The cloud deployment model identifies the specific type of cloud environment based on ownership, scale, and access, as well as the cloud’s nature and purpose. In This course, you would learn the flask app, Heroku, AWS, GCP. DataTrained provides you best in class content by leading faculty. Get an industry-recognized certificate of completion in Cloud Deployment of Machine Learning Model course from DataTrained.
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The cloud deployment model identifies the specific type of cloud environment based on ownership, scale, and access, as well as the cloud’s nature and purpose. The location of the servers you’re utilizing and who controls them are defined by a cloud deployment model. It specifies how your cloud infrastructure will look, what you can change, and whether you will be given services or will have to create everything yourself. Relationships between the infrastructure and your users are also defined by cloud deployment types.
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The course is good. It gives a detailed description of sqoop and flume. Every concept is very easy and simple to understand.
Instructor has a good command over the subject. Very nicely explained Excellent content, Every Hadoop developer much visit this course even if you are a experience one.
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Currently, he is driving several productivity programs - using data analytics to drive insights from business operations and implementing optimizations such as streamlining workflows, improving service levels, and ultimately reducing cost.
Shankar is a data Scientist with 14 Years of Experience. His current employment is with Accenture and has experience in telecom, healthcare, finance and banking products.
High-level programming language: With Python, the code looks very close to how humans think. For this purpose, it must abstract the details of the computer from you: memory management, pointers,… Hence, it is slower than “lower-level language” like C;
Python is interpreted and not compiled: Python code is interpreted at runtime instead of being compiled to native code at compile time;
Python is a dynamically typed language: Unlike “statically-typed” languages like C, C++ or Java, you don’t have to declare the variable type like String, boolean or int. The less you do, the more your computer has to work. For each attribute access, tons of lookup is required. In addition, being very dynamic makes it incredibly hard to optimize Python;
Global Interpreter Lock (GIL): This GIL basically prevents multi-threading by mandating the interpreter only execute a single thread within a single process (an instance of the Python interpreter) at a time.