What Are the Top Machine Learning and Data Science Conferences In 2023?

Sarah Welsh


Looking for data science and MLOps conferences to attend in 2023? Look no further! We compiled a list of the must-attend conferences for practitioners, leaders, researchers, and professionals. This list not only includes a sampling of the top conferences with big-name speakers, but also conferences that are smaller, more manageable, and great for hands-on learning. Whether you want a deep dive into different parts of the ML lifecycle or to participate in interactive workshops on MLOps or learn about the latest research, these conferences offer something for everyone.

Note: this is by no means an exhaustive list and will be updated throughout the year.

World AI Conference

When: February 9–11

Where: Cannes, France + virtual

Cost: Free–1220€

Size: Large, 10,000+ attendees

Primary Attendees: Large technology companies, enterprise, startups

Good to Know: New conference (this is its second year)

The Basics: This international conference has five broad tracks: challenges and benefits of AI for society, future innovations, strategy, innovation, and applications. There are workshops, the opportunity for founders to pitch startups, a large exhibition hall and receptions. On the final day of the conference, the exhibition hall is open to the public. To give you an idea of the lineup, speakers this year will include AI leaders such as Chad Aronson, Global Head of Intelligent Automation COE at UBER, Lila Ibahim, Chief Operating Officer at DeepMind, and Luc Julia, Chief Scientific Officer at Renault.

Data Science Salon, Austin

When: Feb 21–22

Where: Austin, TX + virtual

Cost: $245 Virtual, $495 In Person

Size: Small, 500 in person, 2500 online

Primary attendees: Executives, senior data science practitioners, data science managers, analysts, engineering professionals

Good to know: Small event, good for networking

The Basics: Data Science Salon Austin is a two-day conference that focuses on AI and machine learning applications in enterprise. Data science panels, talks, and workshops help bring industry leaders and specialists face-to-face to educate each other on innovative new solutions in artificial intelligence, machine learning, predictive analytics and acceptance around best practices. There is also an expo and lots of time for networking in a relatively casual environment. Virtual attendees get lots of the benefits of in-person attendees including presentations, a networking app, coffee chats, and access to all sessions for two weeks after the event.


When: March 20–23

Where: Virtual

Cost: Free, Deep Learning Institute Training extra

Size: Very large (last year ~75K)

Primary attendees: Developers, engineers, researchers, IT professionals

The Basics: Long-running conference that starts with a keynote from Nvidia CEO Jensen Huang. Topics focus on artificial intelligence (AI), computer graphics, data science, machine learning and autonomous machines. followed by a variety of sessions and talks with experts from around the world. There are usually more than 500 sessions that focus on deep learning, robotics, data science, data centers, high performance computing, and related topics. Speakers and attendees come from all industries including finance, healthcare, transportation, manufacturing, retail and more.

Data Council

When: March 28–30

Where: Austin, TX

Cost: $599+

Size: Small, max 500 attendees

Primary Attendees: Engineers, Data Scientists

Good to know: Sessions can be pretty interactive; Discounts when you buy 4 or more tickets

The Basics: A manageable conference for anyone interested in data, especially with an open source mentality. The event covers the entire data journey: Data Infrastructure, Data Engineering, Data Science, Machine Learning, AI, and Data Analytics. Data Council prides itself on engaging talks, free workshops, no big sponsors, and a popular format called “Speaker Office Hours” that allows attendees to interact with each speaker after their talk.


When: March 30

Where: New York, NY

Cost: $249+

Size: Mid-size, ~1000 attendees

Primary Attendees: Machine learning & AI enthusiasts; Enterprise and Startups

Good to Know: Held at a rooftop bar in Midtown Manhattan; job board available for free

The Basics: MLconf is a single day conference targeted at practitioners and ML enthusiasts in industry. The day focuses on 25-30 minute presentations with an educational focus that discuss technical challenges, innovations, and motivation for the development of models, algorithms and statistical models. Topics include everything from AI/ML Ops to ML Ethics to Quantum Computing. There are speakers from Google Brain, 23AndMe, Red Hat, Nike, Startups and Universities. In other words, this one-day conference is jam-packed with opportunities for meeting people and learning something.

