How to Reduce Risk and Time-to-Market in Deep Learning Development

AI is transforming entire industries. But implementing deep learning effectively can be a complex and daunting task, with many potential pitfalls along the way. To help you navigate the challenges of deep learning and maximize its potential for your organization, here are five best practices you should do as you start your AI project:

1. Define the problem you are trying to solve

Defining the deep learning problem is different from the business and product problems. It specifies the technical aspects of the project which include the task (Is it a single task or multiple?), deployment (What are the hardware, environment, and framework?), and performance (Does it have to be realtime? How about the price and user experience?).

2. Assemble the right team

While the exact size of the team depends on the scope and nature of the project, there are positions that are often required for the team to be not only functional but also effective. These are the DL research scientist/engineer (with task and domain experience), data scientist/engineer (with domain experience), and DL deployment/software engineer (with hardware and framework experience).

3. Choose the right tools and technologies

Today, the ecosystem is huge. But one important factor to think about while choosing your set of tools is production. What are you going to do in deployment? In general, there are three sets of tools that you need to set up for: framework (e.g., PyTorch, TensorFlow), training library (e.g., SuperGradients, Hugging Face), and peripherals (e.g., tools for monitoring, data labeling, and more). This is also the time to think about which is more cost-effective: use external tools and services or build your own?

4. Select your model

As you think about production from day-one, you have to consider various tactics to reduce time to market and R&D cycles such as using an off-the-shelf model, if applicable. Other things to consider include accuracy and speed metrics, and whether the model can be converted and deployed, or not. It is also crucial to note that when choosing a model, SOTA is not also ways the best option. Instead, look for the most suitable for your specific needs.

5. Build a clear plan for getting the data

Finally, AI projects are dependent on data so you need to make sure in advance what kind and how much data you need. Where is the data coming from and what are the legal and privacy implications? Can a public dataset help? If labeling is required, you also need to take into account how difficult, expensive, and time consuming it can be.

And there you have it. Ofri Masad , Head of AI at Deci, did a deep dive into each of these factors. Watch the webinar to learn more.

Get ahead with the latest deep learning content

1. PandaGPT: One Model to Instruction-Follow Them All. What makes it stand out from predecessors is its cross-modal capabilities, encompassing text, image/video, audio, depth, thermal, and inertial measurement units.
2. Meta 's new AI models can recognize and produce speech for more than 1,000 languages. It’s a tenfold increase on what’s currently available. 谷歌 's Magic Compose beta is here. The new Messages feature uses AI to help users write text messages, but it comes with privacy concerns.Text-to-video AI generators are advancing rapidly. But where are they headed next?
3. The 2023 AI Index Report is out. See how fast AI is advancing from R&D to technical performance and ethics, the economy and education, AI policy and governance, and more.
4. 亚马逊 Robotics open-sources ARMBench. It includes images, videos, and information on over 235,000 pick-and-place operations on 190,000 distinct items.Object detection on edge devices. Learn how to generate highly efficient models tailored to edge applications.
5. Generative AI in the world of work. Its true strength is to augment, rather than replace, the work of human experts.

Catch the Deci team at one of these upcoming events!

• ?? In-Person Meetup: Foundation Models in Production
• ?? Session on "A Good Design is All You Need: The Importance of Model Design in Achieving Better Than SOTA Accuracy" on Domino Data Lab 's Rev 4
• ??? MLSys, Booth 5
• ??? CVPR, Booth 1517
• ?? HPE Discover, AI Pavilion and HPE Innovation Area

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