performance
You’ve already spent a fortune on machine learning(ML) operations. From model selection, training and evaluation to creating the pipelines, the ML engineering team is already worn down and exhausted from this process alone. Now here’s a new problem — you’re getting bombarded with emails from the customer experiences team. The bottom line is that your model is too big and too slow when it comes to inference. The company is losing potential users due to latency. Speed is an essential factor in customer experience, and latency is a critical blow. Back in 2006, former Google VP stated that tests on users’ responsiveness to speed show that half a second delay on returning results not only kills user satisfaction but also causes a 20% drop in traffic. Likewise, your users will not be patiently waiting around for the AI model to generate results whether that be photos or texts. They will leave and perhaps never come back, resulting in net losses for the company.
Sky-high AI operation costs
Even without taking into account the baseline costs for creating the AI model itself, the bill for AI model operation and maintenance won’t be looking pretty. Highly dependent on technical infrastructure, the larger and more complex your AI model is, the more painfully costly it will be to support your operation environment. Expenditures on GPUs(Graphics Processing Unit) are non-negotiable in neural network training and whether you decide to purchase or rent on the cloud, your hardware is likely to cost you an arm and a leg. Further down the road, the quantity and complexity of an AI model’s inference computations will only grow over time, leading to a greater payout on GPU and energy resources.
The question is, is it in your company’s budget to handle such outrageously expensive operations? It would be a lie to state that every company’s finances are bulletproof, capable of fully equipping high performing server-grade GPUs for inference. Without properly pre-assessing the rapidly increasing operational costs that will occur and devising a plan to save money, the newly incorporated AI could put your company in a deficit rather than help achieve profit.
On-device AI
Privacy and protection of users’ personal information have been a relentlessly pressing issue. A greater number of companies are regarding it as a matter high priority, thus placing AI on an edge device(i.e. IoT devices, sensors, smartphones) has become a popular decision. Deploying AI on an edge device prevents sensitive information from being transmitted to external devices. Yet the detrimental disadvantage of AI models being deployed on edge devices is that its computing power and storage capacity is limited. This makes it next to impossible to load a complex, heavyweight model onto an edge device.
Without directly addressing the aforementioned problems and complications, it is unclear whether the AI model based service can even take off. A feasible and suitable solution to these circumstances is ‘compression.’
Acceleration through compression
AI model compression
Compress and squeeze the neural network so that less computations are needed to take place, achieving computational efficiency. Deploying your AI model on an edge device for personal data security will even become possible as less computational power is required. Compression also solves the problem of latency because reduced computations accelerate the AI model. Your company’s financial resources will be less drained since it can reduce storage requirements of GPUs and require less energy consumption.
In the test case lead by a data science team at Microsoft, the team attempted to retrieve information from medical documents and extract answers to clinical questions. Utilizing compression methods, average runtime per document was decreased from 17.5 milliseconds to 2.05 milliseconds. This is an increase of inference speed by approximately 8.5 times. If 1000 documents were to be processed every 30 minutes, only two Tesla V100 GPUs would be in parallel operation in contrast to the average of ten GPUs running the same model before compression. Operation cost would go down to $6.12 from $30.60 per hour. A net savings of $48,960 would gather in one year through compression.
Skeptics of AI model compression would argue that a compressed model could be exposed to risks of accuracy decline and overall quality may be compromised. Here at SqueezeBits, we push boundaries using the latest compression technology so that losing accuracy is the last thing you have to worry about. Your AI model’s performance accuracy will be ensured.
Exponential business growth from utilizing AI models is not a delusional pipe dream. However, it only becomes a reality when you are able to solve the complications of AI and cut operational costs through AI compression and acceleration. Don’t be bound by the limitations of your AI model, unlock the potential of it with SqueezeBits!
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