KubeCon + CloudNativeCon (Europe)

When: April 18-21

Where: Amsterdam, Netherlands

Cost: Free–$2000+

Size: Large, 3000+ in-person attendees, 19000+ virtual

Primary Attendees: Developers, architects and technical leaders, CIOs, CTOs, press and analysts

Good to Know: There’s an event in Europe and also in the US.

The Basics: KubeCon + CloudNativeCon brings together adopters, developers, and practitioners to collaborate and engage with leaders interested in Kubernetes, Prometheus, and other CNCF-hosted projects. Topics presented on typically include ML, Research, I/O Networking and Storage, Observability, Security, and many more. All platforms that presentations discuss must be open-source, and sales and marketing pitches are generally rejected.


When: April 25-26

Where: Virtual

Cost: Free

Size: Mid-sized, 1000+

Primary Attendees: ML engineers, industry practitioners, executives

Good to know: All talks are available afterward (find last year’s discussions here)

The Basics:  Arize:Observe is the industry’s largest summit dedicated to ML observability from both a business and technical perspective. The event features presentations and panels from thought leaders and ML teams across industries. Prior speakers include ML thought leaders from Chick-fil-A, Disney, Hugging Face, OpenAI, Spotify, Uber, and many more.

CDAO Spring

When: April 25-26

Where: West coast, USA (TBD)

Cost: TBD

Size: Small (200+)

Primary attendees: Data and analytics leaders

Good to know: Practitioners from end user companies can claim a free pass

The Basics: CDAO covers topics across data security, AI, and digital transformation. In 2023 , attendees will receive access to curated content to optimize cross-industry learnings & collaboration. There will be a chance to solve shared problems during round table discussions, have Q&As with speakers and schedule 1 on 1 meetings. Attendees can connect during interactive networking sessions. There are specialized tracks for leaders in data and analytics based on topics that are most important to your focus area.

World Data Summit

When: May 17–19

Where: Amsterdam, Netherlands

Cost: 795€ +

Size: Data not available

Primary Attendees: CDOs, CIOs, Analysts, Data Scientists

Good to Know: Emphasis on customer analytics

The Basics: This conference focuses on the big data ecosystem, with an emphasis on customer analytics. Over three days, the conference helps attendees gain a better understanding of developing an analytical model for customer growth. Experts will discuss all aspects of data analysis, how to work with unstructured data, how to upgrade data visualization and interpretability to the next level. There are workshops that will help practitioners and AI leaders to gain a deeper understanding of customer analytics.


When: May 20–21

Where: Zurich, Switzerland

Cost: TBD

Size: Mid-size

Primary Attendees: Academics and researchers

Good to Know:

The Basics: The focus of the 4th International Conference on Natural Language Processing and Machine Learning (NLPML 2023) is on sharing knowledge and results in theory, methodology and applications of Natural Language Computing and Machine Learning. Research presented is both practical and theoretical. Topics are geared toward a more technical and research-focused audience and emphasize areas of NLP like Chunking/Shallow Parsing, Semantic Processing, Lexical Semantics, Ontology, Text Mining, and much more. Presentations in Machine Learning focus on Applications, Learning in knowledge-intensive systems, Learning Methods and analysis, and Learning Problems

The Data Science Conference

When: October 27–28

Where: Chicago, IL

Cost: $1000+ (Speakers get in for free)

Size: Mid-size

Primary Attendees: Data science practitioners 

Good to know: No formal cut-off deadlines for speaker submissions but as a general guideline, booked 3-4 months in advance.

The basics: Always vendor free, sponsor free, and recruiter free. No pictures or recording are allowed and there is no virtual component. These policies allow for a unique conference experience that allows people attending to be present, to connect, and to gather intentionally about data science without the fluff. The Data Science Conference stands is pretty unique in that it’s industry-focused but also academic, and emphasizes practical applications without the advertising. People who go to this conference end up going again and again.

