Blent was created by Data Science industry pionners to fill the gap between theory and practice. They offer a training that makes people truly operational, able to understand and meet the concrete and current needs of companies.
Blent proposes the highest... Read More
Blent is also the most flexible. 100% online, it allows everyone to move forward according to their pace and constraints. For each course, the training is hybrid, combining coached E-learning, with high quality content accessible from a secure full SaaS platform. Plus, all our trainees are supervised by highly qualified mentors.
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Blent’s Data Engineer’s Bootcamp is a part time Data Engineer course over 3 months, perfectly adapted for workers, entrepreneurs and students. Combining virtual classrooms with our experts and works to be carried out on our e-learning platform, you’ll dive into the function of a Data Engineer and develop a clearer vision of the best practices in the most data-driven companies.
It all begins with a preparation bootcamp in order to learn the basics in Python and data wrangling. You will also get reminders on SQL queries and key Bash commands. Then, you will approach data storage, specifically the several existing solutions.
In addition to classic structured bases of SQL or persistent storage, you will discover the basics of column-oriented databases with Google BigQuery and learn to send SQL-like requests to perform huge query operations.
Once the storage is properly established, one of the first things that a Data Engineer must think about is the correct construction of an ETL (for Extract Transform Load) process in order to automate the processing of the data up to a Data Warehouse. Due to the large amount of data available, parallel computing tools, which include Hadoop and Spark among the most popular, will allow you to perform an impressive number of calculations in a very short time thanks to the clusters available in the Cloud.
Then, you will implement several solutions for Data Streaming, including publish-subscribe architectures with Apache Kafka. You will also learn about Confluent Platform, which brings powerful new features to Kafka.
Finally, this last step is the most important: it defines how the applications will be integrated into a Cloud infrastructure. You will see the power of application containerization with Docker, and how cloud solutions help distribute workloads. In addition, automation tools are essential for the Data Engineer: Blent will teach you to use Airflow, a reference in this field.
Resources : MongoDB, Cassandra, Apache, BigQuery, Redis, VoltDB, Hadoop, Spark, Kafka, RabbitMQ, Docker, MLFlow...
Blent’s Data Scientist Bootcamp is a part time Data Science course over 3 months, perfectly adapted for workers, entrepreneurs and students. Combining virtual classrooms with our experts and works to be carried out on our E-learning platform, you’ll dive into the function of a Data Scientist and develop a clearer vision of the best practices in the most data-driven companies : Data collection (SQL, scrapping) to deployment (Cloud, API, Amazon Web Services) going through modelling (Machine Learning, Deep Learning and interpretability).
First, you will get access to a preparation bootcamp in order to learn the basics in Maths and Python for Data Science. Once the preparation bootcamp is completed, you will learn to use statistics tools and advanced Data Visualization methods to bring knowledge on collected data. You will also review several cleaning and data preprocessing techniques required to implement algorithms during the modelling phase.
Then, you will implement a series of Machine Learning and Deep Learning models allowing to solve the business case, using advanced learning algorithms. You will focus on the interpretation part as it is essential to understand the operation of a model to use it efficiently.
As you may know, a Data Science project doesn’t stop on modelling. It needs to be accessible on the Cloud so that would make it operational and scalable. This is why the final phase will have you deploy by yourselves an online model on the Cloud, accessible from any computer or mobile app.
At the end of the training, a pitching session takes place to present, with your co-workers, the work carried out in front of a jury composed by our partner companies and people from Blent’s community. This session is followed by a huge Recruitment and Networking party !
Python, Jupyter, NumPy, Pandas, Matplotlib, VS Code, PostgreSQL, Bokeh, Scikit-Learn, SHAP, TensorFlow, Keras, Hadoop, Spark, Heroku
Blent’s Machine Learning Engineer bootcamp is about a highly sought job in Data Science. The training, as the two previous ones, is a part time online training and is supervised by highly qualified mentors. The in-depth knowledge of a Machine Learning Engineer enables him or her to handle large-scale projects. The ML Engineer knows how to deploy Machine Learning models, industrialize projects and manage highly scalable infrastructures.
Blent’s Machine Learning Engineer bootcamp is about a highly sought job in Data Science. It is a part time course over 3 months, perfectly adapted for workers, entrepreneurs and students. It iis supervised by highly qualified mentors. The in-depth knowledge of a Machine Learning Engineer enables him or her to handle large-scale projects. The ML Engineer knows how to deploy Machine Learning models, industrialize projects and manage highly scalable infrastructures.
As during this training, a multitude of different tools are needed, it begins with a preparation bootcamp. This latter is meant to pave the group to many advanced concepts of the Python language such as serialisation tools or environments. Acting on different systems, the future Machine Learning engineer needs to know the Bash and UNIX scripting’s basics, as well as project collaboration with Git. Finally, as Cloud Computing has its own vocabulary, this part is detailed in the preparation session too.
After the preparatory bootcamp, come other phases to be known: ML pipeline, model deployment and MLOps.
The ML pipeline consists of unifying the data collection and processing steps with modeling steps.
To do this, we will see how Bayesian optimization methods will be used in the future useful for automating modeling and determining the most efficient model. Since the data is often voluminous, Apache Spark is used for manipulating data with parallel computing and storing it in a NoSQL database with Google BigQuery.
Then the phase Model deployment is the core business of the ML Engineer: putting into production models on servers in the Cloud. For this phase many tools will be used, Kedro (for the architecture of the ML pipeline) and MLFlow (for models filing and tracking) and API REST / Docker.
The third phase is the MLOps one. It is a discipline of automating the deployment of Machine Learning through pipelines. Mixture of DevOps and Data Science, the MLOps is an indispensable skill for the efficient production of models. This phase of the training will start with self-managed serverless deployment using triggers of CI/CD pipelines then the deployment of the environment of production on Kubernetes. Then, in order to be updated regularly with more recent data, Airflow enables model training and deployments to be performed without intervention manual.
Finally, basic principles of safety and tools of monitoring are going to be taught, which are crucial points in the production environment.
Resources : Apache Spark, Google BigQuery, PySpark SQL, BigQuery, LightGBM, MLflow, Docker, Cloud Build, Stackdriver, Grafana
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