All our articles
Insights, field experience and analysis from our Data & AI experts.
These articles are currently only available in French below.
FWD Conference returns in 2026: same community, new era
The go-to Data & AI conference returns on November 16, 2026, under a new name, to explore the agentic era.
leboncoin x Hymaïa: training Product & Engineering Managers on Data & AI challenges
How leboncoin trains its Product & Engineering Managers on Data & AI challenges.
The Forward Data Conference returns in 2025: bigger, more collective, more inspiring!
The Forward Data Conference returns in 2025 to connect the entire Data & AI community.
Forward Data Conference Paris 2024: joining the 1st edition of the international Data & AI conference
Why and how we created the Forward Data Conference, Paris's Data & AI conference.
Tracking data access in AWS
How to track and audit read access to data on AWS (S3, Athena) with CloudTrail, EventBridge, Lambda and Firehose.
From magic to mastery: demystifying AI to maximize its adoption
Overconfidence, disillusionment: how to move from magical thinking to real mastery of AI.
10 pitfalls limiting Data's impact on products and organizations
10 pitfalls that keep organizations from scaling Data. Do you recognize any of them?
10 lessons learned in 8 years of consulting
Diversity, autonomy, patience, convictions: the essential lessons from Data & AI consulting.
3 things to know before building your data team
3 key lessons before building your Data team, drawn from 5 years of field experience.
8 challenges for running LLM inference locally on mobile
Running LLMs on mobile: 8 challenges to overcome before getting there.
Discovering Azure OpenAI
Azure OpenAI decoded: the first cloud offering with turnkey GenAI services built in.
Apache Spark benchmark: preparing the TPC-DS test
Spark benchmark: Yarn vs Kubernetes. Preparing the TPC-DS test and choosing the dataset.
The Data Business Model Canvas
A canvas to frame your Data projects: the 3U rule (Useful, Usable, Used).
Data Literacy: 4 actions to start democratizing Data
Data Literacy: 4 concrete actions to democratize Data within your company.
Data Stories: Data Mesh at BlaBlaCar
How BlaBlaCar is rethinking its Data organization by drawing on Data Mesh principles.
PySpark & Pandas DataFrames: very similar in use, very different under the hood
PySpark vs Pandas DataFrames: very similar in use, but very different internal mechanics.
From idea to deployment: building a generative AI product with Bedrock
A field report on building a generative AI product with Amazon Bedrock.
Not having a diverse enough Data team
Does your Data team have every skill needed to build end-to-end Data products?
Believing Data culture stops at the Data team
Does everyone share the same definition of Data roles, responsibilities and stakes across your company?
Thinking Data is exempt from good Craft practices
Software Engineering best practices apply to your Data products too.
Seeing Data teams as "service vendors"
Does your Data team still have to convince the business to work with it?
GenAI at the heart of Product and Business: takeaways
Takeaways from the GenAI Hymaday: insights from Mirakl, Pernod Ricard, Malt, Nickel and Hymaia.
Data is not an end in itself
Data only has value if it serves a clear business objective. Let's stop treating it as an end in itself.
Data acculturation is not a one-way effort
Data acculturation should be seen as a two-way journey, not a one-way street.
Data Mesh: the importance of data governance
Data Mesh: why data governance is a core pillar of this approach.
The Triple Diamond of Data
Adapting the Double Diamond to Data & AI products with a third space dedicated to data.
Responsible AI in 8 major challenges
Interpretability, bias, governance, ethics: the 8 major challenges for Responsible AI.
Artificial Intelligence according to Luc Julia
An interview with Luc Julia, co-creator of Siri and CSO at Renault, on his vision of AI.
LLMs & Generative AI: the Path of Reason
Our book on LLMs and Generative AI: challenges, field experience and practical advice.
MLOps: applying DevOps principles to Machine Learning
MLOps decoded: applying DevOps principles to Machine Learning to industrialize your models.
Optimizing your Spark job
10 concrete ways to reduce your Spark job's execution time.
Configuring your data project without headaches, with Hydra
The headache of data configuration: see how Hydra can make your life easier!
Thinking "one-shot" for Machine Learning industrialization
Putting an ML model into production is only the start. How to plan for industrialization from day one.
Thinking of your Data Platform as a Product
Is your Data Platform suffering from low adoption? The key: treat it as a Product.
Over 80% of data projects never reach production, so what?
Over 80% of Data projects never reach production. Should we really be alarmed?
Data Product Manager, a fast-growing role
Data Product Manager: a fast-growing role at the crossroads of Product Management, Data and AI.
Poetry: configuring private GCP repositories
Configuring Poetry with a private Python repository on GCP Artifact Registry, step by step.
Poetry: finally, the tool to tame Python?
Poetry simplifies Python dependency management. An overview of its features.
Data Science products: 6 modeling approaches to quickly build your MVP
6 approaches to building a Data Science MVP without waiting for the perfect model.
Data Science products: don't wait for the perfect model before industrializing!
Move quickly to an end-to-end MVP instead of perfecting your ML model.
AI products: a survival kit for ambitious Product Managers
The survival kit for Product Managers stepping into AI products.
What is Data Mesh?
Data Mesh decoded: why and how to decentralize the management of your data.
What is Responsible AI?
The fundamentals of Responsible AI: definition, challenges and best practices for ethical AI products.
Serverless inference: when AWS SageMaker meets AWS Lambda
Combining AWS SageMaker and Lambda for real-time ML predictions, with no servers to manage.
Spark: when to cache a DataFrame?
When and how to use Spark caching on your DataFrames for real performance gains.
Falling into the infinite PoC trap
Why so many Data projects stay stuck at the PoC stage and never reach production.
An overview of Responsible AI
An overview of the pillars of Responsible AI and its 8 major challenges.
From data science to data analytics, a step backward?
Moving from data scientist to data analyst, a step back? Not at all. A field report.
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