ODSC Europe

When: June, TBD

Where: Europe, TBD

Cost: TBD

Size: Mid-size, ~1500 (+more online)

Primary attendees: Data scientists, academics and researchers, enterprise

Good to know: Good for recruiting

The basics: ODSC Europe has conference focus areas in Machine Learning & Deep Learning, NLP, Data Engineering & MLOps, Machine Learning for Finance, a Kick Start Bootcamp, and Vector Search. Like the other ODSC events there’s a pre-conference bootcamp followed by two days of keynotes, talks, and workshops.

Big Data + Analytics Summit Canada

When: June 13–14

Where: Toronto, Canada

Cost: TBD

Size: Large

Primary attendees: C-Suite, enterprise, practitioners, strategists

Good to know: Meals are included in the ticket price; comprehensive COVID screening and protocols

The basics: This is a big event where leaders and practitioners can explore the latest trends across the big data landscape, including security, architecture and transformation, cloud migration, governance, storage, AI and ML and more. This year, there’s an emphasis on: hiring and retaining talent through a digital skills shortage, innovative technical and strategic content designed by experts for experts, and diversity in data & technology. All presentations are recorded and are available about a week after the conference ends.


When: June 19–22

Where: Munich, Germany + Virtual

Cost: 404€+

Size: Data not available

Primary Attendees: Leaders and practitioners at Startups, Enterprise

Good to know: Strong emphasis on business strategy

The Basics: MLCon is an international conference with focus areas in everything from ML business strategy to development to tools. The conference has an entire day devoted to ML Strategy where you can learn from experts on what it takes to build successful ML products.

If you’re a developer looking to build on existing ML skills, there’s an Advanced ML Development focus where experts will help you get to know the software architecture of machine learning systems. The conference organizers tout this as a unique opportunity to get into the peculiarities of ML systems and take a project to the next level.


When: June 18–22

Where: Vancouver, Canada

Cost: $490+

Size: Large, 10,000+

Primary Attendees: Half academic, half industry practitioners

Good to Know: Good for recruiting, good for research

The Basics: This conference covers research in computer vision and pattern recognition, and has a balanced mix of industry practitioners and researchers. There’s a high standard for papers presented here, where topics of interest include all aspects of computer vision and pattern recognition including machine learning, deep learning architectures, biometrics, computational imaging, and much more. If you’re working or doing research in this area, you’ll want to attend this conference at least once.

Deep Learning World

When: June 18–22, 2023

Where: Las Vegas, NV

Cost: 1495+

Size: Mid-size (~800)

Primary Attendees: Enterprise, ML practitioners

Good to Know: Virtual component is free

The Basics: Deep Learning World covers the commercial deployment of (you guessed it) Deep Learning. The event’s mission is to promote breakthroughs in the value-driven operationalization of established deep learning methods. There are hands-on workshops during the conference on topics such as Machine Learning with Python, Deep Learning in Practice, ML for Business Value, ML Techniques for Predictive Analytics. Speakers come from many different industries and fields including finance, retail, healthcare, government and business.

VentureBeat Transform

When: July 2023, TBD

Where: San Francisco, CA + Virtual

Cost: Free–$999

Size: Large

Primary Attendees: C-suite

Good to Know: Virtual component is free

The Basics: VentureBeat Transform is an AI and data conference that brings together business and technology leaders to discuss the latest advances in AI, big data, analytics, and more. There is coverage across applied AI and focuses in industries like healthcare, finance, retail, manufacturing, and security. This event is a great opportunity to get in front of leaders in lots of different industries, network, attend keynote speeches, and learn something.


When: July 23–July 29

Where: Honolulu, HI

Cost: $50–$1295

Size: Large

Primary Attendees: Academics/researchers and industry practitioners

Good to Know: Discounts for students

The Basics: The International Conference on Machine Learning (ICML) is one of the longest-running ML conferences–the first one was held in 1980. It’s  an internally-renowned academic conference for presenting and publishing ML research. Research at the conference spans all areas including artificial intelligence, statistics and data science, as well as application areas such as machine vision, computational biology, speech recognition, and robotics.


