Transformers for Enterprise Use Cases
Learn how to apply transformers, state-of-the-art deep learning models, to your business with the Hugging Face team that has pioneered their open-source access.
Access to: session recordings, curated resources and exclusive events
Designed to help make organizations
Data Driven
Product Leaders
Program managers and product managers that are focused on metrics like growth and revenue, and prioritization decisions
Data Scientists
Data Scientists and Data Science managers who help map strategic decisions to actionable experimental designs and then interpret the results in a trustworthy manner
Engineering Leaders
Engineering managers, directors, VPs, and CTOs who want to make their organizations data-driven with metrics and A/B tests
Designed for ML Engineers & Researchers
who want to utilize Transformers to build reliable and scalable services
- Develop an advanced understanding how how to use Transformers through the entire machine learning lifecycle
- Apply core transformer libraries (Transformers, Datasets, Accelerate, Optimum)
- Champion, deploy, and scale Transformer models in your organization, finding opportunities to use them to solve business problems
- Have 1+ years of experience working with machine learning models
- Familiarity with the basics of Transformers (free Hugging Face online course covers the basics)
- Looking to use machine learning to solve problems related to NLP, audio, images, and video within an enterprise
Live Cohort
Transformer-based models have taken the Machine Learning world by storm! Immediately revolutionizing Natural Language Processing, Transformers continue to make an impact for both cutting-edge research and industry applications. The successes of Transformer models like BERT and GPT have kickstarted a rapidly growing ecosystem of models and techniques, and data-driven organizations are wary of being left behind. Hugging Face exploits the properties of Transformer models to make it easy for individuals to “finetune” their own models, lowering the barrier for research and allowing traditional software engineers to include machine learning functionalities into their stacks. This course equips ML practitioners with the knowledge, skills, and tools that they’ll need to use state-of-the-art models to solve their problems.
- What key features have made Transformer models so successful in NLP?
- How can I use Transformer models to solve problems in my domain?
- What technical considerations do I need to address in order to develop and deploy my own Transformer models?
- What does the future hold for Transformers?
- How can I keep up and integrate with the latest Transformer models?
In traditional software development, CI/CD automates many tasks, including testing, building and deploying software. But CI/CD for ML is a different beast. Testing and deployment of ML can be triggered by many event types, and observability and logging requirements are materially different for ML.Today, no single tool can facilitate end-to-end CI/CD for ML. The process of testing, building and deploying ML requires a symphony of tools and glue code to create an integrated CI/CD system. To offer an entry point that many data scientists and engineers are familiar with, we’ll teach you how to integrate GitHub with other ML tools to build custom CI/CD automations for ML that will increase your engineering efficiency and prevent errors from being released to production.
In session one, we will cover the importance and use of pretrained models within the Transformer ecosystem. At the end, learners will be able to:
- Search and select the best pretrained transformer models using the Hugging Face hub
- Use the pipeline abstraction to quickly build applications for the typical tasks in ML (NLP, audio, image, and video)
- Make recommendations for the right licenses for software, models, and datasets for your enterprise
In session two, we’ll talk about using and fine-tuning Transformer models. By the end, learners will be able to:
- Use automated and manual fine-tuning using tools such as AutoTrain and Transformers libraryÂ
- Use of tokenizers and datasets packages
- Apply advanced best practices for fine tuning, including problematic issues like leakage, catastrophic forgetting, and memorization
In session three, the instructor will dive deeper into the transformer architecture and how transformers operate. By the end, learners will be able to:
- Understand the Transformers architecture to troubleshoot issuesÂ
- Build a Q&A search engine, leveraging an understanding of attention and contextualized representations
- Argue for the importance of considering the ethical implications of large language models
During the fourth session, we’ll discuss developing and deploying Transformer models in enterprise settings. Afterwords, learners will be able to:
- Describe the typical issues and considerations that arise when deploying Transformers within an enterprise
- Differentiate between the various ML deployment options, explaining their benefits and drawbacks
- Use the optimization methods, including ONNX and quantization based on the Optimum package
- Describe privacy and security issues with Transformers
In this final session, the instructor will cover how to build your own pretrained model. After reviewing when it makes sense, the major focus will be going over the tools, tips, and techniques for building your own transformer model. By the end, learners will be able to:
- Develop the business case for building a pretrained model
- Use tools for building a pretrained model, including Accelerate, Megatron, DeepSpeed, and T5X
- Avoid common pitfalls when building a pretrained model
- Make appropriate hardware choices for pretraining
- Discuss state-of-the art models such as GPT 3 or BLOOM
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Team?
Sphere offers a range of subscription packages that provide discounts on all courses in our library. We help upskill employees at some of the world’s best companies. Learn more about pricing options here or book a time to talk to one of our staff below.
Book a free consultation
Learn live from a world-class
Instructor
Learn live from a world-class
Instructor
Rajiv Shah is a Machine Learning Engineer who has managed hundreds of data scientists at DataRobot, Snorkel AI, Caterpillar, and State Farm.Â
Eno Reyes is a ex-Microsoft software engineer who has founded and sold two companies. At Hugging Face he now focusses on open-source generative image models, prompt engineering, and using language models for reasoning.
Derek Thomas is a Machine Learning Engineer with experience advising teams in Financial Services, Manufacturing, Tech, Utilities, and Retail.
Nicholas Broad is a Kaggle Master and Machine Learning Engineer. He has experience advising teams in Healthcare, Retail, and Tech.
Learn live from world-class
Instructors
Rajiv Shah is a Machine Learning Engineer who has managed hundreds of data scientists at DataRobot, Snorkel AI, Caterpillar, and State Farm.Â
Eno Reyes is a ex-Microsoft software engineer who has founded and sold two companies. At Hugging Face he now focusses on open-source generative image models, prompt engineering, and using language models for reasoning.
Derek Thomas is a Machine Learning Engineer with experience advising teams in Financial Services, Manufacturing, Tech, Utilities, and Retail.
Nicholas Broad is a Kaggle Master and Machine Learning Engineer. He has experience advising teams in Healthcare, Retail, and Tech.
Guest Lectures by
Industry Experts
Join a diverse and experienced
Community
This cohort gives you access to a rich community of like-minded professionals from some of the best businesses in the world. Even after the course ends, you will continue to learn and build with each other.
Exclusive Content
to advance your business
Get access to exclusive content through live sessions, meetups and our Student Portal (even after you finish the cohort). Ask questions and get personal feedback directly from your instructors and others taking the course.
Still have questions?
We’re here to help!
Do I have to attend all of the sessions live in real-time?
You don’t! We record every live session in the cohort and make each recording and the session slides available on our portal for you to access anytime.
Will I receive a certificate upon completion?
Each learner receives a certificate of completion, which is sent to you upon completion of the cohort (along with access to our Alumni portal!). Additionally, Sphere is listed as a school on LinkedIn so you can display your certificate in the Education section of your profile.
Is there homework?
Throughout the cohort, there may be take-home questions that pertain to subsequent sessions. These are optional, but allow you to engage more with the instructor and other cohort members!
Can I get the course fee reimbursed by my company?
While we cannot guarantee that your company will cover the cost of the cohort, we are accredited by the Continuing Professional Development (CPD) Standards Office, meaning many of our learners are able to expense the course via their company or team’s L&D budget. We even provide an email template you can use to request approval.
I have more questions, how can I get in touch?
Please reach out to us via our Contact Form with any questions. We’re here to help!
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