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AWS Ͱ࢝ΊΑ͏ʂ͡Ίͯͷػցֶश IoT ALGYAN @ 2017.04.01
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@pottava (AWS Certified) SA, DevOps Engineer Pro ❤ Amazon ECS, AWS Batch, IAM
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גࣜձࣾεϐϯϑ
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ɹɹػցֶशͷ͓͞Β͍ • ػցֶशͱ • ػցֶशͱਂֶश ɹɹֶश • σʔλͷ४උ • ࢼߦࡨޡ / POC • ֶश ɹɹਪ ɹɹAWS ར༻ Tips ࠓ͓͢͠Δ͜ͱ 4
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ػցֶशͷ͓͞Β͍
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ػցֶश
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Կ͔ಛఆͷ͕͋Δͱͯ͠ɻ ίϯϐϡʔλʹ ٬؍తࣄ࣮ͷΈ Λ༩͑Δ͜ͱͰ ۩ମతͳճΛಘΔɺ·ͨͦΕΛվળ͢Δ͜ͱɻ ػցֶशͱ 7
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y = ax + b a, b Λਓ͕ؒࣄલʹܾΊΔͷ͕Ұൠతϓϩάϥϛϯάɻ ࣮σʔλ͔Βίϯϐϡʔλʹܭࢉͤ͞Δͷ͕ػցֶशɻ ػցֶशͱ 8
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y = ax + b ྫʣ x : ࠷ۙΓ߹ͬͨਓͷಛ y : ͜ͷͻͱͱকདྷ݁ࠗͨ͠ΒͤʹͳΕΔ͔ ػցֶशͱ 9
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y = ax + b ྫʣ ਓؒʮ y = 0.7 * ੑ֨x + 0.2 * ֎ݟx + 0.1 * ऩೖx ͰΑΖʯ ػցʮաڈͷσʔλ͔Β͍͑ ɹɹɹy = 0.4 * ੑ֨x + 0.1 * ֎ݟx + 0.5 * ऩೖx ͕దʯ ػցֶशͱ 10
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y = ax + b a, b ΛܾΊΔͨΊͷ࡞ۀΛֶशɺ ܾ·ͬͨ a, b ͷ͜ͱΛֶशࡁΈϞσϧͱݴ͏ɻ ֶशࡁΈϞσϧΛͬͯ ࣮ࡍʹ x Λೖ͠ y ΛಘΔͷ͕ਪɻ ֶशͱਪ 11
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y = ax + b Ϗδωε্ॏཁͳͷɺ༏Εͨਪ͕Ͱ͖Δ͔ɻ ༏ΕͨਪΛ͢ΔͨΊʹɺ༏ΕͨϞσϧ͕ඞཁɻ ༏Εͨ a, b ΛܾΊΔͨΊͷֶश͕ɺͷݟͤͲ͜Ζɻ ֶशͱਪ 12
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y = ax + b σʔλ͔Β a, b ΛٻΊΔํ๏ͨ͘͞Μ͋Δɻ ղ͖͍ͨʹΑͬͯɺదͳํ๏ΛબͿඞཁ͕͋Δɻ ֶशΞϧΰϦζϜ 13
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y = ax + b σʔλ͔Β a, b ΛؼೲతʹٻΊΔ۩ମతͳํ๏ͷ͜ͱɻ • A / B Ͳͬͪʁ → ϩδεςΟοΫճؼ • ച্Λ༧ଌ͍ͨ͠ → ઢܗճؼ • ސ٬Ληάϝϯτ͚͍ͨ͠ → k ฏۉ๏ • ϨίϝϯυΛग़͍ͨ͠ → ڠௐϑΟϧλϦϯά • … ֶशΞϧΰϦζϜ 14
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IoT ͰूΊͨσʔλ͔Β༏ΕͨϞσϧΛ࡞Γਪ͢Δɻ ͖ͬ͞ͷֶशΞϧΰϦζϜɺ͑ͦ͏Ͱ͢ΑͶʁ • ͏͙͢ނো͢Δʁ͠ͳ͍ʁ • ऩ֭ྔͲͷ͘Β͍ʹͳΔͩΖ͏ʁ • ࣅ௨ͬͨάϧʔϓʹ͚͍ͨ • Ճจͯ͘͠Εͦ͏ͳαΠυϝχϡʔԿͩΖ͏ʁ IoT × ػցֶश 15