When: August 7–9

Where: Las Vegas, NV

Cost: Free–$2195

Size: Mid-size, 1700+ attendees

Primary Attendees: Executives, Industry Practitioners

Good to Know: In-person attendees get access to a networking app to help set up 1:1 meetings

The Basics: Ai4 is a mid-size AI conference held every year, bringing together business leaders and data practitioners to facilitate the responsible adoption of artificial intelligence and machine learning technology. This event’s purpose is to provide a common framework for what AI means to both enterprise and the future of the planet. There are over 26 industry-specific tracks, with presentations that are both technical and non-technical. Speakers confirmed for 2023 so far include leaders from Stability AI, the U.S. Department of Energy, J.P. Morgan Chase, Twitter, and Pfizer, to give you an idea of the industries this conference spans.


When: August 20–24

Where: Dublin, Ireland

Cost: TBD

Size: Large

Primary attendees: Academics and practitioners working in spoken language processing

Good to know: Theme for 2023 is on Inclusive Spoken Language Science & Technology

The Basics: INTERSPEECH gathers researchers and practitioners who work on the science and technology of spoken language processing. INTERSPEECH conferences emphasize interdisciplinary approaches to speech science and technology, from theory to application.

INTERSPEECH 2023 will feature talks and poster sessions, plenary talks by experts, tutorials, special sessions and challenges, show & tell, exhibits, and satellite events. Accepted papers embrace a broad range of science and technology in speech, language and communication, including but not limited to: Speech synthesis, analysis of conversation, speech coding and enhancement, speech and hearing disorders, phonetics and phonology, automatic speech recognition, and more.

Southern Data Science Conference

When: September, TBD

Where: Atlanta, GA

Cost: $300-$550

Size: Small, 500+

Primary Attendees: Industry, academia, government

Good to Know: Single-track allows you to catch all the talks and workshops

The Basics: A small but mighty two-day event focused on machine learning in production. There are speakers from major companies and startups, and no sessions overlap. Last year, workshops provided comprehensive introductions to deep learning, computer vision, AI-powered search, and more. Breakfast and lunch is included with the ticket price, which is good news if you like to eat.


When: October 16–20

Where: San Diego, CA

Cost: Free (Virtual)–$700+

Size: Large

Primary Attendees: Popular conference for analytics engineers and data professionals

Good to Know: The virtual component is really well-done

The Basics: This event is hosted every year by dbt Labs, and shows the wide range of applications for analytics engineering. It’s a highly interactive conference where analytics engineers can focus on learning and sharing skills and tools that produce higher impact data work. This can be a great team-building event that can give you lots of new strategies, applications, lessons, and tools for current and future projects. Every year, dbt Labs uses the conference as a platform to announce new features and initiatives.

World Summit AI

When: October 11–12

Where: Amsterdam, Netherlands

Cost: 199 €+

Size: Large

Primary Attendees: Enterprise, FAANG, research, government, investors (“the full AI ecosystem”)

Good to know: Virtual (on-demand) attendance is lower and they have academic and startup discounts; there’s an (Ai-powered) networking tool

The Basics: World Summit AI hosts a large AI ecosystem of enterprise, startups, investors, and researchers to discuss pressing AI issues and set the global AI agenda. Past sessions have spanned AI ethics to applied AI to hands-on workshops. There is lots of opportunity for networking, many high-profile plenary sessions, lots of curated panels, and even smaller board-room style meetings. 1-on-1 meetings can be organized through an AI-powered matchmaking tool.

Big Data & AI Toronto

When: October 18–19

Where: Toronto, Canada

Cost: TBD

Size: Large, 5000+ (in 2022: 4200 in person, 1300 online)

Primary Attendees: practitioners, entrepreneurs, C-suite and decision makers from Fortune 500 companies.

Good to know: Big Data & AI Toronto covers Big Data, AI, Cloud and Cybersecurity

The Basics: The conference has been running since 2016, providing a platform for IT decision-makers and practitioners to explore and discuss insights, showcase the latest innovative projects, and connect with the best and brightest minds in the industry. The conference happens over two days in both in-person and virtual formats. There’s an exhibition, panels and talks, bootcamps to give attendees technical skills, and networking opportunities.


