ML Strategy Day

ML Strategy Day

Learning Machine Learning: Opportunities and Pitfalls

Recording available until September 19, 2023 

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ML Strategy Day 

Learning Machine Learning: Opportunities and Pitfalls 

Live on June 19, 2023 

9:00 a.m. – 12:30 p.m. CEST

Building Successful ML Products

Artificial Intelligence and Machine Learning are not only extremely trendy, they’re useful tools to get ahead of the game in the digital world. Join our ML Strategy Day and gain invaluable insights to confidently navigate the exciting yet complex world of Artificial Intelligence and Machine Learning.

The MLCon Strategy Day provides a unique opportunity to learn from experts what steps must be taken to build successful ML products. It provides an in-depth overview of ML pioneers’ and thought leaders’ approaches to developing amazing Machine Learning implementations: which know-how is needed, which methodologies are helpful, what technology choices must be made, and how to manage ML in production. Incorporating Machine Learning into a business strategy opens up fascinating new possibilities, but is anything but simple. We have seen far too many failed ML projects or prototypes that had no impact on the business. At the same time, if ML projects are approached correctly and with wisely chosen means, the opportunities for innovative digital products are limitless. The MLCon Strategy Day enables you to develop a viable roadmap for your ML project or review existing roadmaps in collaboration with our ML experts. It will provide you with the knowledge you need to be successful with your ML strategy!

ML Strategy Day

Program

You can do great things with machine learning and right now everyone wants to do it. But getting Machine Learning to work, let alone turning it into a sustainable business, is a real pain in the ass.As a Machine Learning Consultant and Engineering Director at Sinch, I train a lot of people to build their own Machine Learning algorithms and turn them into customer value. And while it is not that hard in and of itself, it is really easy to make mistakes, even for the best of us. Setting up a whole system, using the right tools and frameworks, and getting the best training data is a challenge, even for experienced machine learning experts.In this talk, I’ll explain what machine learning is and what it isn’t. I will highlight some of the most common mistakes and how to avoid them. But if you think you can stop making mistakes by doing everything right, think again!

AI is becoming increasingly central to business success, and a key driver in innovation for companies, but it bears unique challenges and complexities. In this session, we’ll explore strategies and practical tips for driving your AI team to be highly impactful from various angles. Positioning and effectively communicating the value of AI to the organisation, building trust and collaboration with other teams, choosing the right projects, and establishing a supportive culture for success.

The current situation at most companies could be summarized as below:
· Every team has their own unique way of testing and productionizing a model
· Lack of a centralized feature store
· Severe data quality issues
· Limited to no data or model monitoring in production (or test)
· Limited to no operational readiness
· Fragmented collaboration with partner teamsThis presentation takes the use case of a typical data science org that can apply software engineering principles to improve and solve all the above typical scenarios.

A vision that all data science teams could aspire for, involves the following:
· access to reliable data (with SLOs),
· automate data processing, model, training, evaluation and validation,
· productionize the model either for batch or online serving,
· continuously monitor data and model in production,
· use a trigger based mechanism to auto train, deliver and deploy in production

For achieving the vision, multiple goals need to be put in place. Some of them are below:
· Transform and standardize on how we do MLOps across all teams
· Leverage a centralized feature store and remove any training or serving skew
· All data produced must be treated as a product
· Enable comprehensive data and model monitoring capabilities
· Follow standard tiered approach model for implementing operations readiness
· Lastly, nurture relationships and collaborate with data engineering, central infra teams, etc

The rest of the presentation will go into details on how to implement each of the above goals along with a few high level architectural patterns.

ML Con
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Speakers

Pieter Buteneers

Pieter Buteneers

Sinch

Amit Bendor

Amit Bendor

Artlist

Vinay Narayana

Levi Strauss & Co.

Speaker

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