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Ұํɺࠓͷ
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ਂֶश
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σΟʔϓϥʔχϯάɻ ଟߏͷωοτϫʔΫΛ༻͍ͨػցֶशͷ͜ͱɻ ྫʣ 4 ͷωοτϫʔΫྫ ਂֶशͱ 18
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੨ؙʹΛೖྗ͢Δͱɺؙʹ͕͑ग़ྗ͞ΕΔɻ ྫʣ͍҆ɺඒຯ͍͠ → ങ͏͖ 0.9ɺങΘͳ͍͖ 0.1 ਂֶशͱ 19
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2 Ҏ߱ͷ֤ϊʔυ˓͕ɺલͷ͔ΒͷೖྗΛجʹ y = ax + b ΛͬͯࣗࣗͷΛܭࢉɻ ਂֶशͱ 20
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ΑΓͬͱΒ͍͑͠Λฦͨ͢Ίʹ ֤ϊʔυͷ a, b ΛࣄલʹܾΊΔͷ͕ɺֶशɻ શϊʔυͷ a, b ͕ܾ·ΕɺͦΕֶ͕शࡁΈϞσϧɻ ਂֶशͱ 21
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ػցֶशશൠɺղܾ͍ͨ͠͝ͱʹબ͢Δͷ͕ ֶशΞϧΰϦζϜɻਂֶशྫ֎Ͱͳ͍ɾɾ ྫʣ • ͜ͷ͖Ύ͏Γ S? M? L? → ΈࠐΈχϡʔϥϧωοτϫʔΫ (CNN) • ࠓͷൃݴϙδςΟϒʁ → ࠶ؼܕχϡʔϥϧωοτϫʔΫ (RNN) • ਓ͕ඈͼग़͖ͯͨ͠ʂंΛݮʂʂ → ͋Ε͜ΕΈ߹Θͤ • … ਂֶशΞϧΰϦζϜ 22
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ඒ͍͠Έ߹ΘͤͷྫɺAlphaGoɻ ʢจ: http://www.nature.com/nature/journal/v529/n7587/abs/nature16961.html ʣ • 4 ͭͷσΟʔϓχϡʔϥϧωοτϫʔΫΛར༻ • ͦΕͧΕ ॳڃऀ pπ / தڃऀ pσ / ্ڃऀ pρ / அ vθ ͱ͍ͬͨҐஔ͚ • pπ ਓؒͷ 800 ສͷ൫໘σʔλΛݩʹֶशɻਫ਼͍͕ߴʹղΛಘΔɻ • pσ 13 ͷ CNNɻ3,000 ສ൫໘Λ 50 GPU Ͱ 3.4 ԯεςοϓɺ3 िֶؒशɻ ϓϩͷࢦ͠खΛ 57.0% ͷਫ਼Ͱ༧Ͱ͖Δɻ • pρ 50 GPU Ͱ 1 ͔͚128 ສճࣗݾରઓɻطଘιϑτʹ 85% ͷѹతউɻ • vθ pσ ͰϥϯμϜʹ 3,000 ສ൫໘Λੜ͠ɺpρ Ͱ 1 ԯ 6,000 ສճϩʔϧΞτͨ͠উΛ ڭࢣσʔλʹɺ50 GPU ͰҰिؒ 5,000 ສճ֬ޯ߱Լ๏Λ࣮ࢪɻ • ࣮ରઓͰ1,202 CPU + 176 GPU͕ΘΕɺpσ Ͱ࣍ͷखબɺvθ Ͱ൫໘ධՁɻ • উ͍͍͕ཧ٧Ίͷ pρ ΑΓɺਓؒͷบΛֶΜͩ pσ Λ͔ͬͨɻ • উҼ pρ ͷపఈతͳڧԽֶशʹՃ͑ɺϞϯςΧϧϩ୳ࡧͱ CNN ͷ߹͔ͤɻ ਂֶशΞϧΰϦζϜ 23
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࣮ફతͳωοτϫʔΫɺ ࣮ࡍʹͲΕ͘Β͍σΟʔϓͳϨΠϠʔͳͷʁ ྫʣ ΈࠐΈχϡʔϥϧωοτϫʔΫͷҰछɺResNet 152 ਂֶशΞϧΰϦζϜ https://research.googleblog.com/2016/08/improving-inception-and-image.html 24
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͓ɺ͓͏ɾɾ Ϟσϧͱܾͯ͠ΊΔมͲΕ͚ͩ͋Δͷɾɾ ֶशʹͲΕ͚͔͔ͩ࣌ؒΔͷɾɾ ͱ͍͏͔ɺϨΠϠʔఆٛ͢Δ͚ͩͰ৺͕ંΕͦ͏ɻ ࣗલͰ࣮ʁ·͋ແཧͰ͢ΑͶɾɾ ਂֶशΞϧΰϦζϜ 25
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ͦ͜Ͱ
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ਂֶशϑϨʔϜϫʔΫ