When: October, TBD

Where: Virtual

Cost: Free

Size: Large

Primary attendees: ML and MLOps practitioners

Good to know: Content is available after on demand

The basics: TWIMLcon is put on every year by the team behind the TWIML AI Podcast. It began after a series of interviews that explored the state of ML technology and platforms in 2018. The popular series formed the basis for this virtual event that focuses on providing a forum for people in MLOps: those working on productionalizing, operationalizing, and scaling ML and AI in real scenarios. Though the conference is virtual there are lots of opportunities to connect and network.


When: May 9-11 (Boston)

Where: Boston (ODSC East)+ San Francisco (ODSC West)

Cost: TBD

Size: Large

Primary Attendees: Data scientists, academics and researchers, enterprise

Good to Know: Good for recruiting; ODSC has multiple events

The Basics: ODSC East has big-name speakers, great workshops, hands-on training sessions, and tons of networking opportunities. AThere’s a big focus on ML, NLP, data analytics, responsible AI, and more. If you’re looking to build skills or grow in your career, you can apply for the mini-bootcamp to get ML training prior to the event. The bootcamp covers programming, deep learning, data visualization, tools and more. There are hands-on workshops and training sessions, recruiting sessions, and tons of opportunities for networking.

Ray Summit

When: September, TBD

Where: San Francisco

Cost: TBD

Size: Large

Primary Attendees: Engineers and ML practitioners, developers, data scientists, and students and academics

Good to know: In-person with a virtual component

The basics: Ray Summit is an event for software engineers, machine learning practitioners, data scientists, developers, MLOps professionals, and architects — and anyone else who wants to learn about building and deploying large-scale applications, especially in AI and machine learning. The event is all about scalable machine learning and scalable Python and highlights new developments in AI scaling and open source. Speakers from major companies like Amazon, Google, IBM, Lyft, Meta, and OpenAI discuss outcomes with Ray.


When: October 2–6

Where: San Francisco, CA

Cost: TBD

Size: Large

Primary Attendees: Software developers, technical team leads, senior engineers

Good to know: Good for networking

The basics: Qcon focuses on emerging software trends and innovations, and is a place where senior software engineers, tech leads, and architects come together to learn, share, and push each other to drive innovation in the software industry. It’s a great place for industry leaders to share what they’ve learned, techniques they’ve discovered, discover new innovations, and get inspired.

MLOps World

When: October, TBD
Where: TBD

Size: Large

Primary Attendees: MLOps practitioners and leaders, and anyone working in ML/AI

Good to know:  Half virtual events and workshops, half in person

The basics: MLOps World is a conference designed to help companies put more models into production environments as effectively, responsibly, and efficiently as possible. The event usually happens over four days with two days online and two days in person. There are hands-on workshops, a start-up expo and career fair, sponsored parties, talks, and panels. Workshops last year included sessions on treating your data platform link a product, overcoming data infrastructure limitations, building real-time ML features, and creating governance systems that ensure fairness.

AI Dev World

When: October, TBD

Where: TBD

Cost: TBD

Size: Large (2000+)

Primary Attendees: Software engineers and data scientists

Good to know: Most of the talks are technical

The basics: AI Dev World is an event for research and breakthroughs in AI developer technologies. Tracks include: chatbots, machine learning, open source AI libraries, AI for the enterprise, and deep AI. This conference targets engineers and data scientists looking for an introduction to AI, and AI developers looking for a landscape view on the newest AI technologies. In both cases the subject matter can lean towards more technical. There is also usually a hackathon and a startup expo

KubeCon + CloudNativeCon (North America)

When: November 6–9

Where: Chicago, IL

Cost: $99–$1750

Size: Large

Primary Attendees: Developers, architects and technical leaders, CIOs, CTOs, press and analysts

Good to know: Nice student discount

The basics: This is the Cloud Native Computing Foundation’s flagship conference, and it brings together adopters and technologists from open source and cloud native communities. Topics presented on typically include ML, Research, I/O Networking and Storage, Observability, Security, and more.


