Community of practitioners working to promote and encourage the adoption of successful ML projects.
Organizers of TMLS in June & @MLOpsWorld Nov, Austin TX.
If you only keep room for a few high-signal AI events each year, keep TMLS on your radar.
10 years practitioner-led, built by people who ship + operate ML/AI (and now agentic) systems, not just talk.
TMLS 2026: Jun 16–19 (Virtual Jun 16 • Toronto Jun 17–18)
Semantic similarity is not the same thing as answer retrieval.
Lean on embeddings as the default for every use case and you get systems that sound convincing while returning weak, incomplete, or confidently incorrect answers.
David vonThenen, @NetApp .
Every software company claims to be becoming an AI company. Most are re-running the wrong playbook: treating AI like an infrastructure migration instead of a shift in how products are designed, shipped, and operated.
Alet Blanken, @Workday . TMLS 2026.
The default assumption in AI security is that encryption costs performance. Fully Homomorphic Encryption, especially.
The talk analyzes trade-offs between encryption overhead and latency, using open-source FHE and model optimizations.
Tyson Macaulay, @01quantuminc
A robot that can recall what it did last week needs memory across three dimensions: spatial, descriptive, and visual.
That is the architecture behind experience recall for temporal question answering in agentic robots.
@stevewaslander , @UofT
Agent vulnerability is primarily architectural, not a model alignment problem.
Fixing the model without addressing orchestration logic leaves the most exploitable attack surface untouched.
Naga Sujitha Vummaneni, @Ripple
TMLS 2026 → https://t.co/ZeMnODpBeq
$26/month infrastructure. 200+ languages served. A core team of three.
That's the Multilingual Climate Chatbot, a production RAG system, open-source and easy to adopt.
Luis Ticas + Helena Yu, Sprout Climate
Real data can be a legitimate option for pre-training tabular foundation models - despite being underutilized in favour of synthetic data.
It captures complex signals critical for downstream generalization.
Anthony Caterini @Layer6AI is exploring that here at TMLS 2026
Evals in production: the #1 constraint our committee flagged this year.
Korede Adegboye is presenting his framework at TMLS 2026: automated dataset curation, failure-mode detection, and uncertainty-aware decisioning.
→ See All Speakers: https://t.co/F0oeCcK9cZ
Vino Sangaralingam is automating and productionizing an NLP-based process with a GenAI component in regulated finance and payments, where ROI and measurement need to be built into the pipeline from day one.
She’s on the MLOps World Steering Committee.
Ten years of Canadian AI practitioners in one room, and once again, that room is at CIBC.
Proud to have them back as Platinum Sponsor and host venue for our 10th annual summit.
TMLS 2026 · June 16–19 · Toronto, CA
→ https://t.co/6uCXCS55td
One person with the right AI tools can go from idea to working product in days.
TMLS and FGF Brands are putting that to the test at Toronto Tech Week. 5-day agentic hackathon, any stack, demo to judges on Friday.
May 25–29, Toronto, CA
Vinothini Sangaralingam is productionizing NLP with a GenAI component in regulated finance and payments, where governance and auditability aren't optional.
She's on the TMLS 2026 Steering Committee.
TMLS 2026 · June 16–19 · Toronto, CA
→ Learn more: https://t.co/ia0qouq55O
Dippu Singh is building AI systems that combine high-precision models with geospatial data to detect security anomalies across live feeds, focused on architectures that are scalable, trustworthy, and safe for enterprise decision-making.
Dippu Singh is building real-time AI systems that detect security anomalies across live geospatial data feeds, where the architecture has to be trustworthy enough to act on automatically.
He's on the TMLS 2026 Steering Committee.
TMLS 2026 · June 16–19 · Toronto, CA