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ར༻ऀΞϧΰϦζϜͷ࣮Λ͢Δ͜ͱͳ͘ ֤छύϥϝλͷࢦఆ͚ͩͰֶशɾਪ͕Ͱ͖Δɻ ྫʣTensorFlow ʹΑΔΈࠐΈχϡʔϥϧωοτϫʔΫ (CNN) ఆٛ ɹ ͲΜͳॱংͰͲΜͳΛܦ༝͢Δ͔ɺײతʹΘ͔Δ ਂֶशϑϨʔϜϫʔΫ 28
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ΞϧΰϦζϜͷ࣮ͦͷಓͷϓϩʹ͓ͤͭͭ͠ɾɾ ࢲͨͪΓ͍ͨ͜ͱ͚ͩͰ͖Δ࣌ɺ౸དྷɻ ྫʣը૾ʹ͍ࣸͬͯΔਓ͕Γ͍ͨ → TensorFlow Ͱ CNN Λֶ͑शɾਪͰ͖Δʂ ≒ Golang Ͱ HTTP/2 Λ͑ηΩϡΞͳ௨৴؆୯ʂ ਂֶशϑϨʔϜϫʔΫ 29
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TensorFlowɺMXNetɺCaffeɺChainerɺTheanoɾɾ ͦΕͧΕͷಛΛؑΈͯͲΕΛ͏ͷ͔ɻ • ରԠΞϧΰϦζϜ • ಈ࡞ɾڥ • ܭࢉ / Ϧιʔεར༻ޮ • ར༻Մೳͳݴޠ / खଓతɾએݴత • εέʔϥϏϦςΟ / ෳ GPUɺฒྻαʔόରԠ • ใͷ๛͞ / ΤίγεςϜ / ༻αϙʔτ • … ਂֶशϑϨʔϜϫʔΫ 30
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ΫΠζͰ͢
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ʮ2 ͔݄લ͔ΒूΊͨσʔλ͔Β ඪମॏ·Ͱ͋ͱԿϲ݄ ͔͔Δ͔༧ଌ͍ͨ͠ʯ
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࣍ͷͲΕΛ͏ɾɾʁ
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ϩδεςΟοΫճؼ ઢܗճؼ ΈࠐΈχϡʔϥϧωοτϫʔΫ Ҩతϓϩάϥϛϯά
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࣮ફ͢ΔલͷɺେͳϙΠϯτ
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ʮ͋ͳͨͷۀʹػցֶशΛ׆༻͢Δ 5 ͭͷϙΠϯτʯ https://www.slideshare.net/shoheihido/5-38372284 גࣜձࣾ Preferred Infrastructure ൺށ কฏ͞Μ ͱ͍͍ͯεϥΠυͰͨ͠ɻ 36
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ͯ͞
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ֶशͷྲྀΕΛ࠶֬ೝ
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ػցֶशͷྲྀΕ 2. σʔλલॲཧ 3. ֶश 4. ਪ 1. σʔλऩू 39
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Ҏ߱ɺ͜ͷྲྀΕʹԊ͍ɺAWS ΛͲ͏͍͍͑ͷ͔ ར༻λΠϛϯάͱతผʹ͝հ͠·͢ɻ ػցֶशͷྲྀΕ 2. σʔλલॲཧ 3. ֶश 4. ਪ 1. σʔλऩू 40
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ֶश
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σʔλͷ४උ
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1ɹσʔλͷऩू ͪΖΜɺIoT ͔ΒಘΒΕΔηϯασʔλ༗༻ʂʂ ͱ͍͑ɺ·ͣػցֶशΛࢼͯ͠ΈΔ͚ͩͳΒ Ұൠެ։͞ΕͨσʔλΛ׆༻͢Δͷ͕؆୯Ͱ͢ɻ 43
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Ұൠެ։͞Εͨը૾ू ݚڀίϯςετΜͰɺͨ͘͞Μ͋Γ·͢ɻ • MNIST ɹ http://yann.lecun.com/exdb/mnist/ • CIFAR-10 & CIFAR-100 ɹ https://www.cs.toronto.edu/~kriz/cifar.html • ImageNet ɹ http://www.image-net.org/ • … 44
