What We Offer
At Hi! Paris, we help you manage your projects from start to finish, from creation to exploitation of the idea. The team is made up of machine learning engineers. We support you on your machine learning project by working in close collaboration with you.
Getting ready for AI.
Whether you’re new to AI or ready to take it to the next level, you need to focus on data, manage its lifecycle, and account for it to accelerate intelligent, automated results. We can help you at every step of your machine learning project.
Our Services
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Problem understanding
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How can we collaborate ?
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We will clearly define the objectives and methods of the needs analysis and detail the techniques to guide you in the needs gathering phase of your project:
- Benchmark of the competition
- Proof Of Concept
Tools: talking to experts, experience
How can we collaborate ?
During the project management, we organize a kick-off to review the ins and outs of the project, and thus the expected elements of the solution. The more precise the needs analysis, the easier the functional and technical design stages will be. Indeed, the purpose of the needs analysis is to define the scope and functional amplitude of the project. Creative energy is directed in one direction to meet the challenges of the project.
Get the data
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During this phase, we use a set of techniques from various scientific disciplines such as statistics or artificial intelligence, to build models from the data, i.e. to find interesting structures or patterns according to predefined criteria, and to extract a maximum of knowledge from them.
- to identify data quality problems
- to detect interesting subsets to formulate in hypothesis forms.
How can we collaborate ?
Develop a tailored plan to implement proofs of concept and tools for research and data interoperability and metadata standards.
- Decide on the data to be used for analysis (goals, quality, volume or data types)
- Acquire the data (web scraping, database, …)
- ID and correctly handle the missing values
Libraries python : Pandas, BeautifulSoup, Flake8, Pylint ,…
Explore the data
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The data preparation phase deals with the tasks to be defined on the final data set from the initial raw data. These are the table, the recording and selecting attributes, cleaning data, constructing new attributes and transforming data for modeling.
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- Define business process integration strategy and create deployment plan
- Determine machine learning approach and design application
- Define data integration strategy and create execution plan
Librairies python: Matplotlib, Numpy, Scipy, Pandas, …
Development - Training model
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The expertise of our teams allows us to accompany you in the industrialization phase of your own algorithms. Our teams can assist you in :
- Validation of your algorithms on real cases
- Quality control of your algorithms’ computer code
- Integration of your algorithms with design optimization tools
- Assistance in the deployment and use of your algorithms
- Corrective and evolutionary maintenance of these algorithms.
How can we collaborate ?
- Contribute process knowledge and expertise to feature engineering and model development
- Select algorithm and develop machine learning model
- Develop, test, and maintain data platform
Librairies python: scikits learn, scikit network, …
Tests and evaluation
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It is important to further evaluate the model and review the steps performed to build the model to ensure that it is properly meeting the project objectives.
This phase is used to evaluate the model to ensure that it is meeting the project’s objectives.
At the end of this phase, a decision on the use of the data mining results should be made.
How can we collaborate ?
- Automated model validation: After training, the model performance is examined before production by evaluating performance metrics and comparing the new model with old versions of the model.
Deployment
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Creating the model is not the end of the project. In general, the knowledge gained will need to be organized and presented in a way that the client can use it.
Deployment can be as simple as generating a report or as complex as implementing a repeatable data mining process.
It is necessary at the beginning of the project to know what actions will need to be taken in order to be able to use the models created.
- Continuous delivery (CD) of the model: The trained and validated model is delivered as a prediction service and deployed automatically.
How can we collaborate ?
- Manage machine learning model deployment and process integration
- Deploy machine learning model and model refresh cycle
- Set up production data flows and automate model
Tools: TensorFlow, ML flows, Docker, …
Development of document
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Once the project has been executed, it is time to put it into operation. Then, once everything is verified and validated, it’s time to close the project with the elaboration of all the related documentation.
How can we collaborate ?
Complete software documentation is provided at your request, whether it is a specification document for developers/testers or software manuals for end users.
Continous improvment
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The stable model obtained in the previous phase is then delivered to production. Testing, monitoring, release management, automation, continuous deployment, monitoring and governance are applied to the machine learning model in this phase.
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Track and report progress against goals
- Mine model for insights and learnings and experiment with changes and improvements
- Govern and manage data quality