Stephen discusses his experience working with data platforms, particularly the challenges of training and sharing knowledge among different stakeholders. This talk highlights the importance of having clear priorities and a sense of practicality and mentions the use of modular job design and data classification to make it easier for end users to understand which data to use. Stephen also mentions the importance of being able to move quickly and not getting bogged down in the quest for perfection. We recommend Stephen's blog post "Airflow's Problem" for further reading.

The toolkit and infrastructure empowering machine learning practitioners are advancing as ML adoption accelerates. We'll go through the current landscape of ML tooling, startups, and new projects from an investor's perspective.

The First MLOps Community in Africa - MLOps Lagos Community Meetup features a discussion with experts about their experiences with MLOps at their companies. The MLOps Community was introduced with its goals and presents on scaling machine learning innovation with MLOps, XAI, and A/B testing. The meetup concludes with a panel featuring practitioners and students sharing their experiences with MLOps.

As an emerging sub-discipline, MLOps still needs to prove itself inside many enterprises where ML may present significant opportunities. Focused on the needs of the ML and Enterprise Architect this talk discusses some of the key areas which motivate the adoption of an MLOps approach to handling ML solution development and operations.

Machine learning in the Cloud can be easy and flexible. Join us for a 1.5 hours hands-on workshop around Google Vertex AI: 1. Train models with Vertex AI Training 2. Serve models with Vertex AI Endpoints 3. Putting it together by using Vertex AI Pipelines (including Vertex AI Model Registry and Vertex AI Experiments)

Foundation models are rightfully being compared to other game-changing industrial advances like steam engines or electric motors. They’re core to the transition of AI from a bespoke, less predictable science to an industrialized, democratized practice. Before they can achieve this impact, however, we need to bridge the cost, quality, and control gaps. Snorkel Flow Foundation Model Suite is the fastest way for AI/ML teams to put foundation models to use. For some projects, this means fine-tuning a foundation model for production dramatically faster by creating programmatically labeling training data. For others, the optimal solution will be using Snorkel Flow’s distill, combine, and correct approach to extract the most relevant knowledge from foundation models and encode that value into the right-sized models for your use case. AI/ML teams can determine which Foundation Model Suite capabilities to use (and in what combination) to optimize for cost, quality, and control using Snorkel Flow’s integrated workflow for programmatic labeling, model training, and rapid-guided iteration.

When it comes to Dattaraj's interest, you'll hear about his top 3 areas in Machine Learning. What he sees as up and coming, what he's investing his company's time into and where he invests his own time. Learn more about rule-based systems, deploying rule-based systems , and how to incorporate systems into more systems. there is no difference between ML systems and deploying models. It's just that this machine learning model is much smarter than traditional rule based models.

Setting Healthy Boundaries: Generating Geofences at Scale with Machine Learning Stephanie describes how project44 developed a new technique for generating geofences for customer facilities using historical data combined with machine learning. The audience learns how Stephanie and her team did it, including the pitfalls of deploying a product like this to production. She makes a special point to discuss the varied open source tools the team used including DBSCAN, Dask, geopandas, folium, Airflow, and more. Cars-Forge: Spot EC2s Made Easy Nikhil showcases cars-forge, a tool developed at Cars.com that was recently open sourced. Cars-forge is a command line tool that starts EC2 instances and runs scripts on them. Additionally, Nikhil also previews Skelebot, a command-line tool for developing ML projects and executing them in docker.