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AWS Public Datasets https://aws.amazon.com/jp/public-datasets/ 45
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Public Datasets | Google Cloud Platform https://cloud.google.com/public-datasets/ 46
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Public data sets for Azure analytics https://docs.microsoft.com/en-us/azure/sql-database/sql-database-public-data-sets 47
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Datasets « Deep Learning http://deeplearning.net/datasets/ 48
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http://www.data.go.jp/data/dataset 49 ͜Μͳͷ͋Δ
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AWS
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ؔ࿈αʔϏε܈
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σʔλऩूʹศརͳαʔϏε • AWS IoT • Amazon Kinesis Streams • Amazon CloudWatch Logs • Amazon S3 • Amazon DynamoDB • Amazon Cognito + AWS SDK • Amazon API Gateway 52
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2ɹσʔλͷલॲཧ Python ͚ͩͰࡁΉͳΒͦΕͰ͍͍ͷͷɺ ෳͷσʔλιʔε͔ΒϝλσʔλΛऔಘͨ͠Γ େنͳϑΝΠϧ͔ΒσʔλΛൈ͖ग़͢ͳΒ ઐ༻ͷιϑτΣΞαʔϏε͕ศརɻ 53
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AWS ͷؔ࿈αʔϏε܈
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σʔλલॲཧʹศརͳαʔϏε • Amazon Mechanical Turk • Amazon Athena • AWS Lambda / Step Functions • AWS CloudWatch Events • Amazon EMR / Batch / EC2 55
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Amazon Mechanical Turk 56 ΞϝϦΧͰͦͷར༻͕ ͱͯྲྀߦ͍ͬͯΔ ͱͷ͜ͱɾɾ
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ࢼߦࡨޡ / POC
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ࢼߦࡨޡʹศརͳͷͨͪ
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ओʹՊֶٕज़ܭࢉػցֶशͷۀքͰ ͋Ε͜Εࢼߦࡨޡͨ͠ΓɺͦΕΛ୭͔ͱڞ༗͢ΔͨΊͷ πʔϧɻଟ͘ͷݚڀऀΤϯδχΞʹѪ༻͞Ε͍ͯΔɻ git ͳͲͰόʔδϣϯཧ͢Δͷ༰қʂ Jupyter notebook 59
