transformation to cloud native Rigid Manual Expensive Infrastructure / IaaS Public Cloud “Serverless”/ FaaS On-premise Middleware / PaaS ● ● Cloud Native Hybrid ● ● ● Up Agile Portable 低 低 自動化成熟度 高 VMs (GCE) Kubernetes (GKE) PaaS (GAE) Serverless (GCF) App Engine 以原始碼為基礎佈署 Kubernetes Engine 以容器為基礎佈 隨選生成的K8S叢集 Compute Engine 隨選生成的虛擬機 IaaS and PaaS at Scale Google App Engine

Moreover, like all vectorized "dot calls," these "dot operators" are fusing. For example, if you compute 2 .* A.^2 .+ sin.(A) (or equivalently @. 2A^2 + sin(A), using the @. macro) for an array A, it performs to give a convenient infix syntax A ⊗ B for Kronecker products (kron), then [A,B] .⊗ [C,D] will compute [A⊗C, B⊗D] with no additional coding.CHAPTER 5. MATHEMATICAL OPERATIONS AND ELEMENTARY FUNCTIONS "vectorized" fashion to arrays and other collections with the dot syntax f.(A), e.g. sin.(A) will compute the sine of each element of an array A. 5.7 Operator Precedence and Associativity Julia applies

Moreover, like all vectorized "dot calls," these "dot operators" are fusing. For example, if you compute 2 .* A.^2 .+ sin.(A) (or equivalently @. 2A^2 + sin(A), using the @. macro) for an array A, it performs to give a convenient infix syntax A ⊗ B for Kronecker products (kron), then [A,B] .⊗ [C,D] will compute [A⊗C, B⊗D] with no additional coding.CHAPTER 5. MATHEMATICAL OPERATIONS AND ELEMENTARY FUNCTIONS "vectorized" fashion to arrays and other collections with the dot syntax f.(A), e.g. sin.(A) will compute the sine of each element of an array A. 5.7 Operator Precedence and Associativity Julia applies

Moreover, like all vectorized "dot calls," these "dot operators" are fusing. For example, if you compute 2 .* A.^2 .+ sin.(A) (or equivalently @. 2A^2 + sin(A), using the @. macro) for an array A, it performs give a convenient infix syntax A ⊗ B for Kronecker products (kron), then [A, B] .⊗ [C, D] will compute [A⊗C, B⊗D] with no additional coding. Combining dot operators with numeric literals can be ambiguous "vectorized" fashion to arrays and other collections with the dot syntax f.(A), e.g. sin.(A) will compute the sine of each element of an array A. 6.7 Operator Precedence and Associativity Julia applies

Moreover, like all vectorized "dot calls," these "dot operators" are fusing. For example, if you compute 2 .* A.^2 .+ sin.(A) (or equivalently @. 2A^2 + sin(A), using the @. macro) for an array A, it performs give a convenient infix syntax A ⊗ B for Kronecker products (kron), then [A, B] .⊗ [C, D] will compute [A⊗C, B⊗D] with no additional coding. Combining dot operators with numeric literals can be ambiguous "vectorized" fashion to arrays and other collections with the dot syntax f.(A), e.g. sin.(A) will compute the sine of each element of an array A. 6.7 Operator Precedence and Associativity Julia applies

Moreover, like all vectorized "dot calls," these "dot operators" are fusing. For example, if you compute 2 .* A.^2 .+ sin.(A) (or equivalently @. 2A^2 + sin(A), using the @. macro) for an array A, it performs give a convenient infix syntax A ⊗ B for Kronecker products (kron), then [A, B] .⊗ [C, D] will compute [A⊗C, B⊗D] with no additional coding. Combining dot operators with numeric literals can be ambiguous "vectorized" fashion to arrays and other collections with the dot syntax f.(A), e.g. sin.(A) will compute the sine of each element of an array A. 6.7 Operator Precedence and Associativity Julia applies

Moreover, like all vectorized "dot calls," these "dot operators" are fusing. For example, if you compute 2 .* A.^2 .+ sin.(A) (or equivalently @. 2A^2 + sin(A), using the @. macro) for an array A, it performs give a convenient infix syntax A ⊗ B for Kronecker products (kron), then [A, B] .⊗ [C, D] will compute [A⊗C, B⊗D] with no additional coding. Combining dot operators with numeric literals can be ambiguous "vectorized" fashion to arrays and other collections with the dot syntax f.(A), e.g. sin.(A) will compute the sine of each element of an array A. 6.8 Operator Precedence and Associativity Julia applies

Moreover, like all vectorized "dot calls," these "dot operators" are fusing. For example, if you compute 2 .* A.^2 .+ sin.(A) (or equivalently @. 2A^2 + sin(A), using the @. macro) for an array A, it performs give a convenient infix syntax A ⊗ B for Kronecker products (kron), then [A, B] .⊗ [C, D] will compute [A⊗C, B⊗D] with no additional coding. Combining dot operators with numeric literals can be ambiguous "vectorized" fashion to arrays and other collections with the dot syntax f.(A), e.g. sin.(A) will compute the sine of each element of an array A. 6.7 Operator Precedence and Associativity Julia applies

Moreover, like all vectorized "dot calls," these "dot operators" are fusing. For example, if you compute 2 .* A.^2 .+ sin.(A) (or equivalently @. 2A^2 + sin(A), using the @. macro) for an array A, it performs give a convenient infix syntax A ⊗ B for Kronecker products (kron), then [A, B] .⊗ [C, D] will compute [A⊗C, B⊗D] with no additional coding. Combining dot operators with numeric literals can be ambiguous "vectorized" fashion to arrays and other collections with the dot syntax f.(A), e.g. sin.(A) will compute the sine of each element of an array A. 6.7 Operator Precedence and Associativity Julia applies

Moreover, like all vectorized "dot calls," these "dot operators" are fusing. For example, if you compute 2 .* A.^2 .+ sin.(A) (or equivalently @. 2A^2 + sin(A), using the @. macro) for an array A, it performs give a convenient infix syntax A ⊗ B for Kronecker products (kron), then [A, B] .⊗ [C, D] will compute [A⊗C, B⊗D] with no additional coding. Combining dot operators with numeric literals can be ambiguous "vectorized" fashion to arrays and other collections with the dot syntax f.(A), e.g. sin.(A) will compute the sine of each element of an array A. 6.7 Operator Precedence and Associativity Julia applies