When: November TBD

Where: TBD, California

Cost: TBD

Size: Large (5000+ including hybrid)

Primary Attendees:  Data scientists, academics and researchers, enterprise

Good to know: Good for recruiting; ODSC has multiple events

The basics: ODSC’s Western conference also has (like ODSC East and ODSC Europe):  big-name speakers, great workshops, hands-on training sessions, and tons of networking opportunities. ML, NLP, data analytics, responsible AI,MLOPs, Ml safety and security and research frontiers are some of the focuses. There is a bootcamp for ML training prior to the event that covers programming, deep learning, data visualization, tools and more. Also features hands-on workshops and training sessions, recruiting sessions, and tons of opportunities for networking.

Re:Work MLOps Summit

When: November, TBD

Where: London + San Francisco

Cost: TBD

Size: Mid-size

Primary Attendees: ML practitioners

Good to Know: You can find past talks here

The Basics: The summit’s focus is creating efficient ML workflows and decreasing time to production. Participants from industry and academia will discuss how to optimize the ML lifecycle and streamline pipelines for better production. Among the covered topics are automation, architectural practices, data efficiency, ML pipelines and workflows, and deployment.

Toronto Machine Learning Summit

When: November, TBD

Where: Toronto, Canada

Cost: Free–$700

Size: Mid-sized, 1000+

Primary Attendees: 50% ML practitioners, and a mix of researchers and business leaders

Good to Know: Good networking via Whova last year; Ticket includes food, drinks, and parties

The Basics: TMLS takes an explorative approach to address the needs of ML researchers, professionals, and entrepreneurs. There’s a big emphasis on community, inclusion, and support, and there’s room for collaboration across academia and industry. As such, there’s a large mix of technical backgrounds. Conference events include workshops, case studies, opportunities to hear about cutting-edge research, and open discussion groups.


When: November 2023, TBD

Where: Location TBD

Cost: $25–$175

Size: Large

Primary attendees: Researchers, Academics

Good to know: Organizers include top researchers from major universities, Google Research, DeepMind, etc.

The Basics: NeurIPS has become one of the premiere ML conferences. While Machine Learning and neuroscience are the primary fields represented, other focus areas include cognitive science, psychology, computer vision and linguistics. Last year there were affinity events that included sessions for: Global South in AI; Women in ML; North Africans in ML; Indigenous in AI, and more. There are keynotes, poster sessions, competitions, a journal showcase, an expo, and tons of opportunities to hear about and learn from the latest research and top minds in ML, AI, and neural information processing.

The AI Summit New York

When: December 7–8, 2023

Where: New York, NY

Size: Mid-size, less than 5000

Primary Attendees: Practitioners, Policy Makers and Business Leaders

Good to Know: Emphasis on applied AI

The Basics: The AI summit convenes experts, practitioners, policy makers and business leaders in the field. The purpose of the AI Summit New York brings together technical leads from a vast range of industries. Last year, the event hosted the Women in AI Flagship Summit, at the AI Summit New York, presented a line-up of women leaders and passionate allies. Discuss AI through the lens of ethics, culture, and development, whilst still considering the ROI of AI.

VOICE Summit

When: December 4–6

Where: Arlington, VA

Size: Mid-size 1000+

Primary Attendees: Anyone working on conversational AI

Good to know: Good for lead generation

The Basics: The goal of this conference is to bring together leaders and practitioners around the world working on, developing, or researching conversational AI. As such, attendees and speakers span industries; last year there were speakers from Target, Amazon, Google Assistant, General Motors, Salesforce, Best Buy, and more. The conference (and the industry) focuses on Contact Center Modernization, CX and CX Automation, and Custom Assistant Development and Implementation.


When: December, TBD

Where: Virtual

Cost: Free

Size: Unknown, Virtual

Primary Attendees: ML practitioners

Good to Know: Very practitioner-focused and can get pretty technical

The Basics: Apply(Conf) is an event for machine learning and data teams to discuss the practical data engineering challenges they face when building operational ML systems. It’s a virtual, practitioner-focused event for data and ML teams to discuss the practical data engineering challenges faced when building ML for the real world. Participants share best practice development patterns, tools of choice and emerging architectures they use to successfully build and manage production ML applications. Apply(recsys) 2022 focused on the specific challenges of building recommender systems, with talks on best practice development patterns, tools of choice, and emerging architectures to successfully build and manage production RecSys applications. We can’t wait to see what the theme is for 2023.