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ֶशʹͱ͕͔͔ͯ࣌ؒΔͷɻ ߦྻܭࢉ͕ಘҙͳ GPU Λ͕͑࣌ؒઅͰ͖·͢ʂ ࣗͷ PC ʹ͍ͬͯ͞Δ GPU ͕͑Δ͔ɾɾʁ (NVIDIA) GPU 60 ʢ͜Ε͕ͬͯ͞Δਓ͍ͳ͍ͱࢥ͏͚Ͳ..ʣ
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(NVIDIA) GPU 61 ݱࡏ AWS Ͱ GPU Λ͏ͱ͖ͷ Tips Λ·ͱΊ·ͨ͠ https://speakerdeck.com/pottava/tesorflow-v1-dot-0-on-ec2
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ࢼߦࡨޡ͢Δʹ͜ΕͱͯศརͰ͢ɻ ϥΠϒϥϦ͕ͲΜͲΜόʔδϣϯߋ৽ͯ͠େৎʂ Ϋϥυ্ʹֶशɾਪΛ࣋ͬͯߦ͘ͱ͖ʹ༗༻ʂ docker run -it --rm -p 8888:8888 jupyter/tensorflow-notebook Docker 62
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Docker 63 ݱࡏ AWS Ͱ Docker Λ͏ͱ͖ͷ·ͱΊ https://speakerdeck.com/pottava/containers-on-aws
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NVIDIA ͷ Docker Πϝʔδ 64 ҎԼͷΑ͏ͳܧঝؔͷΠϝʔδ͕ެ։͞Ε͍ͯ·͢ɻ ػցֶशͷಈ࡞ཁ݅ʹదͨ͠ΠϝʔδΛϕʔεʹɻ ಠࣗ Docker ΠϝʔδͷϏϧυͰ͖·͢ɻ cuda:7.x-runtime ubuntu:14.04 cuda:7.x-devel cuda:7.x-cudax-runtime cuda:7.x-cudax-devel caffe (v0.14) digits (v4.0)
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AWS Ϣʔβ Retty ͞Μ͕ GPUʢཧʣΛങͬͨɻ ͱͯڵຯਂ͍ɻ Ϋϥυඞཁͳͷʁ 65 http://qiita.com/taru0216/items/dda1f9f11397f811e98a
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࣮ࡍɺνʔϜͰݚڀɾ։ൃΛ͢ΔͱͳΔͱ ڥͷ / ෳ / ڞ༗ / ݖݶཧͳͲ՝ͨ͘͞Μɻ ޙड़ͷ EMR or ECS + IAM ͳͲͷΈ߹ΘͤΕ ࠷৽ͷ GPU ڥΛࣗಈ͢Δͱ͍ͬͨ͜ͱʂ AWS Ͱͷੳڥ 66
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AWS ͷؔ࿈αʔϏε܈
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ࢼߦࡨޡϑΣʔζʹศརͳαʔϏε • Amazon Machine Learning • Amazon EMR / EC2 ‣ p2 / g2 (GPU) instances ‣ Deep Learning AMI • Amazon EBS / S3 / ECR 68
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ೋ߲ྨɺෳΫϥεྨɺઢܗճؼͷϚωʔδυαʔ ϏεɻσʔλͷऔΓࠐΈ͔Βɺֶशɾਪ͕ߦ͑·͢ɻ αʔόͷཧ͕ෆཁͳͨΊɺεέʔϥϏϦςΟਪαʔ ϏεͷՄ༻ੑؾʹͤͣ OKʂ Amazon Machine Learning 69
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GPU Πϯελϯε 70 AWS ʹ 2 छྨ͋Γ·͢ʢݱߦੈʣ g2 ܥ: NVIDIA GRID K520 ɹɹɹɹ1,536 CUDA cores / GPU ͕ 2 ͭͰ 1 ͭͷ K520 ɹɹɹɹg2 Ͱ͑Δ GPU ຊདྷάϥϑΟοΫɾήʔϛϯά༻్ p2 ܥ: NVIDIA Tesla K80 ɹɹɹɹഒਫ਼ԋࢉ࠷େ 2.91 TFLOPSɺ୯ਫ਼ԋࢉ࠷େ 8.74 TFLOPS ɹɹɹɹ2,496 CUDA cores / GPU ͕ 2 ͭͰ 1 ͭͷ K80 ɹɹɹɹp2 ͷ GPU ൚༻ίϯϐϡʔςΟϯά༻్
