BigML is a consumable, programmable, and scalable Machine Learning platform that makes it easy to solve and automate Classification, Regression, Time Series Forecasting, Cluster Analysis, Anomaly Detection, Association Discovery, and Topic Modeling tasks. BigML is helping thousands of analysts, software developers, and scientists around the world to solve Machine Learning tasks end-to-end, seamlessly transforming data into actionable models that are used as remote services or, locally, embedded into applications to make predictions.
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Master Machine Learning with BigML online or in-situ workshops.
Become a BigML Certified Analyst, Engineer, or Architect.
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For companies with stringent data security, privacy, or regulatory requirements, BigML offers Private Deployments that can run on your preferred cloud provider, ISP, or own infrastructure with commodity servers to meet enterprise grade requirements such as traceability and repeatability for all your workflows. More info.
BigML wants to help you get the most benefit from your data, as such, we offer three different types of certification programs that fit the needs of every professional profile. Please visit the BigML Certifications page to see the differences among the courses and choose the one that works best for you!
A 2-month BigML Standard subscription is included in each certification course.
In order to be eligible to enroll in the BigML Certified Engineer course you must be familiar with general Machine Learning concepts, the BigML Dashboard and its resources. Also, some programming skills are mandatory in this certification, as you will be asked to understand and generate code in Python and the languages available in the platform: Flatline and WhizzML. You can use our documentation and tutorials as a head start: ML 101, Tutorials, API documentation, and WhizzML.
Once you have successfully passed the BigML Certified Engineer course, you are eligible to enroll in the BigML Certified Architect course. By becoming a Certified Architect in BigML you will be able to structure an end-to-end Machine Learning project with the global vision needed to connect all the pieces of the Machine Learning application together for the project to work smoothly. The course has been designed to be partly hands-on. Your evaluation questions will include simplified versions of real end-to-end Machine Learning applications in different production scenarios (online, batch, event-driven) using different programming languages and tools (Javascript, Python, GitHub, Docker, etc.). Mastering these tools is not the goal or a prerequisite of the course, and commented examples are provided so that you can learn about the coding particulars that need to be used in your answers. Nevertheless, being familiar with them is recommended.
Democratizing Machine Learning and promoting a good-quality education is a key mission for BigML. In order to lower potential barriers and let everyone have access to our certifications, we are pleased to offer discounts to those from qualifying nations. Please contact us if you live in a country that does not belong to the G20 group or similar (based on GDP) and we will be happy to offer you a discount.