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Amazon DeepLearning AMI 71 શ෦ೖΓ AMIɺ͋Γ·͢ʂʂ TensorFlow 1.0, MXNet, Caffe, CNTK, Theano, Torchɻ CUDA 7.5, cuDNN 5.0, Anaconda ɻ
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ֶश
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3ɹֶश ͬͱॏཁɺ͔ͭͷ͕͔͔࣌ؒ͘͢͝Δͱ͜Ζɻ ࣗࣾͷϞσϧΛ࡞Δͷͱͯେม͕ͩ ΦϦδφϧͷͷ͕Ͱ͖Εوॏͳࡒʹɻ Ϋϥυͷ༷ʑͳαʔϏε͕αϙʔτͯ͘͠Ε·͢ɻ 73
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AWS ͷؔ࿈αʔϏε܈
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ֶशϑΣʔζʹศརͳαʔϏε • Amazon EMR / Batch / EC2 ‣ p2 / g2 (GPU) instances ‣ Deep Learning AMI ‣ Spot Fleet / AutoScaling Group • Amazon Machine Learning • Amazon EFS / EBS / S3 / ECR • Amazon SQS 75
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AWS Batch 76 https://www.youtube.com/watch?v=UR8BI2Exkbc Պֶٕज़ܭࢉɾϋΠύϑΥʔϚϯείϯϐϡʔςΟϯά ༻్ͰਅՁΛൃش͢Δɺେنͳεέʔϧɺδϣϒͷґ ଘఆ͕ٛՄೳͳϚωʔδυฒྻࢄόονॲཧج൫ɻ
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͢Ͱʹ Black Belt ͷࢿྉ͕ެ։͞Ε͍ͯ·͢ɻ AWS Batch http://aws.typepad.com/sajp/2017/02/aws-black-belt-online-seminar-aws-batch.html 77
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ࢲϢʔβࢹͰݱঢ়Λ·ͱΊ·ͨ͠ɻ AWS Batch http://qiita.com/pottava/items/d9886b2e8835c5c0d30f 78
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ਪ
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4ɹਪ ֶशࡁΈͷϞσϧΛ͍ɺਪ͢Δɻ Ϗδωεͱ݁͢Δ͜ͱ͕ଟ͘ɺՔಇ 24 / 365ɻ Մ༻ੑͱϨΠςϯγ͕ॏཁͳͷҰൠαʔϏεಉ༷ɻ ͔ͯ͠͠αʔόϨεͰɾɾ͍͚Δɾɾɾʁ 80
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AWS ͷؔ࿈αʔϏε܈
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ਪϑΣʔζʹศརͳαʔϏε • Amazon ECS / EC2 ‣ p2 / g2 (GPU) instances ‣ Deep Learning AMI ‣ Spot Fleet / AutoScaling Group • AWS Lambda / Amazon API Gateway • AWS ElasticBeanstalk • Amazon EFS / EBS / S3 / ECR 82
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ϑϧϚωʔδυͳ Docker ίϯςφΫϥελڥɻ GPU ϕʔεͷਪΞϓϦέʔγϣϯͩͬͯಈ͖·͢ʂ Amazon ECS 83 https://speakerdeck.com/ayemos/build-image-classification-service-with-amazon-ecs-and-gpu-instances ΫοΫύουגࣜձࣾ છ୩ ༔Ұ͞Μ
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ͪ͜Β͢Ͱʹ Black Belt ͷࢿྉ͕ެ։͞Ε͍ͯ·͢ɻ Amazon ECS 84 http://aws.typepad.com/sajp/2017/02/aws-black-belt-online-seminar-aws-batch.html
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AWS Lambda 85 αʔόϨεͰ MXNet ʹΑΔਪΛ͢Δ࣮ྫ http://aws.typepad.com/sajp/2017/01/ seamlessly-scale-predictions-with-aws-lambda- and-mxnet.html
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AWS ར༻ Tips
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ػցֶशΛ AWS ͰΔͳΒɺͬͯಘ͢Δػೳ No. 1ʂ ԾαʔόΛ҆͑͘Δىಈํ๏ɻ AWS ͷσʔληϯλͷʮ༨ʯΛ ͜ͷֹۚͳΒ͍·͢ʂͱʮೖࡳʯͯ͠ىಈɻ Spot Fleet / Spot Πϯελϯε 87
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͜Ε·Ͱհ͖ͯͨ͠ AWS ͷ֤αʔϏεΛ Ͳ͏͍͍͔ͨΛ yaml / json Ͱએݴతʹهड़͓͖ͯ͠ Ұؾʹੜɾഁغ͢Δ͜ͱ͕Ͱ͖Δɻ ؆୯ਝʹɺ҆৺ͯ͠ڥ͕ߏஙͰ͖Δɻ ӡ༻ෛՙԼ͕Γ·͢ɻΠϯϑϥΛόʔδϣϯཧՄೳʹɻ CloudFormation 88
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Let’s try, anyway!
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ؼͬͨΒࣗͰҰ࿈ͷྲྀΕΛܦݧʂʂ࣮ફେࣄɻ ʮࣈը૾ఆ with TensorFlow on AWSʯ ࠓͷ॓ http://qiita.com/pottava/items/2fb2572f7099d432ebd9 90
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ػցֶशʹͲͷΫϥυΛ͏͔ʁͱߟ͑ΔΑΓ ৄ͍͠ਓΛั·͑ͯɺͬ͞ͱใΛूΊͯ ͍͍ͱ͜ͲΓͰ͏ͷ͕Α͍ͱࢥ͍·͢ɻ ͱ͍͏͔ɺࢼ͚ͩ͢ͳΒ ϩʔΧϧʹڥΛ͑ΕेͰ͢ɾɾ ·ͱΊ͡Όͳ͍ɺ·ͱΊ 91
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JAWS-UG AI ࢧ෦
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ίϯςϯπ • AWS Ͱ AI αʔϏεΛ࣮ɾӡ༻͢ΔͨΊͷ ɹҰൠతͳٕज़ใɺݟɺࣄྫڞ༗ͷ • ͢Ͱʹ׆༻͍ͯ͠Δํ • ಋೖΛݕ౼͍ͯ͠Δํ • ԿͦΕ͓͍͍͠ͷʁͳํʢ։࠵͝ͱʹқ͕ଟগҧ͍·͢ʣ
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͋Γ͕ͱ͏͍͟͝·ͨ͠ ࢀߟจݙ: • AWS Batch – ؆୯ʹ͑ͯޮతͳόονίϯϐϡʔςΟϯάػೳ – AWS https://aws.amazon.com/jp/batch/ • AWS Black Belt Online SeminarʮAWS Batchʯͷࢿྉ͓ΑͼQAެ։ http://aws.typepad.com/sajp/2017/02/aws-black-belt-online-seminar-aws- batch.html#QCPzBdn.twitter_tweet_count_m • re:Invent 2016: AWS Big Data & Machine Learning Sessionsɻ https://aws.amazon.com/blogs/big-data/reinvent-2016-aws-big-data- machine-learning-sessions/